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Verifier Setup

OrganizationPopular
AI-Builder-Club
verifier-setup

Set a repo up to prove engineering-task work actually works before it ships. Investigates the repo, ensures a one-command dev stack (`dev-local`) exists, asks whether verification runs locally or in a sandbox (crabbox), confirms/installs the driver (the `playwright-cli` skill for web by default). Outputs three artifacts: a committed `/verify` skill (per-task verification SOP — spawn a verifier sub-agent → drive the app → screenshot/video proof → open a PR with the proof embedded), the `/dev-local` skill + script, and the installed driver skill. Use when someone says "set up verification", "make this repo verifiable", "scaffold a verify skill", "set up the verifier".

Overview

PublisherAI-Builder-Club
Repositoryskills
Skill nameverifier-setup
Stars
1.3K
Forks
159
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by AI-Builder-Club on GitHub. Read the source before you install it.

Installation

Install the Verifier Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AI-Builder-Club/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/verifier-setup .claude/skills/verifier-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verifier Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Verifier Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Verifier Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

verifier-setup — scaffold this repo's /verify skill

Goal: leave the repo able to prove an engineering task works before it ships — run once, and it wires up everything the per-task /verify loop needs.

You are setting up — not verifying anything yourself right now. The /verify template lives at assets/verify.template.md (next to this skill). Parallels dev-local-setup (which generates a script + its skill doc): a setup skill that leaves behind reusable, repo-specific artifacts.

What this produces (the outputs)

Running verifier-setup end-to-end leaves the repo with:

  1. A /verify skill.claude/skills/verify/SKILL.md, the repo-tailored per-task verification SOP (spawn a verifier sub-agent → drive the app → screenshot/video proof → open a PR with the proof embedded). Generated in Step 5.
  2. A /dev-local skill + its scriptscripts/dev-local.sh and .claude/skills/dev-local/SKILL.md, via dev-local-setup (Step 2) if not already present. The one-command stack /verify depends on.
  3. The driver skill installed — the playwright-cli skill for web apps (Step 2); for non-web, the concrete exercise tool confirmed present.

Step 0 — Inventory what already exists (check before you add ANYTHING)

Before creating anything, take stock — the repo may already have some of this, under whatever name or layout its team chose. Look for the capability, not a specific filename; the paths below are only examples. For each, decide reuse as-is / adapt-extend / create fresh — never blindly overwrite working setup:

  • A way to start the app — a one-command dev launcher, a Makefile/Procfile target, docker-compose, package scripts (e.g. scripts/dev-local.sh, but any form counts).
  • A prior verification SOP/skill — from an earlier run of this skill or the team's own convention.
  • A driver for the app's interface — a browser automation tool already available (e.g. the playwright-cli skill), or the relevant API/CLI client.
  • An existing test/e2e suite or checks — however organized.
  • Sandbox/cloud-box config — anything giving isolated per-agent stacks.
  • An evidence/artifact convention — where proof lands and how a reviewable link gets published (a release, bucket, CI artifacts, etc.).

Every later step is conditional on this inventory: if a capability exists and works, reuse and adapt it (fill gaps, don't regenerate); only create what's missing.

Step 1 — Investigate the repo (don't guess)

Discover the real facts the generated skill will hardcode:

  1. How the app is exercised — is it a web app (has a browser UI + a dev server on a port), an API/service (HTTP endpoints, no UI), a CLI, or a desktop/mobile app? This picks the driver.
  2. Stack launcher — is there already a way to start the app (any form — see Step 0)? Note the up-command and the app URL/port. If none, Step 2 handles it.
  3. Auth — is the primary flow login-gated? Is there a session/auth helper the verifier can mint a session with (see e2e-setup)? Record it, or "n/a".
  4. Regression checks — the repo's fast codified checks (type-check, lint, unit, existing e2e commands) from package.json/Makefile/turbo.json/etc.
  5. Proof upload — how a reviewable video URL is produced (a pr-evidence GitHub prerelease via gh release upload is the default; a bucket/CI artifact works too).

