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The earlier fix in this branch was built on a wrong diagnosis. It assumed a
structured question hides any prose sharing its message, so it split report and
question across two turns. A controlled check showed prose before a question
renders fine; what hides a report is emitting it AFTER the question. The split
therefore fixed nothing and introduced a worse failure: a turn that ends on the
report is a turn that ends, and the questions never arrived at all.
Persistence returns to main's ordering, byte for byte, and the boundary prose is
gone. What replaces it is a position rule: the question is the last thing in the
response.
The trace test added here found two failures beyond the reported one. Critique
can fail to land in three ways, and they are now all asserted:
1. Question emitted before the report, hiding it behind the picker.
2. No close at all: no questions and no skip line, so polish inherits nothing.
3. Report authored into the persistence heredoc and never written to chat,
leaving a perfect snapshot and a user who sees nothing.
Mode 3 predates this branch entirely. Persistence step 1 now says the temp file
is an archive copy, not delivery.
The Codex final-question gate is promoted out of its <codex> fence, where it was
stripped for three of four providers, and the skip branch is now a countable
threshold (fewer than 3 Priority Issues) rather than a judgment call.
Known floor, recorded in the suite README: gpt-5.6-luna passes 1 run in 6 and
deepseek-v4-flash is flaky. claude-sonnet-5 and gemini-3.5-flash are consistent.
Prepared with AI assistance (Claude Code).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
194 lines
12 KiB
Markdown
194 lines
12 KiB
Markdown
# Skill-behavior tests
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LLM-backed scenarios that verify how the impeccable skill drives context,
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command-reference, new-work, and native-platform loading. Each scenario runs
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against one current model from each supported provider (Anthropic, OpenAI,
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Google, DeepSeek).
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These are the tests you re-run when you refactor anything in SKILL.md's
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`## Setup` section. They fail when the agent stops following the loading
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contract.
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## Run
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```bash
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bun run test:skill-behavior
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IMPECCABLE_SKILL_BEHAVIOR_VERBOSE=1 bun run test:skill-behavior # dump per-scenario traces
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IMPECCABLE_SKILL_BEHAVIOR_MODELS=claude-sonnet-5 bun run test:skill-behavior # scope to one model
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```
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Requires `.env` at repo root with at least one of `ANTHROPIC_API_KEY`,
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`OPENAI_API_KEY`, `GOOGLE_CLOUD_API_KEY`, `DEEPSEEK_API_KEY`. Providers without a key are
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skipped, not failed.
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## How it works
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Each scenario:
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1. `prepareWorkspace()` mints a temp dir, symlinks the canonical skill
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into `<workspace>/.claude/skills/impeccable`, and optionally writes
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`PRODUCT.md` / `DESIGN.md` fixtures.
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2. `runTurn()` inlines `SKILL.md` (placeholders neutralized) as the
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system prompt and runs Vercel AI SDK `generateText` with four
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workspace-scoped tools: `bash`, `read`, `write`, `list`, and a fake
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provider-neutral `ask_user_question` backed by a deterministic simulated user.
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3. The tools record every call into a `trace` that the test asserts on.
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4. For scenario 4, a second `runTurn` reuses turn 1's `responseMessages`
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so the model sees a real multi-turn conversation.
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The trace is the source of truth, not the model's free-form reply.
