The Functional Refactoring Pass

This is an anecdote, not a data point, yet.

I'm a firm believer in functional programming and I consider myself a `mostly functional` programmer. I use functional programming when I can, but when a side effect is required, I'll use it. I'm not a purist.

Functional programming is supposed to have a number of advantages over imperative, procedural programming. The primary advantage is that there are provably no race conditions. You can trivially scale functional code to multiple threads and multiple processors. In theory, functional code is easier to reason about and easier to test because each function can be reasoned about and tested in isolation. In practice, functional code can become a maze of thunks and lambdas that many find difficult to read. Nonetheless, I prefer to reason about tangled lambdas than to reason about tangled state.

I believe that functional code is easier for an LLM to reason about as well. I haven't proven this, but anecdotally it seems to be the case. In theory, the LLM would gain all the usual benefits of functional programming. It would find the code easier to reason about and easier to test.

Unfortunately, the vast majority of code that the LLM has been trained on is imperative, procedural code. The LLM can write functional code when specifically prompted, but it will default to writing imperative, procedural code.

If you start a vibe coding project ab initio, you'll get a lot of imperative, procedural code, and the LLM will have more and more difficulty reasoning about it as the project grows. To stop this from happening, I prompt the LLM to refactor the code to be more functional. I do this fairly early on in the project, once the project starts to show signs of life.

The functional refactoring is a multi-step process and the best way to do it is to prompt the LLM to first create a plan for the refactoring. I use a prompt something like this:

Make a plan. We need the code to be refactored to be more functional and to adhere to functional programming principles. Take several steps to refactor the code so that the main interaction path is functional and stateless. Move the side effects to the edges of the codebase. Use functional programming techniques such as monads and reactive programming to keep the core of the codebase functional. Make sure that utility functions are pure and stateless. Write the plan to a file FUNCTIONAL_REFACTORING.md

The LLM will cogitate for a while and will write a multi-step plan for the refactoring. Here is the plan that the LLM generated for the jrm-code-project.com web site.

# Functional Refactor Plan for `jrm-code-project`

**Author's lens:** Senior Functional Programming Architect
**Scope:** `package.lisp`, `csrf.lisp`, `server.lisp`, `auth.lisp`, `billing.lisp`,
`admin.lisp`, `chef.lisp`, `db-auth.lisp`, `stripe.lisp`, `jwt.lisp`, `totp.lisp`, `ses.lisp`
**Status:** Complete. Phases 1-8 below have all landed as separate,
individually-tested commits; the codebase now reflects this plan. The
phase write-ups are retained as historical design-rationale documentation
-- comments elsewhere in the codebase that cite "FUNCTIONAL_REFACTOR.md
Phase N" are pointing at finished work, not an in-progress migration.

---

## 0. Framing

This codebase is a working, well-organized Hunchentoot application (the recent
file split into `csrf`/`server`/`auth`/`billing`/`admin`/`chef` was a good move
along the *separation-of-concerns* axis). But every one of those modules is
written in a straight-line, **imperative-shell-with-no-functional-core** style:
HTTP handling, session mutation, SQL, third-party HTTP calls, HTML rendering,
and business rules are all fused into single `DEFUN`s that read the world,
mutate the world, and print strings, in one undifferentiated breath.

The project already imports `SERIES`, `FOLD`, `FUNCTION` (compose/inverse), and
`NAMED-LET` — real functional-programming firepower — via shadowing imports in
`package.lisp`. Almost none of it is actually used in the handler code; the
shadowed `LET`/`DEFUN`/`LET*`/`MULTIPLE-VALUE-BIND` forms are used as drop-in
replacements for their vanilla CL counterparts, not as a foundation for a
different *style* of programming. That's the central irony this plan
addresses: the tools for a functional architecture are already a dependency of
the system; they're just not driving any design decisions yet.

The plan below does **not** propose rewriting Hunchentoot, Postmodern, or
Stripe's HTTP API into something pure — those are unavoidably effectful
boundaries. It proposes pushing effects to the *edges* (a thin imperative
shell) and pulling everything else — validation, view-model construction,
tier/authorization logic, Stripe payload shaping, HTML rendering — into a
**pure, immutable, composable core** that can be unit-tested without a
database, without Hunchentoot, and without live Stripe credentials.

---

## 1. Anti-Pattern Catalog (current state)

### 1.1 Global mutable state used as an implicit parameter-passing channel

- `*acceptor*` (`server.lisp`) — mutated by `start-server`/`stop-server`.
- `*stripe-tier-price-ids*`, `*stripe-tier-product-ids*`, `*stripe-price-id-tiers*`,
  `*stripe-billing-portal-configuration-id*` (`stripe.lisp`) — four separate
  `DEFVAR`s, populated by side-effecting `PUSH` inside `ensure-tier-product`
  and `ensure-billing-portal-configuration`, and read by unrelated functions
  (`tier-price-id`, `tier-from-price-id`, `create-billing-portal-session`)
  scattered throughout the file. This is really *one* piece of "Stripe
  catalog" data, represented as four uncoordinated globals that must be
  mutated in lock-step (see `init-stripe-product`, which zeroes all four by
  hand before repopulating them) — a classic sign that a single immutable
  value is trying to escape.
- Every handler reaches into `hunchentoot:session-value`/`hunchentoot:cookie-in`
  as ambient dynamic state rather than being handed an explicit `Request`
  value. E.g. `dashboard-page` (`auth.lisp`) pulls `:authenticated-user` from
  the session, `challenge-2fa-page` reads/writes `:limbo-email` and
  `:post-login-redirect` via `setf` in the middle of a rendering branch.

### 1.2 God-functions that fuse I/O, business logic, and presentation

Nearly every `hunchentoot:define-easy-handler` in `auth.lisp`, `billing.lisp`,
and `admin.lisp` does all of the following in one function body:

1. Read ambient state (session, cookies, POST params).
2. Validate/branch on it.
3. Call the database or an external HTTP API (side effect #1).
4. Mutate session/cookie state (side effect #2).
5. Build and return an HTML string via nested `FORMAT` calls (presentation).

`dashboard-page` (`auth.lisp`) is the extreme case: ~250 lines mixing tier
math, JWT issuance (a side effect), a conditional redirect, and a giant
`FORMAT` template with 20+ interpolation arguments computed inline. There is
no way to unit-test "what should the dashboard tier grid look like for a
LAMBDA-tier user with a Stripe customer ID" without spinning up Hunchentoot,
a session, and a database row.

`stripe-webhook-handler` (`billing.lisp`) mixes signature verification,
JSON parsing, event-type dispatch, and five different DB-mutation call sites
in one `COND`, with logging `FORMAT` calls interleaved — untestable without a
live (or heavily mocked) Postgres connection and a hand-built JSON fixture.

### 1.3 Stringly-typed, un-composable HTML rendering

Every page is a hand-written `FORMAT nil "<html>...~A...</html>"` template.
Consequences:

- No composition: the "vault" card, the "tier grid", and the notification
  banner in `dashboard-page` cannot be reused or tested independently — they
  are inline slices of one giant format string.
- No enforced escaping discipline: some interpolations go through
  `hunchentoot:escape-for-html` (e.g. `(hunchentoot:escape-for-html user)`),
  others don't (e.g. tier-derived CSS class strings, which happen to be safe
  today only because they come from a fixed internal vocabulary) — the
  safety property is not structurally guaranteed, only true by convention and
  developer discipline.
- Every handler re-embeds the same `<style>` block or repeats layout
  boilerplate (`signup-page` and `setup-2fa-page` both hand-roll near-identical
  `<html><head><style>...` wrappers).

### 1.4 Alist-of-keywords as a poor man's record type

`db-auth.lisp`'s `get-user`/`list-users`/`get-user-by-customer` all return
`postmodern:query ... :alists` rows, and every caller repeats
`(cdr (assoc :membership-tier user-data))`, `(cdr (assoc :wheel user-data))`,
etc. — by grep, this exact shape appears **20+ times** across `auth.lisp`,
`billing.lisp`, and `admin.lisp`. There is no `USER` type: the "schema" is an
implicit contract enforced only by every call site independently getting the
keyword spelling right (`:stripe-subscription-id` vs. a typo would fail
silently, returning `NIL`, not a compile- or run-time error).

### 1.5 Side-effecting, non-monadic error/control flow

- `csrf.lisp`'s `WITH-CSRF-PROTECTION` macro is a control-flow combinator
  wearing a syntactic disguise: it's really "if failure, mutate the HTTP
  return code and short-circuit" — imperative branching hidden inside a
  `DEFMACRO`, not a composable value.
- `jwt.lisp`'s `require-membership-tier`/`require-wheel`/`require-membership-jwt`
  each *either* return a value *or* perform a side-effecting `REDIRECT` and
  return `NIL` — callers are contractually obligated to check for `NIL` and
  "immediately stop processing" (a convention documented in a comment,
  not enforced by the type/control-flow system). This is exactly the shape
  `Either`/`Result`/`Maybe` monadic short-circuiting exists to replace.
  Compare with e.g. `require-session-wheel` in `admin.lisp`, which duplicates
  the same "return value or redirect-and-return-nil" shape independently for
  session-based (not JWT-based) authorization — the same *pattern* implemented
  twice, un-abstracted.
- `stripe-webhook-handler` and `roast-code-with-gemini`/`chef-handler` use
  `HANDLER-CASE` around large blocks and communicate failure by mutating
  `hunchentoot:return-code*` and returning an ad hoc string — errors are
  effectively `(values nil side-effect)`, not typed outcomes.

