test-harness

Imported from zernie/vigiles (skills/test-harness/SKILL.md) via skills.sh. Install upstream with npx skills add zernie/vigiles --skill test-harness. Copyright stays with the author.

Test the Claude Code harness — the hooks, skills, settings, and CLAUDE.md that steer an agent — as the assembled machine it ships as. vigiles gives three tiers, cheapest first; this skill picks the right one, writes the test, and runs it.

The guiding rule: start at the cheapest tier that can answer the question, and climb only when it genuinely can't. Two of the three tiers need no model and no API key, so they run on every commit for free — reach for the paid real-model tier only when the question actually requires a real model.

Step 0 — Pick the tier (the judgment call)

Match what you're testing to the cheapest tier that can answer it:

Most harness questions — block/allow, wired-in, context-landed — never need a model. Only "does the model trigger / behave differently" needs the eval tier.

⚠️ A trigger-rate of 0% on EVERY prompt is a wiring bug until proven otherwise. It reads like a verdict on the description, and three separate setup mistakes produce it: a bare id in fired where the namespaced <plugin>:<skill> is required; pluginDir where a loose .claude/skills needs skillsDir; and a missing fixture, since a run starts in an empty directory and a prompt about a file that isn't there is one the model is right to decline. Rule all three out before reporting it. (A partial rate is a real number — don't second-guess it.)

Don't tune against a cheaper model until you've checked it's actually a floor. compareContainment(weak, strong) answers that: it reports prompts that fired on the weak model but NOT the strong one, and each one means the weak model is not a lower bound but a different router. Prompts that fired only on the strong model are expected and are not a failure. Measured once (21 skills, 84 prompts, haiku vs sonnet): 3 weak-only, and one skill higher on haiku — so containment is not established, which is why the floor stays.

If the unit and deterministic tiers can both answer it, prefer unit: it's faster and reaches events the deterministic mock can't drive.

Step 0.4 — Observing a run, and what it costs

Two questions have their own references — open the one you need, don't guess:

• "What did the run actually DO?" — which tools it called, whether it stayed inside its declared allowed-tools, what it wrote, how to record a call without executing it → references/observing-a-run.md
• "Is this free, sub-priced, or does it need a container?" — the three buckets, and what to tell the user after a paid run → references/cost-and-expectations.md

Never say "we'll test it" without settling the second one first.

Step 1 — Ensure vigiles is installed

Check whether vigiles is a dependency (package.json), and install it as a dev dependency if not:

npm i -D vigiles    # or: pnpm add -D vigiles / yarn add -D vigiles

The deterministic tier additionally needs the claude CLI on PATH (no API key): npm i -g @anthropic-ai/claude-code. The eval tier needs model auth. If the claude CLI is missing, you can still write and run unit-tier tests.

Step 2 — Locate the harness surface to test

Find what the project actually ships, in this order:

1. .claude/settings.json / .claude/settings.local.json — inline hooks.
2. .claude-plugin/plugin.json — a plugin manifest (hooks, skills, agents, mcpServers).
3. hooks/hooks.json — the plugin hooks convention (e.g. obra/superpowers).
4. skills/<name>/SKILL.md, agents/<name>.md, commands/<name>.md.

Pick one concrete thing to pin down — a specific PreToolUse hook, a specific SessionStart injection, a specific skill.

Step 3 — Write the test for the chosen tier

Per-tier skeletons, and the one mistake that silently swallows failures (a hand-rolled runner eats stderr) → references/writing-tests.md

Read it before writing the file — the skeleton differs per tier, and the runner warning has cost real debugging time.

Step 4 — Run it

In a runner (node:test / vitest / jest) the tests are plain async functions. Or use the zero-setup CLI, which discovers and runs the files:

npx vigiles test                 # *.harness.{mjs,ts} — unit + deterministic, no API key
npx vigiles eval --trials=6      # *.eval.{mjs,ts} — real model (local / nightly, not CI)

Unit-tier runHook tests need no claude and always run — write and run them even with no claude installed. A tier that genuinely can't run reports a loud ⊘ SKIPPED (tallied separately, never a fake ✓); a standalone script emits one via skip(reason) from vigiles. A skip passes by default, but in a CI job that asserts the capability is present, run vigiles test --no-skip so a skipped tier fails — a green-with-skips is untested surface. Keep unit + deterministic tests in CI (free); run evals locally or on a schedule with auth.

CI — don't hand-write the steps

These tiers belong in CI, and there is a published Action for it. Run vigiles init: it writes .github/workflows/vigiles.yml, wiring the Action (zernie/vigiles@v1) for the jobs that can use it plus a plain npx vigiles test job for this tier — that one needs repo-local node_modules, which the Action does not install, so it stays hand-rolled on purpose.

If the repo already has a workflow, the Action's inputs are documented in docs/github-action.md. Read them there rather than guessing: the input list is defined in action.yml, and a copy of it here would be a second source of truth that goes stale without anything noticing — which is exactly what happened to this file's own sibling docs and to a consumer's CI comment, both measured on 2026-08-18.

Step 5 — Lock the eval so CI stays honest (you do this automatically)

Real-model evals run on the user's subscription — locally, never in CI. So as part of writing an eval, you keep its result fresh for them. Do these two things without being asked:

1. Give every eval a name. That's what the lock keys on.

2. After running it, commit the lock:

   vigiles eval --update      # records the result → .vigiles/eval-locks/<name>.lock.json

   Then commit that file. CI runs vigiles eval --check (no model) to verify it still matches the inputs — so a later edit that forgets to re-eval fails loud instead of shipping stale numbers.

When you later change a skill's description or prompts, re-run vigiles eval --update and commit the updated lock — the change altered what the eval measures. (vigiles also nudges you: when a lock exists, a SKILL.md edit triggers a non-blocking reminder.)

Why it's cheap: --check only hashes inputs (skill text, prompts, model). A threshold change in the test re-uses the saved numbers (no model); only an input change needs a fresh --update. Full mechanics: docs/harness-testing.md.

When the user didn't say what to test

Don't ask them to specify — pick something real and demonstrate. Scan the harness surface (Step 2), choose the cheapest meaningful test, write it, run it, and show the result. Good default picks, in order:

1. A PreToolUse hook → unit-test that it blocks the thing it's meant to block (and allows a safe sibling).
2. A SessionStart hook that injects context → deterministic test that the text actually reaches the model (assertRequestContains).
3. A skill → deterministic test that it resolves via pluginDir, then offer the paid measureTriggerRate eval as a follow-up.

Then say which tier you used and why, and offer to climb a tier if the cheaper test can't fully answer their question.

Reference

The full guide — every tier, testing skills for real, "fired ≠ landed", the safe-by-default sandbox, the coverage matrix, and how it compares to promptfoo — is in docs/harness-testing.md.