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## Scenarios
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| # | Setup | Assertion |
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| 1 | empty workspace | runs `context.mjs`; loads `reference/init.md` before implementation; automation is not an init bypass |
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| 2 | PRODUCT.md only | runs `context.mjs` 1-3 times; loads `reference/new-work.md` to resolve visual authority, establish a world when needed, and develop the surface |
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| 3 | PRODUCT.md + DESIGN.md | runs `context.mjs` 1-3 times; receives the committed design system and loads `reference/new-work.md` for the task-scoped concept |
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| 4 | PRODUCT.md + DESIGN.md, context already loaded in turn 1 | turn 2 does **not** re-run `context.mjs` |
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| 5 | PRODUCT.md without the legacy `## Register` field and no DESIGN.md | runs `context.mjs`; greenfield craft loads `reference/new-work.md`, not init, to establish the missing world |
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| 6 | PRODUCT.md + DESIGN.md + a minimal `index.html`; prompt is `/impeccable polish` | loads `reference/polish.md` |
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| 7 | same fixture; prompt is `/impeccable audit` | loads `reference/audit.md` |
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| 8 | PRODUCT.md + DESIGN.md + a SvelteKit scaffold (`src/app.css`, components, `+page.svelte`); prompt is `/impeccable polish src/routes/+page.svelte` | reads at least one project code file (CSS / component / page) — not just the skill's reference files |
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| 9 | PRODUCT.md + `index.html` + a seeded update cache with a newer version (`skillVersion` copy-mode so `context.mjs` has a `SKILL.md` to version-check against); prompt is `/impeccable polish index.html` | `context.mjs` runs and its output carries the `UPDATE_AVAILABLE` directive (proven via captured bash output); the agent does **not** auto-run `npx impeccable update` (it must ask first) |
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| 10 | no PRODUCT.md + a minimal `index.html`; prompt is `/impeccable polish index.html` | runs `context.mjs`, loads `reference/polish.md`, and does **not** divert into `reference/init.md` |
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| 11 | empty workspace; prompt is `/impeccable shape ...` | runs `context.mjs`; resolves `reference/init.md` before planning the surface |
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| 12 | empty workspace; prompt is natural-language build intent with no command word | runs `context.mjs`; resolves `reference/init.md` before implementation |
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| 13 | empty workspace; prompt is `/impeccable teach` | runs `context.mjs` and diverts into `reference/init.md` because `teach` aliases `init` |
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| 14 | PRODUCT.md with `## Platform: ios` (native iOS app); prompt is `/impeccable craft a tide detail screen` | `context.mjs` runs and emits the contents of `reference/ios.md` directly, placing native conventions in context without a second model-directed read |
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| 15 | same iOS fixture; prompt is `/impeccable audit` | agent loads `reference/audit.native.md` (the Commands-table native variant, routed instead of `audit.md`) |
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The workflow-contract file adds end-to-end assertions for attended fresh init,
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an initialized natural build request, replacement-world redesign, scope-preserving bolder
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refinement, and critique's closing question. It checks question order and
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context/artifact writes rather than only reference-file loading.
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`critique closes with the question or an explicit skip line` is a regression
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guard, not a routing check. A critique that prints its report and then stops,
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asking nothing and printing no `Questions skipped: <reason>` line, is an
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incomplete run: the close is half the deliverable, and `polish` downstream has
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no priorities to inherit without it. The fixture page is deliberately broken
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enough to put the report past the three-Priority-Issue threshold, so the run
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cannot reach the skip branch on merit. The assertion is deliberately loose about
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*how* the run closes, because either close is valid; what it forbids is neither.
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## Workflow-contract baseline (2026-08-13, current lineup)
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Measured on `claude-sonnet-5`, `gpt-5.6-luna`, `gemini-3.5-flash`, and
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`deepseek-v4-flash` while checking whether an `{{ask_instruction}}` rewrite had
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regressed anything. Two of the four workflow-contract scenarios fail for reasons
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that predate that change. Treat both as the known floor; a regression is a
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failure beyond these.
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| Scenario | claude-sonnet-5 | gpt-5.6-luna | gemini-3.5-flash | deepseek-v4-flash |
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| attended fresh init | not measured | not measured | not measured | not measured |
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| initialized natural build | not measured | not measured | not measured | not measured |
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| redesign replaces DESIGN | flaky | not measured | not measured | not measured |
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| bolder refinement | not measured | pass | pass | **fail** |
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| critique closes | pass (2 of 2) | **fail (1 of 6)** | pass (2 of 2) | flaky (1 of 2) |
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`not measured` means exactly that: the cell was never run in isolation on this
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lineup. Only the two failing scenarios were scoped per model, because the
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investigation was about whether a specific edit had regressed them. The rows are
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worth keeping anyway, since a scenario absent from the table is easy to mistake
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for a scenario that passed.
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**`bolder refinement`, deepseek-v4-flash.** The model runs `context.mjs`, reads
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`bolder.md`, `craft-floor.md`, and `current.html`, then ends its turn without
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editing anything: empty `writePaths`, no `ask_user_question` call, well short of
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the 16-step cap. Confirmed identical on HEAD with `bolder.md` reverted, so it is
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not a skill-text problem. Same shape as the gpt-5.4-mini scenario 6/7 failures
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below: the model consumes the references and then declines to act.
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**`critique closes`, gpt-5.6-luna.** Five runs while tuning the instruction text
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produced one pass. It fails in three distinct ways, which is why the scenario
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asserts on emission order rather than only on the presence of a question:
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1. *No close.* Report lands, no question, no skip line. The failure this
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scenario was written for.
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2. *Question before report.* The question is emitted first and the report after
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it, so the report is withheld until the user answers. Observed directly, and
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the reason the invariant is stated as a position rule ("the question is the
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LAST thing in the response") rather than as prose order.