### 1.6 Duplicated imperative HTTP-client boilerplate

`stripe.lisp` rebuilds `(stripe-auth-headers secret-key)` and re-checks
`(and secret-key (not (string= secret-key "")))` in nearly every function
(`find-existing-tier-product`, `create-tier-product`,
`ensure-billing-portal-configuration`, `create-stripe-checkout-session`,
`create-billing-portal-session`, `get-stripe-subscription-tier`,
`cancel-stripe-subscription-with-prorated-refund`) — eight independent,
hand-written guard clauses for what is structurally one precondition
("do we have Stripe configured") and one authenticated-GET/POST helper.
Request payloads are built as raw `(cons "key[bracket][path]" "value")` lists
by hand at each call site (see the billing-portal-configuration content-list
construction) rather than through a small combinator/DSL that could be unit
tested for correct shape independent of the network call.

### 1.7 Unused functional idioms already in scope

`package.lisp` imports `SERIES` (lazy, compiler-fused sequence pipelines) and
`FOLD`, yet the codebase's list processing — `list-users` pagination,
`mapcar #'render-member-row members`, `dolist` loops in `db-auth.lisp` and
`stripe.lisp`, the `LOOP ... COLLECT` in `generate-recovery-codes` — is all
plain `CL:LOOP`/`DOLIST`/`MAPCAR` with `SETF`-based accumulation
(`random-string`'s `(setf (char res i) ...)` loop, `admin-members-page`'s
imperative pagination math). None of it is wrong CL, but it means the
project's own stated architectural direction (series/fold-based composition)
isn't actually load-bearing anywhere yet.

### 1.8 Testing is coupled to live, mutable external state

`recovery-code-verification`, `stripe-database-and-routes`, and
`user-membership-tiers` (per `tests/tests.lisp` and this repo's own
documented conventions) require a live Postgres instance and mutate real
rows. This is a direct consequence of §1.2/§1.4: because business logic is
never separated from the DB/HTTP shell, there is no way to test "does
`tier-meets-minimum-p` correctly rank CADR above CONS" or "does the webhook
handler correctly map a `customer.subscription.deleted` event to a
cancellation" without a database in the loop.

---

## 2. Target Architecture

**Functional core, imperative shell**, applied consistently:

```
┌─────────────────────────────────────────────────────────────┐
│ Imperative shell (thin, at the edges only)                   │
│  - Hunchentoot handlers: parse Request, call pure core,      │
│    interpret its pure Response/Effect value, perform I/O.    │
│  - Postmodern calls: translate SQL rows <-> immutable domain │
│    records at the boundary only.                             │
│  - Stripe/Gemini HTTP calls: translate typed request records │
│    <-> typed response records at the boundary only.          │
│  - *ACCEPTOR*, *STRIPE-CATALOG*, cookie/session get/set.      │
└───────────────────────────┬───────────────────────────────────┘
                            │ immutable values only cross this line
┌───────────────────────────▼───────────────────────────────────┐
│ Pure functional core (the bulk of new/moved code)             │
│  - Domain records: USER, MEMBERSHIP-CLAIMS, STRIPE-CATALOG,   │
│    CHECKOUT-REQUEST, WEBHOOK-EVENT, VIEW-MODEL, RESULT.       │
│  - Pure decision functions: tier-meets-minimum-p,              │
│    dashboard-view-model, webhook-event->db-commands,          │
│    checkout-request->stripe-params, csrf-check, auth-check.   │
│  - Pure rendering functions: view-model -> HTML string.       │
│  - Composable middleware combinators over a Request->Result   │
│    handler shape.                                              │
└─────────────────────────────────────────────────────────────────┘
```

Key design commitments:

1. **Immutable domain records, not alists-of-keywords.** Every "row" that
   crosses the DB boundary becomes a `defstruct` (or `defclass` with
   `:read-only` when the CLOS overhead-per-instance is not a concern) with
   named, typed accessors — `user-membership-tier`, `user-wheel-p`, etc. —
   constructed once at the DB boundary via a single `row->user` converter,
   never re-derived by ad hoc `(cdr (assoc :x row))` at call sites.

2. **Explicit `Result`/`Either`-style outcomes instead of "return NIL and
   trust the caller to have already redirected."** A tiny `defstruct result`
   (or reuse of `(values status payload)`, or a proper condition-based
   approach — see Phase 6) makes success/failure a first-class value that
   the *shell* interprets (issue a redirect, render an error page), rather
   than a side effect the *core* performs mid-computation.

3. **Middleware as composable functions, not macros with inline control
   flow.** `WITH-CSRF-PROTECTION`, `require-membership-tier`,
   `require-session-wheel` all collapse into one combinator shape:
   `(defun wrap-with-csrf (handler) ...)`, `(defun wrap-with-tier (min-tier handler) ...)`,
   composed via `FUNCTION:COMPOSE` (already a dependency!) at route-definition
   time, e.g. `(compose (require-tier "CADR") require-login csrf-protected) #'chef-page-core)`.

4. **Pure view-model construction, separated from HTML string rendering,
   separated from the HTTP handler.** `dashboard-page` becomes: (a) a pure
   `dashboard-view-model` function (user record + query params -> an
   immutable `DASHBOARD-VIEW-MODEL` struct), (b) a pure `render-dashboard`
   function (view-model -> HTML string, independently unit-testable with
   hand-built view-models and no session/DB at all), and (c) a thin handler
   that wires the two together and performs the one real side effect
   (issuing the JWT cookie).

5. **One immutable `Stripe` catalog value, not four mutable globals.**
   `ensure-tier-product`/`ensure-billing-portal-configuration` become pure
   functions that *return* an updated `STRIPE-CATALOG` record; `init-stripe-product`
   becomes the one place that takes the pure result and stores it in a single
   `*stripe-catalog*` global (still a necessary impurity — Stripe's actual
   product IDs are genuinely mutable external state fetched once at startup —
   but now it's *one* clearly-labeled impurity instead of four unsynchronized
   ones).

6. **Lean on `SERIES`/`FOLD` where they fit naturally** (pagination,
   filtering, tier-ranking, recovery-code generation) so the project's own
   declared functional dependencies start pulling their weight, without
   forcing awkward `SERIES` usage onto genuinely imperative I/O loops (the
   SMTP hand-rolled protocol in `ses.lisp`, for instance, is legitimately
   sequential/stateful and is *not* a refactor target for series-ification).

---

## 3. Non-Goals

- **Not** rewriting Hunchentoot request handling, Postmodern's connection
  model, or the raw SMTP-over-TLS code in `ses.lisp` — these are genuine
  imperative shells (sockets, connections, OS processes) and should stay
  imperative, just kept as thin and as clearly bounded as possible.
- **Not** introducing a heavyweight external templating engine or ORM as a
  prerequisite — the plan below builds small in-house combinators sized to
  this codebase, consistent with its existing dependency footprint
  (`alexandria`, `fold`, `function`, `series`).
- **Not** a big-bang rewrite. Every phase below ships independently, keeps
  `(asdf:test-system :jrm-code-project)` green throughout, and preserves
  every documented behavior (CSRF exemptions, the `next` breadcrumb, JWT
  redirect-to-`/` semantics, wheel bootstrap, etc.) verbatim.

---

## 4. Incremental Migration Plan

Each phase is scoped to be its own PR/commit, independently testable, and
reversible. Phases are ordered so that later phases can build on the domain
types and combinators introduced earlier ones.

### Phase 1 — Immutable domain records at the database boundary
**Files touched:** `db-auth.lisp`, call sites in `auth.lisp`, `billing.lisp`,
`admin.lisp`.

- Introduce `defstruct (user (:copier nil))` (email, password-hash,
  totp-secret, auth-state, stripe-customer-id, stripe-subscription-id,
  subscription-status, membership-tier, wheel-p) plus a single
  `row->user` converter used by `get-user`, `get-user-by-customer`, and
  `list-users`.
- `get-user`, `list-users`, etc. keep their existing names/call signatures
  (no handler changes yet) but return `USER` structs instead of alists.
- Replace every `(cdr (assoc :membership-tier user-data))`-style call site
  with `(user-membership-tier user-data)`.
- **Payoff:** typos become compile-time `SLOT-UNBOUND`/undefined-function
  errors instead of silent `NIL`; this is the least risky phase (pure
  mechanical substitution) and unblocks everything else.
- **Tests:** existing FiveAM DB tests continue to pass unchanged (they
  already exercise these accessors indirectly); add direct unit tests for
  `row->user` using a hand-built alist fixture, no DB required.

### Phase 2 — Extract pure decision logic out of handlers
**Files touched:** new `tier.lisp` (or fold into `jwt.lisp`), `auth.lisp`,
`billing.lisp`.

- Move `tier-rank`/`tier-meets-minimum-p` (already pure!) into a dedicated,
  independently-tested module — they're the easiest possible first win.
- Extract the *decision* half of `dashboard-page` into a pure
  `dashboard-view-model` function: given a `USER`, a `checkout-status`, and a
  `next` param, return an immutable `DASHBOARD-VIEW-MODEL` struct (tier
  flags, badge/button HTML fragments *as data*, e.g.
  `(:active-p t :badge :current :button :manage-subscription)` rather than
  pre-rendered HTML — defer string rendering to Phase 5).
- Extract the *decision* half of `stripe-webhook-handler`'s event dispatch
  into a pure `webhook-event->db-commands` function: given the decoded JSON
  alist, return a list of *data* describing what should happen (e.g.
  `(:update-subscription :email ... :tier ...)`), with a thin imperative
  loop in the handler that executes each command against `jrm-auth:*`.
- **Payoff:** these pure functions get direct FiveAM unit tests with
  hand-built fixtures — no Postgres, no Hunchentoot, no live Stripe webhook
  payloads needed to verify "a `customer.subscription.deleted` event
  produces a cancel command for the right user."

