create-verification-skill

Files (7)

• LICENSE
• SKILL.md
• UPSTREAM.md
• openai.yaml · agents/
• README.md · references/feature-map-example/
• create-note.md · references/feature-map-example/
• search.md · references/feature-map-example/

SKILL.md (first lines)

# Create a verification skill

Every serious project needs a scripted way to drive the real app and prove behavior: launch it, exercise a feature the way a user would, and capture evidence. This skill generates that as a project-local skill (`.agents/skills/verify-<app>/`) tailored to the repo, then links `.claude/skills/verify-<app>` to that canonical directory. You write the generator's output for the next agent, not for a human: it will be read cold, mid-task, by an agent that has never seen the app.

## 1. Interview the repo, not the user

Answer these from the codebase and only ask the user what you cannot observe:

- **Surface:** what does a user actually touch? A web UI, a CLI/TUI, a desktop app, an API, a mobile app, a library? A repo can have several; pick the primary one and note the rest.
- **Run:** how does the app start locally? Prefer the repo's own documented dev command (package scripts, Makefile, README quickstart). Note ports, env vars, seed data, auth.
- **Drive:** how can an agent interact with it programmatically? Existing harnesses first — Playwright/Cypress specs, expect scripts, PTY helpers, curl-able endpoints, a debug port. Only then pick a generic recipe: browser/CDP for web and Electron, a tmux/PTY harness for CLI/TUI, plain HTTP for services.
- **Observe:** what evidence can be captured? Screenshots, terminal transcripts, response bodies, logs, exit codes, DB state.
- **Isolate:** can two instances run side by side (ports, data dirs, profiles)? If not, say so in the generated skill: refusing to double-drive a shared instance beats corrupting the user's session.

If the checkout doesn't build or start as-is, fix that first (or report it precisely) before generating; a skill written against a broken base teaches wrong steps. When an irrelevant missing asset blocks startup (a static dir the API never serves, a sample config), the generated skill may create it, clearly marked as verification scaffolding, and remove it in cleanup.

## 2. Generate the skill

Write `.agents/skills/verify-<app>/SKILL.md` with YAML frontmatter (`name: verify-<app>` and a `description` that names the app, the surface, and when to reach for it — without a description, agents do not advertise the skill to the model) and these sections, each grounded in what the interview actually found (no placeholders left). Create `.claude/skills/verify-<app>` as a relative symlink to `../../.agents/skills/verify-<app>` so both runtimes use the same project-local skill; never maintain a second copy.

- **Launch:** the exact command that starts the app for verification, and how to tell it's ready (a log line, a prompt). Launch must reach a usable state from an empty data dir, including any migrations and seed data. Ready means usable, not just that a process answers. A database health check can pass while its init scripts are still running. Include teardown. For a short-lived CLI or TUI there is no server to keep alive: launch means build the binary (or install deps) once, then start each drive in its own isolated PTY or tmux session.
- **Doctor:** one read-only check that answers "is this instance worth driving?" — process up, right version/build, port owned by us, auth valid. An agent runs this first whenever anything looks off.
- **Drive:** the harness recipe with real selectors/commands from this repo, not examples. Prefer stable handles (ARIA labels, data attributes, prompt strings, route paths) over coordinates and tab order.
- **Evidence:** what to capture for a proof and where it goes. State the proof standards: exercise the real user path, not internal setters or test-only endpoints; capture the action and the resulting state, not just the final screen; verify side effects (files written, rows inserted, messages sent) alongside what's visible; mocks only where a production boundary already isolates the external system. When the safe path is a dry-run or test mode, verify what it actually skips by observing (files, network, git refs) rather than trusting its name: some dry-runs still touch the network or open a browser.
- **Cleanup:** how to tear down instances the run created. Never kill by process name; kill what you started. Cleanup removes instances and scratch state, never the evidence: proof artifacts survive the teardown, in a location the skill names.
- **Helpers:** any script the skill ships is executable and its invocation is shown in the skill body. A helper the reader has to reverse-engineer is not a helper.

## 3. Seed the feature map

Create `.agents/skills/verify-<app>/features/README.md` plus one file per user-facing feature you can identify (aim for the top 3-5 to start, from routes, commands, menus, or docs). Follow the shape in [`references/feature-map-example/`](references/feature-map-example/), with a README index and one file per feature. Each file answers, from the user's point of view: what the feature is, how to reach it, how to drive it with the harness, and what observable end state proves it works. The four H2s are `Sub-features`, `How to get to it (user POV)`, `Driving it with <harness>`, and `Gotchas`. If the seed data lacks a precondition that a journey needs, the journey reports a failure and the reason goes in that feature's `Gotchas`. Never weaken the journey to make it pass. The map is the repo's maintained verification source; a proof that drives one convenient entry point is incomplete when the map lists others.

## 4. Prove the generated skill before handing it over

Run its own instructions end to end once: launch, doctor, drive ONE mapped feature (one is enough; the map exists so later runs can cover the rest), capture evidence, clean up. After cleanup, confirm the evidence still exists at the named location — a cleanup that eats the proof fails this step. Fix what fails, and run the generated cleanup after every failed iteration too, so broken attempts don't strand processes and ports. A generated skill that was never executed is a draft, not a deliverable. If you proved it in a stand-in for the real target (a container in place of a fresh VM, a local stack in place of a remote one), list every setup step the stand-in did that the target will not. Those steps stay unverified until a run on the real target covers them.

## 5. Offer the maintenance loop

To keep the map honest as the app changes, offer to call the Skill tool with "maintain-verification-skill". Suggest a cadence only if they ask.

From VanTanev/skills, MIT.

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