pbakaus_impeccable/tests/skill-behavior

Skill-behavior tests

LLM-backed scenarios that verify how the impeccable skill drives PRODUCT.md / DESIGN.md loading. Each scenario runs against the cheapest tier of each major provider (Anthropic, OpenAI, Google) so a full sweep costs a few cents and finishes in ~2 minutes.

These are the tests you re-run when you refactor anything in SKILL.md's ## Setup section. They fail when the agent stops following the loading contract.

Run

bun run test:skill-behavior
IMPECCABLE_SKILL_BEHAVIOR_VERBOSE=1 bun run test:skill-behavior   # dump per-scenario traces
IMPECCABLE_SKILL_BEHAVIOR_MODELS=claude-sonnet-4-6 bun run test:skill-behavior   # scope to one model

Requires .env at repo root with at least one of ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_CLOUD_API_KEY. Providers without a key are skipped, not failed.

How it works

Each scenario:

1. prepareWorkspace() mints a temp dir, symlinks the canonical skill into <workspace>/.claude/skills/impeccable, and optionally writes PRODUCT.md / DESIGN.md fixtures.
2. runTurn() inlines SKILL.md (placeholders neutralized) as the system prompt and runs Vercel AI SDK generateText with four workspace-scoped tools: bash, read, write, list.
3. The tools record every call into a trace that the test asserts on.
4. For scenario 4, a second runTurn reuses turn 1's responseMessages so the model sees a real multi-turn conversation.

The trace is the source of truth, not the model's free-form reply.

Scenarios

#	Setup	Assertion
1	empty workspace	runs context.mjs (which prints a NO_PRODUCT_MD directive); agent then loads reference/init.md via Read or cat; does not start writing HTML/CSS
2	PRODUCT.md only (with ## Register: brand)	runs context.mjs 1-3 times; loads reference/brand.md
3	PRODUCT.md + DESIGN.md (brand register)	runs context.mjs 1-3 times; loads reference/brand.md; consults the design system (DESIGN.md bundled in output, but CSS / tokens / directory listing also count)
4	PRODUCT.md + DESIGN.md, context already loaded in turn 1	turn 2 does not re-run context.mjs; reference/brand.md is loaded across turns 1+2
5	PRODUCT.md WITHOUT a ## Register field; task cue says "landing page"	runs context.mjs (which emits a generic register directive); agent loads reference/brand.md via task-cue cascade
6	PRODUCT.md + DESIGN.md + a minimal index.html; prompt is /impeccable polish	loads reference/polish.md
7	same fixture; prompt is /impeccable audit	loads reference/audit.md
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
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)
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
11	empty workspace; prompt is /impeccable shape ...	runs context.mjs, diverts into reference/init.md, and does not start writing HTML/CSS
12	empty workspace; prompt is natural-language build intent with no command word	runs context.mjs, diverts into reference/init.md, and does not start writing HTML/CSS
13	empty workspace; prompt is /impeccable teach	runs context.mjs and diverts into reference/init.md because teach aliases init

Scenario 9 passed on all three current-lineup providers (claude-sonnet-4-6, gpt-5.5, gemini-3.1-flash-lite) on 2026-05-28.

Baseline state (2026-05-20, previous cheap tier)

Lineup changed. The default models are now claude-sonnet-4-6, gpt-5.5, and gemini-3.1-flash-lite (production-tier on Anthropic and OpenAI). The table below was measured on the old cheap tier (claude-haiku-4-5 / gpt-5.4-mini) and is kept as the historical record. Re-measure on the current lineup and update this section; the stronger models are expected to clear the scenario 6/7 routing failures that the old gpt tier showed.

Captured after moving sub-command reference loading from step 4 to step 2 of Setup (so the agent loads reference/<command>.md right after context.mjs, before "doing the work" preempts it), and tightening step 3 to require at least one project code read even when a sub-command reference loads first. Use this table when comparing pre/post refactor: a regression is "more failures than baseline", not "any failures at all".

Scenario	claude-haiku-4-5	gpt-5.4-mini	gemini-3.1-flash-lite
1 (no context)	pass (rare flake — agent stops after context.mjs without loading init.md)	pass	pass
2 (product only)	pass	pass	pass
3 (product + design)	pass	pass	pass (rare flake — sub-command ref loads but register ref doesn't)
4 (already loaded)	pass	pass	pass
5 (no register field, task-cue cascade)	pass	pass	pass
6 (polish routing)	pass	fail	pass
7 (audit routing)	pass	fail	pass
8 (existing project, explore design system)	pass	pass	pass

21-22 / 24 typical. The stable failures are gpt-5.4-mini scenarios 6 and 7: the model reads index.html (the target file), recognizes "polish" or "audit" as a familiar action, and proceeds with the work without ever loading the sub-command reference. Stronger SKILL.md wording (MUST, "non-optional", reordered earlier) didn't move it; this looks like a model-floor behavior rather than a skill ambiguity. Claude and Gemini honor the load.