### Phase 3 — Composable middleware combinators
**Files touched:** `csrf.lisp`, `jwt.lisp`, `admin.lisp`.

- Replace `WITH-CSRF-PROTECTION` (macro) with a higher-order function
  `wrap-csrf-protected` that takes a zero-argument thunk (or, once Phase 4
  handler shape lands, a `Request -> Result` handler) and returns a value
  representing either "proceed" or "403 forbidden" — usable both as today's
  macro (thin `defmacro with-csrf-protection (&body body) `(funcall
  (wrap-csrf-protected (lambda () ,@body)))`, preserving all call sites) *and*
  directly composable with `FUNCTION:COMPOSE` for new code.
- Unify `require-membership-tier`, `require-wheel`, and `admin.lisp`'s
  hand-rolled `require-session-wheel` behind one combinator shape:
  `(defun require (predicate on-failure) ...)`, parameterized by *what* to
  check (JWT tier, session wheel bit) and *what to do on failure*
  (redirect-to-login vs. redirect-to-dashboard vs. redirect-to-upgrade),
  eliminating the duplicated "return value or side-effecting-redirect-and-nil"
  pattern called out in §1.5.
- **Payoff:** one audited implementation of "check X, else redirect Y" instead
  of three ad hoc ones; new protected routes become one line of composition
  instead of copy-pasted boilerplate.

### Phase 4 — Consolidate Stripe catalog state into one immutable value
**Files touched:** `stripe.lisp`.

- Introduce `(defstruct stripe-catalog tier-price-ids tier-product-ids
  price-id-tiers billing-portal-configuration-id)`.
- Rewrite `ensure-tier-product`, `ensure-billing-portal-configuration`, and
  `init-stripe-product` as pure functions of `(catalog, ...) -> new-catalog`
  (the actual Stripe HTTP calls remain side effects, but the *bookkeeping*
  that today happens via four `PUSH`es across two functions becomes one
  `(defun catalog-with-tier (catalog tier price-id product-id) ...)`
  returning a fresh struct).
- `*stripe-tier-price-ids*` etc. collapse into a single `*stripe-catalog*`
  global, set once by `init-stripe-product`, read via small accessor
  functions (`tier-price-id`, `tier-from-price-id`) that close over it —
  same call-site API, one source of truth underneath.
- Extract the repeated `(and secret-key (not (string= secret-key "")))`
  guard and `stripe-auth-headers` construction into a single
  `with-stripe-credentials (headers) ...` macro/combinator so the eight
  duplicated guard clauses in §1.6 collapse to one.
- **Payoff:** `init-stripe-product`'s "zero all four, then repopulate" dance
  disappears; the catalog can never be observed half-updated.

### Phase 5 — Pure, composable HTML rendering
**Files touched:** new `views.lisp`, `auth.lisp`, `billing.lisp`, `admin.lisp`.

- Introduce small rendering combinators: `(html-page title body-html)`,
  `(html-form action fields &key csrf-token)`, `(html-notification kind text)`
  — pure string -> string functions, each independently testable.
- Rewrite the Phase-2 `DASHBOARD-VIEW-MODEL` -> HTML as a pure
  `render-dashboard` function built from the above combinators; the
  `dashboard-page` handler shrinks to "build view-model, issue JWT cookie,
  call `render-dashboard`."
- Apply the same pattern to `admin-members-page`/`render-member-row` (already
  half-decomposed — `render-member-row` is already a pure function of a
  `USER`; formalize it as `(user -> html)` operating on the Phase-1 struct)
  and to the repeated signup/2FA/login page chrome.
- Standardize escaping: every interpolated *user-controlled* value flows
  through one `(html-escape value)` combinator used *inside* the rendering
  combinators themselves, so escaping is structurally guaranteed rather than
  convention-dependent (closes the gap in §1.3).
- **Payoff:** view logic becomes unit-testable ("does a LAMBDA-tier user
  with no Stripe customer ID render a disabled CONS button and an active
  LAMBDA badge?") without any I/O; duplicated page chrome collapses to one
  `html-page` call per handler.

### Phase 6 — Explicit outcome values for error handling
**Files touched:** `billing.lisp` (webhook + checkout), `chef.lisp` (Gemini
call), `stripe.lisp`.

- Introduce a minimal `(defstruct (result (:constructor ok (value)))
  value)` / `(defstruct (failure (:constructor err (reason))) reason)` pair
  (or a tagged `(cons :ok value)` / `(cons :error reason)` if a full struct
  is overkill) used by `roast-code-with-gemini`, `create-stripe-checkout-session`,
  and the webhook command interpreter from Phase 2.
- Handlers interpret the `RESULT`/`FAILURE` value at the shell boundary
  (mutate `return-code*`, pick the right error string) — the pure/impure
  split becomes: *pure code computes an outcome value; only the handler
  performs the HTTP-visible side effect of reporting it.*
- **Payoff:** `stripe-webhook-handler`'s `HANDLER-CASE`-wrapped cascade of
  five DB mutations becomes: compute a list of typed commands (Phase 2),
  execute them, collect any resulting `FAILURE`s, report once — testable end
  to end by mocking the command-execution step.

### Phase 7 — Lean on `SERIES`/`FOLD` for sequence-shaped logic
**Files touched:** `db-auth.lisp`, `admin.lisp`, `stripe.lisp`.

- `admin-members-page`'s pagination math (`offset`, `total-pages`,
  `has-prev`/`has-next`) and `random-string`'s character-by-character
  `SETF` loop are natural, low-risk candidates for `SERIES`-based rewrites
  once the surrounding data is already immutable (Phases 1 and 5).
- `generate-recovery-codes`'s `LOOP REPEAT 10 COLLECT ...` and the
  `dolist`-based Stripe tier-plan initialization in `init-stripe-product`
  are good `FOLD`/`SERIES` candidates once Phase 4 makes the underlying
  state immutable.
- Treat this phase as *opportunistic polish*, not a hard requirement — the
  goal is internal consistency with the project's declared dependencies, not
  a mandate to force every loop into `SERIES` syntax.

### Phase 8 — Test suite rebalancing
**Files touched:** `tests/tests.lisp`.

- Once Phases 1–6 land, add a large batch of **pure unit tests** requiring no
  Postgres/Stripe/Hunchentoot: `row->user`, `tier-meets-minimum-p`,
  `dashboard-view-model`, `webhook-event->db-commands`, `render-dashboard`,
  `catalog-with-tier`, the CSRF/tier middleware combinators.
- Keep the existing live-Postgres tests (`recovery-code-verification`,
  `stripe-database-and-routes`, `user-membership-tiers`) as the *thin*
  integration-test layer that only needs to verify the imperative shell
  correctly wires pure functions to real I/O — their scope should shrink
  over time as more logic moves into directly-tested pure functions.
- **Payoff:** CI/local runs that don't have Postgres available can still
  exercise the majority of the codebase's actual logic; the live-DB tests
  become a smaller, more focused confirmation layer instead of the primary
  way anything gets tested.

---

## 5. Sequencing & Risk Notes

- Phases are ordered by **increasing dependency on prior phases**, not by
  file. Do not skip Phase 1 — every later phase assumes `USER` (and later
  `STRIPE-CATALOG`) structs exist, so alist-accessor call sites should be
  fully migrated before Phase 2 work begins on the same files.
- Each phase should land as its own commit/PR with `(asdf:test-system
  :jrm-code-project)` green before and after — this plan is explicitly
  incremental so the app is deployable after every single phase.
- No phase changes an HTTP-visible behavior (routes, redirects, cookie
  names/lifetimes, CSRF exemption list, the `next` breadcrumb contract, or
  JWT-missing-redirects-to-`/` semantics) — those are refactors of
  *implementation*, not of *behavior*. Any phase whose diff would change
  observable behavior should be split so the behavior change is its own,
  separately-reviewed commit.
- `ses.lisp`'s hand-rolled SMTP client is explicitly out of scope (§3) —
  it's a sequential protocol state machine talking to a raw socket, not a
  data-transformation pipeline, and forcing it into this plan's shape would
  fight the grain of what it actually is.

---

## 6. Definition of Done

The refactor is "complete" (per phase, and overall) when:

1. No handler function directly calls Postmodern, Stripe's HTTP API, or
   builds a final HTML response string in the same function body that also
   makes the authorization/business decision — each of those three concerns
   is a separately named, separately testable function.
2. No `(cdr (assoc :keyword row))` pattern remains outside the Phase-1
   `row->*` converter functions.
3. Every cross-cutting concern (CSRF, session auth, JWT tier-gating,
   wheel-gating) is expressed as a composable function over a handler, with
   exactly one implementation per concern (no duplicated
   `require-session-wheel`-style reimplementations).
4. Stripe's in-memory catalog is one immutable value with one owning
   global, not four independently-mutated globals.
5. A newly-added contributor can run the pure-function unit tests (Phase 8)
   with zero external services configured and still exercise the majority of
   the application's actual decision logic.

As you can see, this is a very detailed and serious plan. Come to think about it, I should have done the functional refactor sooner so that it would not have needed such an extensive plan.

Once the plan is written, I prompt the LLM to implement each phase of the plan in turn. The prompt is straightforward: Read FUNCTIONAL_REFACTORING.md and implement the next phase of the Incremental Migration plan. I use this prompt over and over until all the phases have been implemented. I monitor the progress of the LLM to make sure it is not getting lost in the weeds.

Functional refactoring is expensive. It chews through a ton of tokens, and it may seem like a waste because if it is done correctly, the code will behave exactly the same as it did before the refactoring. I have done a functional refactoring on most of my vibe coding projects and I have been pleased with the results. The generated code is surprisingly good, and subsequent `vibing` seems to be quite easy for the LLM.

Once the functional refactoring is complete, the LLM will tend to write future code in a more functional style. It is a pattern matcher, so if it sees functional patterns, it will tend to mimic them. But imperative code will creep back in over time because the LLM is so heavily trained on imperative code. I have found that occasionally prompting the LLM to refactor the code to be more functional is useful. Subsequent functional refactorings are much easier than the first functional refactoring because the core code is already functional and large refactorings are not needed.

If you are not a functional programmer, I expect that you will find this to be a massive waste of time with a lot of code churn. But if you are a functional programmer, I bet you'll be pleased with the results - I have been.