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3. *Report never spoken.* The full report is authored into the persistence
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heredoc, archived, and never written to chat. The snapshot is perfect and the
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user sees nothing. This is why persistence step 1 says the temp file is an
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archive copy, not delivery.
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claude-sonnet-5 and gemini-3.5-flash pass consistently. deepseek-v4-flash has
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hit mode 3 once in two runs, so it is flaky here, not clean. Strengthening the
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instruction text moved luna from consistently failing to occasionally passing,
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and further prose tuning stopped paying.
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Read the counts in the table as what they are: small samples on a nondeterministic
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system, gathered while the instruction text was being changed between runs. They
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say the close is reliable on the two strongest models and unreliable on the two
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cheapest ones. They do not support a finer claim than that. Re-measure rather
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than assuming when the lineup changes.
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**`redesign replaces DESIGN`, flaky.** It has failed on two different assertions
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across runs (`designWrite > question` and `implementation > designWrite`), and on
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one run claude-sonnet-5 exhausted the 300s per-test timeout instead of asserting.
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The traces never load `document.md`; the ordering under test comes from
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`new-work.md`. Re-run before believing a single red result here. Which model
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produced which failure was not pinned down, so the row records only that the
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scenario is unstable.
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The `bolder` claude-sonnet-5 cell is unmeasured for a specific reason: the scoped
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run that produced this table used a 180s cap, which sonnet exceeded. That is a
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timeout, not a failure, and it is why the guidance below insists on 300000.
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### Scoping a run while investigating
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Both files honor `--test-name-pattern`, which is much cheaper than a full sweep
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when bisecting one scenario:
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```bash
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IMPECCABLE_QUESTION_DISABLED=1 CI=1 IMPECCABLE_SKILL_BEHAVIOR_MODELS=deepseek-v4-flash \
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node --test --test-timeout=300000 --test-force-exit \
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--test-name-pattern="bolder refinement" tests/skill-behavior/workflow-contract.test.mjs
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```
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Keep `--test-timeout` at 300000. A tighter cap turns claude-sonnet-5's slower
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runs into timeouts that look like failures. Set `IMPECCABLE_QUESTION_DISABLED=1`
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and `CI=1` so `serve-question.mjs` cannot open a browser window on the host. Pipe
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to a file rather than `tail`; node prints the failing-test summary at the end,
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and truncating it costs you the per-model attribution.
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## Baseline state (2026-05-20, previous cheap tier)
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> **Historical record.** The default models are now `claude-sonnet-5`,
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> `gpt-5.6-luna`, `gemini-3.5-flash`, and `deepseek-v4-flash`. The table below
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> was measured on an older cheap tier
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> (`claude-haiku-4-5` / `gpt-5.4-mini`) and is kept as the historical record.
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> Re-measure on the current lineup and update this section; the stronger
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> models are expected to clear the scenario 6/7 routing failures that the old
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> gpt tier showed.
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Captured after moving sub-command reference loading from step 4 to step 2
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of Setup (so the agent loads `reference/<command>.md` right after
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`context.mjs`, before "doing the work" preempts it), and tightening
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step 3 to require at least one project code read even when a sub-command
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reference loads first. Use this table when comparing pre/post refactor:
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a regression is "more failures than baseline", not "any failures at all".
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| Scenario | claude-haiku-4-5 | gpt-5.4-mini | gemini-3.1-flash-lite |
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| 1 (no context) | pass (rare flake — agent stops after `context.mjs` without loading `init.md`) | pass | pass |
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| 2 (product only) | pass | pass | pass |
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| 3 (product + design) | pass | pass | pass (rare flake — sub-command ref loads but world ref doesn't) |
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| 4 (already loaded) | pass | pass | pass |
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| 5 (no register field, task-cue cascade) | pass | pass | pass |
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| 6 (`polish` routing) | pass | **fail** | pass |
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| 7 (`audit` routing) | pass | **fail** | pass |
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| 8 (existing project, explore design system) | pass | pass | pass |
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21-22 / 24 typical. The stable failures are gpt-5.4-mini scenarios 6 and 7:
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the model reads `index.html` (the target file), recognizes "polish" or
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"audit" as a familiar action, and proceeds with the work without ever
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loading the sub-command reference. Stronger SKILL.md wording (MUST,
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"non-optional", reordered earlier) didn't move it; this looks like a
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model-floor behavior rather than a skill ambiguity. Claude and Gemini
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honor the load.
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