Step 2 — Ensure the prerequisites exist (reuse-or-provide, per the Step 0 inventory)

For each, act on what Step 0 found — reuse if present, adapt if partial, create only if missing. Each check is idempotent; a no-op on what's already there:

  • Dev stack. If any working way to start the app already exists (a launcher script, Make/Procfile target, compose, package scripts), reuse it — read it for the up-command/port/services and move on (extend only if a needed service is missing). If there's none, scaffold one via dev-local-setup (don't hand-roll a launcher here). The generated /verify just needs a reliable one-command up.
  • Driver skill.
    • Web → install/confirm the playwright-cli skill (it documents + wraps the browser driver). Ensure its binary is callable too (npx --yes @playwright/cli --version; install it + the chrome channel if missing). This closes the usual local gap where the browser driver was assumed but never installed.
    • Non-web → confirm the concrete exercise tool exists (an HTTP client for an API, the built binary for a CLI). No browser skill needed.
  • Evidence dir. Ensure evidence/ is gitignored (proof output lands there).

Step 3 — Ask the user: local or sandbox?

Present the choice (default and recommend local — it's simpler to stand up):

  • Local — one dev stack on the machine (scripts/dev-local.sh up). Best for a single task at a time. Recommend this unless they need parallelism.
  • Sandbox (crabbox) — an isolated cloud box per agent, for concurrent loops or a fixed-port/single-instance stack. If chosen and not yet set up, scaffold via crabbox-setup; the generated skill drives the app in-box via cbx.sh pw.

Record the pick as the generated skill's default RUN_MODE (the other stays a documented fallback).

Step 4 — Confirm the driver

State the detected driver and confirm with the user (default playwright-cli for web apps). For non-web, name the concrete tool (e.g. curl/an HTTP script, the built CLI). This becomes DRIVER in the generated skill.

Step 5 — Generate .claude/skills/verify/SKILL.md

If a prior verification skill/SOP already exists (Step 0), update it in place — refresh the repo-specifics/placeholders, preserve any hand-edits the team added; don't clobber. Otherwise copy assets/verify.template.md → the repo's skills dir (.claude/skills/verify/ or the repo's convention) and fill every {{...}} placeholder from Steps 1–4: STACK_UP, APP_URL, RUN_MODE(+RUN_MODE_NOTE), DRIVER(+DRIVER_INSTRUCTION), AUTH_HELPER(+AUTH_INSTRUCTION), EXERCISE, REGRESSION_CMDS, EVIDENCE_UPLOAD, DATE. Delete branches that don't apply (e.g. drop the browser/video language for a non-web repo). Keep it to one screen — it's an SOP the agent follows, not an essay.

Step 6 — Hand off

Commit the generated skill (+ any .gitignore/dev-local changes). Tell the user:

  • how to run it — "/verify before opening a PR (or when asked to verify), on a branch with changes committed";
  • the run mode chosen and how to switch;
  • any prerequisite they must install/start once (e.g. Docker for infra, gh auth for evidence upload).

Principles

  • Check before you create; adapt, never clobber. Every artifact — dev-local, the driver skill, e2e, evidence gitignore, the verify skill itself — is inventoried first (Step 0) and reused/extended when it already exists. Only create what's genuinely missing; preserve working setup and team hand-edits.
  • Discover, don't assume. Stack command, port, auth, and checks come from the repo — the generated skill hardcodes real facts, not conventions.
  • Provision before you generate. The driver, launcher, and evidence/ exist before the /verify skill ships, so it never fails on a missing tool.
  • Right-sized. A web monorepo gets the full browser+video SOP; a CLI tool gets a stdout-assertion SOP. Match the template to the repo; cut what doesn't apply.
  • The output is a skill, not a run. verifier-setup scaffolds; /verify runs.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Verifier Setup AI skill do?

Set a repo up to prove engineering-task work actually works before it ships. Investigates the repo, ensures a one-command dev stack (`dev-local`) exists, asks whether verification runs locally or in a sandbox (crabbox), confirms/installs the driver (the `playwright-cli` skill for web by default). Outputs three artifacts: a committed `/verify` skill (per-task verification SOP — spawn a verifier sub-agent → drive the app → screenshot/video proof → open a PR with the proof embedded), the `/dev-local` skill + script, and the installed driver skill. Use when someone says "set up verification", "...

Why use Verifier Setup on TypingMind?

Because you install it once and use it with any model. Verifier Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Verifier Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AI-Builder-Club/skills/tree/main/skills/verifier-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Verifier Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Verifier Setup?

As many as you like. As long as a model supports skills, you can use Verifier Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Verifier Setup AI skill free?

It is published on GitHub by AI-Builder-Club. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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