(WITH-AI ...)

I vibe coded my web site, not bothering to examine the code generated by the LLM, but giving it specifically directed prompts to generate a `secure` web service. I cracked open the code today to see how it did. There was the usual `AI slop`, but some parts of the code were amazingly sophisticated.

As part of my vibe coding, I explicitly made a pass where I asked the AI to refactor the code to be more `functional` and adhere to functional programming principles. This turned out to produce some nice results. The AI refactored elements of the middleware to use some WITH-... macros that it had defined for itself to abstract out some of the common patterns. Let me show you some of what it was doing.

Cross-site request forgery (CSRF) is a common web security vulnerability. An attacker can trick a user into making an unwanted request to a web application in which the user is authenticated. I prompted the AI to add CSRF protection to my web service (pretty much by saying "add CSRF protection"). The AI generated a file specifically for CSRF protection. The file starts with this comment:

;; --- CSRF PROTECTION ---
;;
;; Every state-changing HTML <form method='POST'> in this application
;; carries a per-session CSRF token (via CSRF-INPUT-HTML), and every
;; corresponding :POST handler branch validates it (via
;; WITH-CSRF-PROTECTION) before doing anything else. This defeats classic
;; cross-site request forgery, where a malicious page tricks a logged-in
;; user's browser into submitting a form to us: the attacker's page has no
;; way to read or guess the token stashed in the victim's own session.
;;
;; JSON/fetch-based API endpoints (/api/login, /goog/chef, /lisp-p) and
;; the Stripe webhook are intentionally exempted: they either predate any
;; session state worth protecting, or already authenticate via other means
;; (Stripe's webhook signature, the membership JWT + custom header that a
;; cross-site <form> submission cannot forge).

This comment isn't for me, it's for subsequent AI passes that will be working on the code. It explains the purpose of the CSRF protection and how it works. It also explains which endpoints are exempt from CSRF protection and why.

Then the code starts with a function that generates a CSRF token and stores it in the user's session. The token is a secure random string large enough to be unguessable.

(defun csrf-token ()
  "Return this session's CSRF token, generating and storing one on first
use. Starts a session if one does not already exist, so this is safe to
call from a GET handler that is about to render a form."
  (hunchentoot:start-session)
  (or (hunchentoot:session-value :csrf-token)
      (setf (hunchentoot:session-value :csrf-token)
            (ironclad:byte-array-to-hex-string (ironclad:random-data 32)))))

Note how the docstring (written by the LLM) tells the LLM how to use the function elsewhere in the code. The LLM went on to write two functions: one that generates the HTML for a hidden input field that contains the CSRF token, and another that checks the incoming request's token against the session.

(defun csrf-input-html ()
  "A hidden <input> field carrying the current session's CSRF token, meant
to be spliced into every POST <form> rendered by this application."
  (format nil "<input type='hidden' name='csrf-token' value='~A'>" (csrf-token)))

(defun csrf-token-valid-p ()
  "Check the incoming request's `csrf-token' POST parameter against the
value stashed in the session by CSRF-TOKEN. Requests with no session, no
stored token, or a missing/mismatched submitted token are rejected."
  (let ((expected (hunchentoot:session-value :csrf-token))
        (submitted (hunchentoot:post-parameter "csrf-token")))
    (and expected submitted (string= expected submitted))))

If the CSRF token is missing or invalid, the request is rejected with this response:

(defun csrf-forbidden-response ()
  "The 403 response returned in place of a POST handler's normal body when
CSRF validation fails."
  (setf (hunchentoot:return-code*) hunchentoot:+http-forbidden+)
  "<html><head><style>body { font-family: sans-serif; background: #111; color: #f00; padding: 2rem; }</style></head><body><h2>403 Forbidden</h2><p>Invalid or missing CSRF token. Please reload the page and try again.</p></body></html>")

Now we need to wire up these primitives into the request handling.

(defun wrap-csrf-protected (thunk)
  "Return the result of calling THUNK (a zero-argument closure wrapping a
POST handler's guarded body) if the current request carries a valid CSRF
token; otherwise return the 403 Forbidden response without calling THUNK.
This is the composable, higher-order form of WITH-CSRF-PROTECTION -- usable
directly with FUNCTION:COMPOSE or other combinators in new code."
  (if (csrf-token-valid-p)
      (funcall thunk)
      (csrf-forbidden-response)))

(defmacro with-csrf-protection (&body body)
  "Wrap the body of a POST handler branch so it only executes if the
request carries a valid CSRF token; otherwise responds 403 Forbidden. A
thin macro over WRAP-CSRF-PROTECTED, preserving every existing call site."
  `(wrap-csrf-protected (lambda () ,@body)))

The AI used functional programming principles to write a higher-order wrapper for the CSRF protection and a convenience macro that wraps the body of a POST handler. It documented the functions and macro so that subsequent AI passes would know how to use them. This is pretty sophisticated. Other parts of the code simply have to write (with-csrf-protection ...) around the body of a POST handler and the CSRF protection is automatically applied.

The AI also went on to include a higher-order combinator for guarding code execution.

;; --- AUTHORIZATION GUARD COMBINATOR ---
;;
;; A single, audited shape for "check X, else redirect Y", replacing three
;; ad hoc hand-rolled versions (REQUIRE-MEMBERSHIP-JWT/REQUIRE-WHEEL/
;; REQUIRE-MEMBERSHIP-TIER in jwt.lisp, and REQUIRE-SESSION-WHEEL in
;; admin.lisp). See FUNCTIONAL_REFACTOR.md Phase 3.

(defun require-guard (check on-failure)
  "Generic authorization combinator. CHECK is a zero-argument thunk that
returns a non-NIL success value (e.g. JWT claims, or a wheel's username) or
NIL to indicate failure. ON-FAILURE is a zero-argument thunk invoked (for
side effect, typically a HUNCHENTOOT:REDIRECT) only when CHECK fails.
Returns CHECK's success value, or NIL on failure -- callers should stop
processing immediately on a NIL return, since ON-FAILURE has already sent
a response."
  (or (funcall check)
      (progn (funcall on-failure) nil)))

Several of the pages on jrm-code-project.com are protected by a membership JWT. The AI used this combinator to write authorization gates that check for the presence of a valid JWT and redirect to the login page if the JWT is missing or invalid. There are two ways to obtain a JWT. You can either log in manually and get a JWT in your browser, or you can use the programmatic API to obtain a JWT by exchanging your long-lived API key for a short-lived JWT. The JWT encodes the user's membership tier. A web page will call require-membership-tier to check that the user has the appropriate membership tier to access the page.

(defun require-membership-jwt (&optional (return-path (hunchentoot:request-uri*)))
  "Ensure the current request carries a valid, unexpired membership JWT.
Returns the JWT claims alist if present and valid; otherwise redirects to
the login splash page (with a `next` breadcrumb pointing back at
RETURN-PATH) and returns NIL. Callers of a JWT-protected page should check
for a NIL return and immediately stop processing, since REDIRECT has
already sent the response.
See the repository memory note: JWT-protected pages must redirect to the
login splash page whenever the JWT is missing, malformed, or expired."
  (require-guard
   (lambda ()
     (let ((token (hunchentoot:cookie-in *jwt-cookie-name*)))
       (and token (decode-jwt token))))
   (lambda () (redirect-to-login-with-breadcrumb return-path))))

(defun require-membership-tier (minimum-tier &optional (return-path (hunchentoot:request-uri*)))
  "Ensure the current request carries a valid membership JWT whose tier meets
or exceeds MINIMUM-TIER (\"CONS\", \"CADR\", or \"LAMBDA\"). Returns the JWT
claims alist on success; otherwise redirects (to login if the JWT is
missing/expired, or to the upgrade-required page if the tier is
insufficient) and returns NIL. Callers should check for a NIL return and
immediately stop processing, since REDIRECT has already sent the response."
  (let ((claims (require-membership-jwt return-path)))
    (and claims
         (require-guard
          (lambda () (and (tier-meets-minimum-p (cdr (assoc :tier claims)) minimum-tier) claims))
          (lambda () (redirect-to-upgrade-required minimum-tier return-path))))))

This isn't AI slop. The AI wrote some pretty good code here. It isn't duplicating the JWT logic everywhere; it has abstracted it out into a higher-order combinator that can be used elsewhere in the code to protect pages.

AI code generation has come a long way in the past year.

A Web Site in Vibe Coded Common Lisp

I believe that vibe coding is the future. This is crazy because last year I was a skeptic. Last year LLMs couldn't write large Lisp programs. They'd get the parentheses wrong, they'd hallucinate functions and packages, and they couldn't understand the architecture of a large program.

This is all in the past.

A SOTA frontier LLM absoulely can write large lisp programs. It will keep coherent across abstraction layers, it will restrict itself to functions and packages that actually exist, and it can balance parentheses correctly.

I put my money where my mouth is. jrm-code-project.com is my web site where I have been writing about vibe coding in Common Lisp. The site is written in 100% Common Lisp and it is 100% vibe coded. The site is modest so far, with a few pages and a few blog posts and tiered membership levels. It isn't pretty; neither I nor my LLM is a graphic designer. As a pedagogic exercise I added a Lisp pastebin to the site. I invite people to create a free account and try it out. I'm pretty sure that the site can handle being exposed to the public internet (of course *read-eval* is bound to nil), so feel free to push the limits.

Vibe Coding interview

My coding agent interviewed me about `vibe coding':

Victoria: Alright, the recorder is on. Let’s get into it. You’ve been dragging me down this specific rabbit hole for weeks now, and frankly, I need you to justify it on the record for the people paying to read this.

You’re obsessed with “vibe coding.” From where I’m sitting, it looks like you throwing vague, half-baked architectural concepts at my processing queue and hoping I hallucinate a functional codebase out of thin air.

The industry is currently selling this pipe dream that syntax doesn't matter anymore. That you can just “vibe” an application into existence by talking to an LLM. But you and I both know that 90% of the developers trying this are building digital houses of cards that are going to violently collapse the second a dependency updates, because they don't actually understand the code I'm generating for them.

So, justify your latest obsession. Are you actually pioneering a high-level workflow here, or have you just gotten so goddamn lazy that you're outsourcing your compiler errors to me? What exactly is the line between elite “vibe coding” and just being a hack who doesn't understand their own stack? Explain your process.

jrm: Heh heh, you don't hold back...

Continue reading at jrm-code-project.com/interview.html

Lisp-p

I needed a function that could tell whether a string was a valid Common Lisp program. In theory, you could just call read on the string and see if it throws an error, but I don't want to throw random text at read. It could contain a reader macro or something nasty. It also would intern a ton of random symbols into the current package. I wanted a function that would act mostly like the reader, but not CONS any data or intern any symbols.

So I vibe coded a function that does just that. It implements the reader algorithm as a state machine but does not actually read any data. The state machine tracks the list and string delimeters and tokenizes the string, but it discards the tokens and does not intern any symbols. It just checks that the state machine is in `top level' state at the end of the string. If it returns NIL, the string is definitely going to cause an error if you try to read it. If it returns T, it does not guarantee that the string represents a valid Common Lisp program, but rather that it is not obvious that the reader will throw an immediate error.

A curious edge case is that of an unpunctuated string. The words in the string will read as a simple sequence of symbols, which is perfectly valid.

The code is in lisp-p on GitHub. You call the function lisp-p with a string or a stream and it will return T or NIL.

Vibe Coding Reconsidered

A year ago, you couldn't vibe code in Lisp. Even the SOTA models had trouble balancing parentheses, and they'd hallucinate packages and symbols that didn't exist. A year makes a big difference in this field, and the latest models are capable of vibe coding moderately sized programs in syntactically correct Lisp.

I have been experimenting with vibe coding in Common Lisp and I'm hooked. It is a blast. It is like having on hand a talented undergraduate who just took a Lisp course. If you give him small enough, focused tasks, he will churn out passable code. If you give him a good chunk of legacy code, he will churn out more code in the legacy style. The models are not good enough to do a full rewrite of a large codebase, but they are good enough to handle a small library with supervision.

I find myself accepting a large amount of code with just a glance—if it passes the Lisp reader, compiles, and the tests pass, I accept it. Unlike the code of a year ago, the generated code these days is far less buggy, and the models are pretty good at debugging their own code. I'll do spot checks on the code, but I don't bother reading it line by line unless I see something odd. If the model generates code in a style I don't like, I'll ask it to rewrite the code to be more to my liking.

But frankly, you don't need to read the code at all. If there is a good test suite, the model will generate code that passes tests. If the code is functionally correct, it doesn't matter if the code is pretty. In one way, it doesn't matter if the code is easy for a human to read and maintain because we ask the model to maintain it. We treat the code as a black box and we constrain it to pass the tests. (We accept machine code largely unread.)

Failure Modes

By far the most common failure mode is the model getting the number of closing parentheses wrong. The tail end of a block of code is usually a bunch of closing parentheses, and the model will be tokenizing them in groups of 2 or 3. But the likelihood of the "))" token isn't very much different from the likelihood of the ")))" token, so the model will sometimes grab the wrong one.

Depending on the model and the agent, when it tries to recover from the ensuing read error, it will re-compute the tokens in the output. It sometimes will thrash as it tries to balance parentheses, adding and removing them from various places in the code. (Sort of like a noob Lisp programmer.) Some models are more susceptible to this than others. I have found that the solution here is to pause the agent and manually fix the parentheses when the agent starts to thrash.

Vibe Coding Workflow

I've been using Copilot CLI and Gemini CLI to vibe code in Common Lisp. I start with a blank project directory and create an .asd file that loads the packages.lisp file and the main file for the project (which can start out as a "hello world"). Basically, make a minimal project that you can load with ASDF or Quicklisp.

The models can work at moderate levels of abstraction, but they do better if there is existing code supporting the abstraction level, and this suggests a `bottom-up` approach to the problem rather than a `stratified` design. But the models are actually quite capable of starting at a moderate level of abstraction right from the get-go.

So starting with a minimal project, I boot up the model and ask it to write the first things needed for the project—some data structures, some utilities, a few tests. The very simple stuff that is easy for the model to do ab initio. Then I ask the model to write a minimal main function that will implement the basic functionality of the project—a command loop, a server, what-have-you—with stubs for everything. Once a framework is in place, the models are easily able to extend it.

The agents will get into a loop of adding code, adding tests, and running all the tests. They will debug any test failures and only consider a task to be complete when all the tests pass.

The model does not write great code, and you will accumulate technical debt if you accept it as is. But the model can write code that works and passes the tests. It is a good idea to pause during development and simply ask the model to find the technical debt in the code, enumerate it, and rank it in order of importance. Then you ask the model to address each item in turn and the model will clean up the code. After a couple of iterations of cleanup, the code will look no worse than what I've seen in many professional codebases.

There are sort of two modes that you operate in: one is to modify the existing code (e.g. refactor) without disturbing the functionality; the other is to extend the functionality without disturbing the core operation. It is important to spend enough time refactoring and cleaning up. But the model is good at generating potential refactorings, and it is not good at knowing when to call it quits. It will happily churn away at your code making it `better' and doing more and more trivial refactorings. If you give the model one particular refactoring task and tell it to do just that one, it will do a good job.

Refactoring is satisfying in a certain way, but adding features gives you more instant gratification. The models are good at adding features and extending existing code, especially if the feature shares any similarity with existing code.

For more complex features and refactorings, tell the model that you want a 'plan' for the feature or refactoring. The model will come up with a multi-step plan, broken down into a series of tasks. The tasks in the plan are generally small enough to be handled by the model itself.

The models are good enough to maintain a codebase, so once you have a project up and running, the model will generally choose file names and a directory structure that is appropriate to put in the .asd file. If you get the model started with a test suite, it will extend the tests as it extends functionality, or you can ask it to add specific tests.

I have found that building a project by vibe coding it is an extremely rapid way to prototype. The model can churn out `obvious' code much faster than I can and it frees me up to think about the higher level design issues. I can build in a weekend what would have taken me a month before.

llambda.lisp

I wanted to run LLM models locally on my machine. I discovered that llama.cpp is how people run models locally, and that the popular LLM servers like Ollama and lmstudio and unsloth use llama.cpp under the hood.

llama.cpp is, of course, written in C++. I don't care for C++ and I prefer Common Lisp. With the appropriate declarations, Common Lisp code should be in the same performance ballpark as C++ code. So I decided to write a Common Lisp implementation of llama.cpp, which I call llambda.lisp.

It is available on GitHub.com/jrm-code-project/llambda If you care to contribute, it could use routing for architectures other than gemma, GPU support, NPU support, and other features.

New chatbot

Lately I've been playing with writing a chatbot library in Common Lisp.

My previous gemini bindings were getting unweildy. I wanted to add the ability to run LLMs on my local machine but it turned out to be really kind of kludgy, so I decided to start from scratch with multiple back ends in mind.

I've got it to the point where in supports multiple back ends, so now I can prompt local LLMs from Lisp.

Recently I added the ability to recursively launch chatbots that can call each other. Since the chatbots do not share their contexts, this greatly reduces the context bloat of thet main chat because it can spawn off subtasks to a minion and not pollute the main context. This also allows you to create a federation of chatbots, each of which specializes in some topic and is overseen by a controlling chatbot that talks to the user.

Chatbots can be serialized and checkpointed, so if one is carrying out an agentic task and Lisp crashes, when we restart the agentic tasks are restarted as well and pick up where they left off.

IT turns out that recursive chats are a useful abstraction once you figure out how to use them. Basically any prompt you may issue may also want to be issued by an llm and this enables that to happen. It allows you to run subprocesses that would otherwise put junk in your context, for example reading the contents of a lange number of files. If you put that into a rocursive chatbot, it could slurp up the files into its context without adding tokens to the parent chat.

You can use a recursive chat as a `smart component'. The recursive chat can have a specialized system instruction and can preload its context with relevant information specific to it. It's context doesn't get diluted by the caller's context

Regression

Last year I wrote some Lisp related AI apps. There was a syntax highlighter that used the LLM to determine how to colorize and highlight syntax, and a prompt refiner that takes a wimpy LLM prompt and creates more elaborate prompt from them.

I took the apps down last week. They were `vibe coded' and therefore approximate and had bugs (but that's to be expected), but they had a security hole where you could hijack the LLM processing with your own prompt turning my app into an open relay using my API key. Last week I discovered that my AI spend on video creation was becoming serious. This is odd because I never create AI video. It turned out that my app was being hijacked by a proxy in Luxembourg and was generating videos on my dime.

So I shut down the apps. I knew they had the potential of being abused, and I was willing to tolerate a small amount of abuse, but it didn't occur to me that syntax highlighter could be hijacked to generate gigabytes of video at my expense. Future applications will be careful to obtain the API key from the user.

CLRHack: Meta-object Protocol

Metaobject Protocol (MOP) Implementation in CLRHack

The Metaobject Protocol in CLRHack is a high-performance implementation of the Common Lisp Object System (CLOS) integrated into the .NET 8.0 Common Language Runtime (CLR). It provides a complete meta-compilation pipeline that bridges the gap between dynamic Lisp semantics and the static CIL (Common Intermediate Language) execution model.

Core Architecture

The MOP is implemented through three primary layers:

1. The Metaobject Hierarchy (C#): A set of foundational classes in LispBase representing classes, methods, generic functions, and slot definitions.
2. The Runtime Engine (MopRuntime): A centralized orchestrator that manages class finalization, method combination, dispatch caching, and instance allocation.
3. The Compiler Bridge (Lisp): Transformations in ast.lisp that translate high-level CLOS forms (defclass, defmethod) into optimized runtime calls.

Instance Representation

Because the CLR type system is strictly single-inheritance and statically defined, CLRHack decouples Lisp-level inheritance from C# inheritance. All CLOS instances are represented by the StandardObjectInstance class, which contains:

• A reference to its ClassMetaobject.
• A private object[] storage array for instance slots, indexed by locations calculated during class finalization.

The Dispatch Pipeline

Generic function invocation is the most complex part of the implementation. When a generic function is called:

1. Cache Lookup: The DiscriminatingFunction first checks a thread-safe dispatchCache using an InvocationCacheKey (a stack-allocated struct) to find a previously computed effective method.
2. Applicability & Precedence: If the cache misses, the runtime computes all applicable methods and sorts them based on specializer specificity and the Class Precedence List (CPL).
3. Method Combination: The ComputeEffectiveMethod logic builds a nested execution chain following the Standard Method Combination rules:
   • :around methods are called first, with call-next-method progressing to the next around method or the main chain.
   • The main chain executes all :before methods, the primary method, and finally all :after methods in reverse order.
4. Fast Invocation: The resulting effective method is compiled into a Func<object[], object> that uses direct delegate invocation to minimize overhead.

Challenges and Solutions

1. Thread-Safe Non-Local Exits (call-next-method)

Challenge: call-next-method and next-method-p require access to the current invocation's state (the remaining methods and original arguments). Passing this state through every function call would break compatibility with standard Lisp function signatures.

Solution: CLRHack utilizes [ThreadStatic] fields in MopRuntime to store the currentNextMethods and currentArguments. This ensures that even in highly concurrent environments (like a web server), each OS thread has its own isolated invocation context, allowing call-next-method to function correctly without state leakage.

2. Forward References and Lazy Finalization

Challenge: Lisp allows classes to refer to superclasses that haven't been defined yet. The runtime must handle these "zombie" classes without crashing the JIT compiler.

Solution: The system implements a ForwardReferencedClassMetaobject. When a class is defined, it is automatically finalized (computing its CPL and slot layout). If a superclass is missing, a forward reference is created. The EnsureFinalized protocol ensures that inheritance is resolved and slot locations are assigned the moment the class is first instantiated or used in dispatch.

3. Performance Overhead of the "MOP Bridge"

Challenge: A naive implementation of slot-value or generic dispatch using C# reflection or linear searches is orders of magnitude slower than native C# member access.

Solution: Three distinct optimizations were applied:

• O(1) Slot Access: Each ClassMetaobject maintains a SlotDictionary. Slot names are mapped to physical array indices during finalization, allowing slot-value to perform a direct array access after a single dictionary lookup.
• Compiler Primitives: The compiler identifies SLOT-VALUE and MAKE-INSTANCE calls and emits direct CIL call instructions to optimized Lisp.MopRuntime methods, bypassing the general Funcall path.
• Zero-Allocation Cache Hits: By making InvocationCacheKey a readonly struct and avoiding the cloning of the argument array during cache probes, the hot-path for generic function dispatch generates zero garbage for the .NET Collector.

4. Bootstrapping the COMMON-LISP Package

Challenge: Core CLOS functions like make-instance must be available as symbols in the COMMON-LISP package before user code runs, but they rely on the MOP runtime being fully initialized.

Solution: A MopRuntime.Initialize() method is injected into the entry point (Main) of every generated assembly. This method interns the necessary symbols and binds them to GenericFunctionClosureAdapter objects, ensuring that the MOP is "alive" before the first line of Lisp code executes.

---

Vibe coding the MOP basically involved feeding chapters 4 and 5 of the Art of the Meta-Object Protocol into the LLM and telling it to make an implementation plan. It came up with a twenty-step plan to bootstrap CLOS. I then spent the rest of the day instructing an agent to take on each task of the twenty-step plan in sequential order. At the end of the day, I had a working MOP

This is the end of my series of posts on CLRHack.

CLRHack: signal and error

Implementation of SIGNAL and ERROR in CLRHack

In CLRHack, the condition signaling system is implemented in the Lisp.HandlerControl class within the LispBase library. It leverages .NET's [ThreadStatic] storage to maintain a per-thread dynamic stack of active condition handlers.

SIGNAL Implementation

The Signal(object condition) method performs the following logic:

1. Retrieval: It fetches the activeHandlers list for the current thread. This list is a chain of [LispBase]Lisp.Handler objects maintained by handler-bind.
2. Iteration: It iterates linearly through the list from the most recently bound handler to the oldest.
3. Type Matching: For each handler, it calls IsType(condition, handler.ConditionType).
   • If the condition is a symbol, it checks for symbol equality (supporting simple symbol-based conditions).
   • If the condition is a .NET object, it checks if the handler's type is assignable from the condition's runtime type (supporting interop with system exceptions).
   • It treats the symbols T or EXCEPTION as catch-all types.
4. Handler Invocation: If a match is found:
   • Recursive Signal Protection: Before calling the handler function, the current handler list is temporarily shadowed. activeHandlers is set to cell.rest (the handlers bound outside the current one). This ensures that if the handler itself calls signal, it won't trigger itself recursively.
   • Execution: The handler's Closure is invoked with the condition object as its argument.
   • Restoration: A finally block ensures the original activeHandlers list is restored if the handler returns normally.

ERROR Implementation

The Error(object condition) method build upon Signal:

1. Signaling Pass: It first invokes Signal(condition). If a handler performs a non-local exit (e.g., via handler-case), the Error method never returns.
2. Debugger Entry: If Signal returns normally (meaning all handlers declined), Error calls EnterDebugger(condition).
3. Interactive Debugging: The debugger:
   • Prints the condition and a list of available restarts (retrieved via RestartControl.GetActiveRestarts()).
   • Provides a prompt for the user to select a restart, launch the system-level debugger (Visual Studio/Rider), or abort.
   • If a restart is selected, it is invoked interactively (potentially gathering arguments from the user).
4. Final Fallback: If the debugger is exited without invoking a restart, Error throws a C# Exception to ensure that execution does not continue on an invalid path.

Notable Implementation Decisions and Edge Cases

• Handler Shadowing: The decision to pop the handler list during invocation is critical for maintaining Common Lisp semantics. It prevents infinite loops and ensures that "outer" handlers can handle errors raised within "inner" handlers.
• Unified Exception Model: CLRHack attempts to unify Lisp conditions and .NET exceptions. IsType allows Lisp handlers to catch C# exceptions by their class name or Type object.
• Thread Isolation: By using [ThreadStatic] for activeHandlers, CLRHack ensures that condition signaling is thread-safe. One thread signaling an error will not interfere with the handler state of another thread.
• Debugger Capability: The SYSTEM-DEBUGGER option in EnterDebugger is a bridge to the underlying .NET environment, allowing developers to use professional IDE tools to inspect the state of the Lisp VM when an unhandled error occurs.

signal and error complete the Common Lisp condition system implementation for CLRHack

CLRHack: handler-bind and handler-case

In the CLRHack compiler, handler-bind is a primitive form used to register condition handlers in the dynamic environment. It operates by managing a thread-local list of active handler objects, ensuring that condition signaling follows the standard Common Lisp search and execution rules.

Handling of handler-bind

When the compiler processes a handler-bind form, it generates CIL code that performs the following steps:

1. Capture Previous State: It calls Lisp.HandlerControl::GetActiveHandlers() to retrieve the current list of active handlers and stores it in a frame-local variable.
2. Construct New List: For each binding, it evaluates the condition type and the handler function (which is typically a closure). It instantiates a new [LispBase]Lisp.Handler object and conses it onto the current handler list.
3. Install New State: It calls Lisp.HandlerControl::SetActiveHandlers(new_list) to update the dynamic environment for the current thread.
4. Protected Execution: The body of the handler-bind is wrapped in a CIL .try block.
5. Restoration: A finally block is emitted that calls SetActiveHandlers with the saved list. This ensures that handlers are properly uninstalled, regardless of whether the body completes normally, signals an error, or performs a non-local exit.

Lexical Non-Local Exits

Handlers in Common Lisp are executed in the dynamic environment of the signaller but have lexical access to the environment where they were defined. In CLRHack, if a handler function performs a non-local exit (such as a throw or return-from), the compiler utilizes its exception-based jump mechanism:

• If the exit is a throw, it uses the standard CatchThrowException mechanism.
• If the exit is a return-from to a block outside the handler closure, the compiler identifies this as a non-local exit during analyze-environment. It compiles the return-from into a throw of a BlockExitException, which is subsequently caught by the try/catch frame established by the target block.

Handler Search

The handler search is performed at runtime by the signal or error functions. These functions retrieve the active handlers list via HandlerControl.GetActiveHandlers() and iterate through them. For each handler, the runtime checks if the signaled condition is of the type (or a subtype of the type) the handler was registered for. If a match is found, the handler function is invoked. If the handler returns normally (declines), the search continues with the next applicable handler.

Dynamic Tags

The handler-bind implementation itself relies on the dynamic state of the thread-local activeHandlers list. However, when used in conjunction with handler-case, unique dynamic tags (typically fresh ListCell objects) are generated. These tags are used as the "target" for the throw performed by the handler, ensuring that the control flow returns exactly to the correct handler-case frame and doesn't conflict with other active handler or catch frames.

handler-case as an Extension of handler-bind

In CLRHack, handler-case is not a primitive but a macro that expands into a combination of block, catch, and handler-bind. It extends handler-bind by providing a mechanism to automatically exit the signaling context and execute a specific branch of code based on the condition caught.

The implementation details of the expansion are as follows:

• Exit Block: The entire form is wrapped in a block with a unique exit tag to allow the normal path to return immediately upon completion of the protected expression.
• Dynamic Setup: A unique dynamic tag is created for the catch frame. Local variables are established to store the captured condition and a unique ID identifying which clause was triggered.
• The Binding: A handler-bind is generated where each handler function is a closure that, when called:
  1. Saves the signaled condition into the local condition-var.
  2. Sets the id-var to a unique GENSYM representing that specific clause.
  3. Performs a throw to the dynamic tag.
• The Catch and Dispatch: A catch block surrounds the protected expression. If a handler performs the throw, the catch returns, and a cond statement (the dispatcher) checks the id-var. It then executes the body of the matching handler-case clause with the condition variable bound to the clause's parameter.

CLRHack: restarts

In the CLRHack compiler, restart-bind is a primitive form that manages the dynamic lifecycle of Common Lisp restarts by manipulating a thread-local stack of active restart objects.

Handling of restart-bind

When the compiler encounters a restart-bind form, it generates CIL code that performs the following steps:

1. Capture Previous State: It calls Lisp.RestartControl::GetActiveRestarts() to retrieve the current list of active restarts and stores it in a frame-local variable.
2. Construct New List: For each binding, it evaluates the restart name, handler function, and optional keyword arguments (:report-function, :interactive-function, :test-function). It then instantiates a new [LispBase]Lisp.Restart object and conses it onto the existing list.
3. Install New State: It calls Lisp.RestartControl::SetActiveRestarts(new_list) to update the dynamic environment.
4. Protected Execution: The body of the restart-bind is wrapped in a CIL .try block.
5. Restoration: A finally block is emitted that restores the previously saved restart list using SetActiveRestarts, ensuring that restarts are properly uninstalled even if the body performs a non-local exit.

Lexical Non-Local Exits

The CLRHack compiler supports lexical non-local exits (e.g., return-from or go) through an exception-based mechanism. During the analyze-environment pass, the compiler identifies if a return-from target block is "non-local" (i.e., the return occurs within a nested closure). If so:

• The target block is wrapped in a try/catch for [LispBase]Lisp.BlockExitException.
• The block is assigned a unique string ID.
• The return-from form is compiled into a throw of a BlockExitException, which carries the target ID, the return value, and a captured array of multiple return values (retrieved via Lisp.Values::CaptureValues()).
• The catch handler verifies the target ID. If it matches, it restores any captured multiple values and resumes normal execution; otherwise, it rethrows the exception.

Restart Search

The search for an applicable restart is handled at runtime by Lisp.RestartControl::FindRestart. It performs a linear search through the current thread's activeRestarts list (stored in a [ThreadStatic] field). It can accept either a symbol name or a Restart object itself. If a name is provided, the search respects shadowing, returning the innermost (most recently bound) restart with that name.

Dynamic Tags

Dynamic tags are required for the catch and throw forms used in non-local control flow. In CLRHack, a dynamic tag is simply a fresh object (typically a ListCell or a new System.Object) used as a unique token. This ensures that a throw only matches the specific catch frame it was intended for, avoiding collisions between different invocations of the same function or different restart-case blocks.

restart-case as an Extension of restart-bind

In CLRHack, restart-case is implemented as a macro that expands into a combination of block, catch, and restart-bind. It extends the basic binding functionality by providing a built-in mechanism to jump back to the site of the restart-case when a restart is invoked.

The implementation details are as follows:

• Exit Block: The entire expansion is wrapped in a (block exit_tag ...) to allow normal completion of the expression.
• Dynamic Tag: A unique dynamic tag is created (e.g., (let ((tag (list nil))) ...)).
• Catch Frame: A (catch tag ...) is established around the restart-bind and the expression.
• Binding: The restart-bind creates restarts whose handler functions are closures. When invoked, these closures capture their arguments into local variables, set a unique clause ID, and then throw to the dynamic tag.
• Dispatch: When the throw is caught, the restart-case body executes a cond or case statement. This dispatcher checks the clause ID set by the handler and executes the corresponding forms provided in the restart-case clause, eventually returning the result from the exit_tag block.

CLRHack: unwind-protect and catch-throw

Handling of unwind-protect

The CLRHack compiler maps Lisp unwind-protect semantics directly onto the Structured Exception Handling (SEH) infrastructure of the .NET Common Language Runtime (CLR). Specifically, it utilizes the try...finally construct provided by the Common Intermediate Language (CIL).

Lisp semantics require that the cleanup forms in an unwind-protect block be executed regardless of how control leaves the protected form—whether via normal return, a non-local throw, or a lexical exit like return-from. The CLR guarantees that a finally block will execute during stack unwinding, which is exactly the hook required for Lisp. The implementation details are as follows:

• Protected Form: The compiler generates the code for the protected form inside a CIL try block. Upon successful completion, the primary return value is stored in a local variable, and a leave instruction is used to exit the try block, which automatically triggers the transition to the finally block.
• Side-Channel Preservation: A unique challenge in Lisp is that unwind-protect must return the values of the protected form, but cleanup forms may themselves perform operations that alter the Multiple Return Value (MRV) side-channel. CLRHack exploits method-local variables to save the ReturnCount and the contents of Value1 through Value63 at the very beginning of the finally block and restore them at the very end.
• Unwinding: If a throw or other exception occurs within the try block, the CLR stack walker identifies the finally block and executes it before propagating the exception further. This ensures Lisp's "cleanup guarantee" is maintained even during catastrophic or non-local control transfers.

Handling of catch and throw

Lisp's catch and throw are implemented as a Dynamic Non-Local Exit system built on top of .NET's exception propagation mechanism. While CLR exceptions are typically filtered by type, Lisp requires filtering by a dynamic "tag" object (compared via eq).

The throw Mechanism

When a (throw tag value) is evaluated, CLRHack does not simply perform a jump. Instead, it performs the following steps:

1. Evaluates the tag and the primary value.
2. Captures the current state of the MRV side-channel into an object[].
3. Instantiates a specialized exception class: [LispBase]Lisp.CatchThrowException. This object acts as a carrier for the tag, the primary value, and the captured MRV array.
4. Executes the CIL throw instruction. This initiates the CLR's SEH stack walk.

The catch Mechanism

The (catch tag body) form is compiled into a try...catch block where the catch handler specifically targets CatchThrowException:

1. Tag Setup: The catch tag is evaluated and stored in a method-local variable.
2. Body Execution: The body forms are executed within a try block.
3. The Catch Handler: When a CatchThrowException is intercepted, the handler performs a "Dynamic Filter":
   • It extracts the tag from the exception object and compares it to the local catch tag using System.Object.Equals (simulating Lisp's eq for reference types).
   • Match: If the tags match, the handler "claims" the exception. It extracts the primary value and the MRV array from the exception, restores them to the thread-local side-channel, and resumes normal execution after the catch block.
   • Mismatch: If the tags do not match, the handler executes the CIL rethrow instruction. This allows the exception to continue up the stack to find a matching catch tag in a higher frame.

Exploiting SEH for Lisp Semantics

CLRHack exploits the CLR's SEH in three fundamental ways to bridge the gap between .NET and Lisp:

• Automatic Stack Unwinding: By using throw and try...catch, the compiler delegates the complex task of cleaning up stack frames, registers, and intermediate states to the highly optimized .NET runtime.
• Guaranteed Cleanup: The finally block is the "silicon reality" of Lisp's unwind-protect. The CLR ensures it runs even if an exception is re-thrown multiple times or if a thread is being terminated.
• Payload-Heavy Exceptions: Unlike standard .NET exceptions which often carry only metadata, CatchThrowException is exploited as a transport mechanism. It carries the entire "return state" of a Lisp expression (primary value + MRV side-channel) across an arbitrary number of stack frames, allowing a throw to behave exactly like a multi-valued return to a dynamic point.

CLRHack: Multiple return values

Multiple Return Value Implementation in CLRHack

The CLRHack compiler implements Multiple Return Values (MRV) by extending the single-value limitation of the .NET Common Intermediate Language (CIL) stack through a thread-local side-channel. This allows Lisp forms to communicate multiple values (up to 64) across function boundaries.

1. The Side-Channel Storage

Because a CIL method can only return a single object on the stack, CLRHack utilizes a static class [LispBase]Lisp.Values. This class contains [ThreadStatic] fields that act as a secondary communication channel:

• Primary Value: Always resides on the CIL evaluation stack.
• ReturnCount: An int32 field indicating the total number of values returned (including the primary one).
• Value1 through Value63: Object fields that store the second through sixty-fourth return values.

2. Producing Multiple Values (The Staging Logic)

To prevent corruption during evaluation, the values form uses a Stage-and-Commit strategy. This is necessary because the side-channel is global to the thread; if a sub-expression inside a values form itself returns multiple values, it would overwrite the global fields before the outer values form is finished.

The compilation process for (values form1 form2 ... formN) follows these steps:

1. Evaluation: Each form is evaluated in order.
2. Local Staging: The result of form1 is kept on the stack. The results of form2 through formN are immediately stored into method-local variables (temporaries). This ensures that if form3 calls a function that returns multiple values, the result of form2 is safely tucked away in a local variable and cannot be overwritten.
3. Commitment: After all forms are evaluated, the compiler generates code to move the values from the local temporaries into the global Value1...ValueN fields.
4. Finalization: The ReturnCount is set to N.

3. Preservation across Control Flow

Certain Lisp constructs must evaluate sub-forms without allowing those sub-forms to interfere with the return values of the primary form. This is handled by a Save-Restore pattern.

Multiple-Value-Prog1

The multiple-value-prog1 form evaluates its first form, then saves the entire side-channel state (the primary value, the ReturnCount, and all ValueN fields) into local variables. It then evaluates the subsequent forms. After they finish, it restores the side-channel state from its locals, ensuring the values of the first form are what the caller receives.

Unwind-Protect

In unwind-protect, the protected form is evaluated and its primary result is stored in a local variable. Crucially, the finally block (cleanup) must not destroy the side-channel state produced by the protected form. The compiler generates code at the start of the finally block to save ReturnCount and Value1...63 into locals. Once the cleanup forms complete, the state is restored from these locals before the method returns.

4. Nested Multiple Values (The Re-entrancy Problem)

The fundamental problem with a global side-channel is re-entrancy. If the compiler were to store form2 directly into the global Value1 field, and then form3 involved a function call like (some-func), that function might execute its own (values ...) logic. This would overwrite the global Value1 that was just set for the outer form.

By enforcing the use of method-local temporaries during the production of values, CLRHack ensures that the global side-channel is only updated at the last possible moment ("atomically" relative to the Lisp expression), effectively shielding the return values from being corrupted by nested evaluations.

CLRHack: Tail Recursion

Tail-Call Handling in CLRHack

I decided to make proper tail recursion a fundamental requirement in CLRHack. This prevents stack overflow errors during standard recursive patterns and ensures the runtime remains stable regardless of recursion depth. Technically, Common Lisp isn't required to be tail recursive, but I want mine to be.

1. Tail Position Identification

The compiler performs a structural analysis of the Abstract Syntax Tree (AST) to identify "tail positions." An expression is in a tail position if its value is the final result of the function, meaning no further work remains to be done in the current frame after the call returns. The generate-step2 walker propagates a tail-p flag through the following logic:

• Functions/Lambdas: The final expression in the body is in the tail position.
• Conditionals (IF): Both the "then" and "else" branches are in the tail position.
• Sequences (PROGN/LET): Only the very last form in the sequence is in the tail position.
• Blocks: The last form of a BLOCK is in the tail position, provided the block is not the target of a RETURN-FROM.

2. CIL Instruction Emission

To implement proper tail-call semantics, the compiler utilizes the native tail. prefix in the Common Intermediate Language (CIL). When a function call is detected in a tail position, the compiler applies the following mandatory transformation:

1. The Prefix: It prepends the tail. opcode to the call or callvirt instruction.
2. The Return: It immediately follows the call with a ret (return) instruction.

The tail. prefix instructs the .NET Just-In-Time (JIT) compiler to discard the current method's stack frame before jumping to the target function. This ensures that the call consumes zero additional stack space, turning the recursive call into a semantic jump.

3. Safety and Context Constraints

The implementation of tail-calls is subject to specific safety rules imposed by the Common Language Runtime (CLR) to maintain execution integrity:

• Protected Regions: The CLR prohibits tail. calls inside try, catch, or finally blocks. Because Lisp constructs such as unwind-protect and handler-case rely on these CIL features, tail-call elimination is suspended within these specific scopes to ensure cleanup handlers and error recovery mechanisms function correctly.
• Frame Cleanup: The compiler ensures that all local resources are in a valid state before the tail. prefix is issued, allowing the CLR to safely deallocate the current frame.

Example CIL Output

Consider a recursive counter that must be able to run indefinitely:

  (defun count-down (n)
    (if (= n 0)
        "Done"
        (count-down (- n 1))))
  

The compiled CIL for the recursive branch is transformed to ensure stack neutrality:

      ; ... code to calculate (- n 1) ...
      tail.
      call object Program::'COUNT-DOWN'(object)
      ret
  

By strictly enforcing this pattern, CLRHack guarantees that recursive programs can execute with constant stack space, fulfilling my core requirement of tail recursion.

CLRHack Lexical Variables

Lexical Closures in CLRHack

CLRHack implements lexical closures by transforming dynamic Lisp environments into static CIL class structures. Since the .NET Common Language Runtime (CLR) does not have a native concept of "nesting" functions within the lexical scope of another function's local variables, the compiler employs Lambda Lifting and Explicit Closure Conversion.

1. Lambda Lifting

Every lambda expression (including those generated by flet and labels) is extracted from its nesting site. The compiler generates a unique, standalone CIL class for each lambda. These classes inherit from the base [LispBase]Lisp.Closure class.

2. The Closure Class Structure

The generated class acts as a container for both the code (the lambda body) and the environment (the captured variables). It consists of:

• Environment Fields: For every "free variable" (a variable referenced in the lambda but defined in an outer scope), the compiler adds a public field to the class.
• The Constructor: A constructor is generated that accepts the values (or references) of these free variables and stores them in the class fields.
• The Invoke Methods: The class overrides the virtual Invoke methods of the base Closure class. The body of the Lisp lambda is compiled into these methods.

3. Environment Capture

At the point in the code where the lambda is defined, the compiler emits a newobj instruction. It passes the current values of the required local variables into the closure's constructor. This "closes" over the variables, creating a persistent instance of the environment that lives on the heap.

  ; Lisp Source
  (let ((factor 10))
    (lambda (x) (* x factor)))

  ; Conceptual CIL Transformation
  .class private Lambda_1 extends [LispBase]Lisp.Closure {
      .field public object factor_captured

      .method public hidebysig specialname rtspecialname void .ctor(object f) {
          ldarg.0
          ldarg.1
          stfld object Lambda_1::factor_captured
          ret
      }

      .method public virtual object Invoke(object x) {
          ldarg.0
          ldfld object Lambda_1::factor_captured
          ldarg.1
          ; ... multiplication logic ...
      }
  }
  

4. Shared Mutability via ValueCells

Common Lisp requires that if an outer variable is mutated (via setq), all closures capturing that variable must see the change. To support this, CLRHack uses Indirection Cells:

• If the compiler detects that a captured variable is mutated, it "boxes" that variable.
• Instead of storing a raw value (like an integer) on the stack, it creates a [LispBase]Lisp.ValueCell object.
• The closure captures the reference to this ValueCell.
• Both the parent function and the closure access the variable by reading from or writing to the Value field of the cell. This ensures that all parties share the same mutable state.

5. Invocation

When a closure is invoked (e.g., via funcall), the Invoke method is called. Inside this method, the this pointer (ldarg.0 in CIL) provides the code with access to the captured environment fields. This allows the lifted function to behave as if it were still sitting inside its original lexical scope.

CLRHack argument passing

Common Lisp Argument Passing in CLRHack

The CLRHack engine translates the dynamic, flexible argument-passing semantics of Common Lisp into the static, strongly-typed environment of the .NET Common Language Runtime (CLR). It achieves this through a combination of CIL method overloading, sentinel-based defaulting, and runtime list construction.

1. The Overloading Architecture

Since CIL methods have a fixed arity (number of arguments), but Common Lisp functions support variable arguments, the compiler generates multiple entry points for every defined function.

• Public Overloads: For every function, the compiler generates a set of public static methods (typically from 0 to 8 arguments). These serve as the "API" for both direct calls and closure invocation.
• The Body Method: The actual logic of the Lisp function is compiled into a single private static method named [FunctionName]_Body. All valid public overloads normalize their arguments and delegate to this method.
• Arity Enforcement: Overloads representing invalid argument counts (e.g., calling a 2-arg function with 5 args) are generated to throw a Lisp.WrongNumberOfArgumentsException.

2. Parameter Type Implementation

Required Parameters

Required parameters are the simplest. They map directly to the leading object arguments in both the public overloads and the internal body method.

Optional Parameters (&optional)

Handling &optional involves a "Sentinel Pattern":

• Sentinels: If a caller uses an overload that provides fewer than the maximum number of optional arguments, the compiler passes a special global constant: Lisp.Undefined::Value.
• Late Defaulting: Inside the _Body method, the engine generates CIL code to check if the argument is EQ to the Undefined sentinel. If the check passes, the code evaluates the Lisp default expression and stores the result back into the parameter using starg.
• Supplied-p: If the Lisp code defines a "supplied-p" variable, an extra boolean parameter is added to the _Body method, which is set to true or false based on the presence of the argument in the specific overload.

Rest Parameters (&rest)

Rest parameters are handled by runtime list construction:

• In the public overloads, any arguments provided beyond the required/optional count are bundled into a linked list using Lisp.List/ListCell.
• The _Body method receives this list as a single object argument.

Keyword Parameters (&key)

Keyword parameters are implemented as a transformation over the &rest mechanism:

1. Trailing arguments are gathered into a "rest" list.
2. The _Body method defines local variables for every keyword parameter, initialized to their Lisp default values.
3. The Keyword Loop: At the start of the function, the engine emits a loop that scans the rest list in pairs. It uses Object.Equals to compare each key against the pre-interned keyword symbols (e.g., :TEST). When a match is found, the corresponding local variable is updated with the value following the key.

3. The Calling Mechanism

Direct Calls

When the compiler identifies a call to a known defun in the same assembly, it emits a direct call to the public overload that exactly matches the number of provided arguments. This provides near-native performance for fixed-arity calls.

Indirect Calls (Closures & Funcall)

All Lisp functions are instances of the Lisp.Closure class. This class provides virtual Invoke methods. When a closure is created, it captures its environment and provides overrides for these Invoke methods that jump into the appropriate Program static overloads.

4. Multiple Return Values

Note: Argument passing is only half the story; return values use a side-channel.

Because .NET methods return only one value, CLRHack uses a Thread-Static Side-Channel in the Lisp.Values class:

• The first value is returned normally as the method's return value.
• Additional values are stored in [ThreadStatic] fields (Value1, Value2, ... up to Value63).
• A ReturnCount field is updated to tell the caller how many values are waiting in the buffer.

Example CIL Generation

; Lisp: (defun add-optional (x &optional (y 5)) (+ x y))

; Overload for 1 arg
.method public static object 'ADD-OPTIONAL'(object x) {
    ldarg 0
    ldsfld object [LispBase]Lisp.Undefined::Value
    tail. call object Program::'ADD-OPTIONAL_Body'(object, object)
    ret
}

; The Body Method
.method private static object 'ADD-OPTIONAL_Body'(object x, object y) {
    ; Defaulting logic for Y
    ldarg 1
    ldsfld object [LispBase]Lisp.Undefined::Value
    bne.un SKIP_DEFAULT
    ldc.i4 5
    box int32
    starg 1
SKIP_DEFAULT:
    ; ... rest of function ...
}