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Wtf

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noobnooc
wtf

Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

Overview

Publishernoobnooc
Repositoryagent
Skill namewtf
Stars
1.4K
Forks
129
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 noobnooc on GitHub. Read the source before you install it.

Installation

Install the Wtf 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/noobnooc/agent.git /tmp/agent
mkdir -p .claude/skills
cp -r /tmp/agent/skills/wtf .claude/skills/wtf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wtf 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 Wtf 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 Wtf 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.

WTF

Use this skill as a hard-nosed pre-launch or pre-commit audit for fast-moving "vibe coded" projects. The goal is to find concrete blockers before code is shipped, not to produce a generic best-practices essay.

Operating Mode

  • Inspect the actual repository before judging it. Start with git status --short, project docs, file tree, package manifests, framework config, CI config, and deployment config.
  • Keep the audit scoped to the user's target: current branch, staged changes, a PR diff, or the whole project. If unclear, default to the current worktree plus files likely to affect deploy/runtime.
  • Prefer evidence over guesses. Tie every finding to a file, command output, or missing expected artifact.
  • Do not print secret values. If a secret is committed or exposed, name the file and variable/key shape, but redact the value.
  • If the user asks to fix issues, implement the fixes after the audit and verify them. Otherwise, remain in review/audit mode.

Audit Workflow

  1. Map the project

    • Identify app type, framework, package manager, runtime, deployment target, database, ORM, auth provider, and build/test commands.
    • Check whether the root is clean or dirty. Preserve unrelated user changes.
    • Find likely entrypoints: app routes, API routes, workers, server actions, CLI commands, cron jobs, migrations, schemas, and config files.
  2. Check repository hygiene

    • Look for committed .env* files other than safe examples, local database files, logs, cache directories, build outputs, generated artifacts, coverage, temporary uploads, screenshots, and tool caches.
    • Verify .gitignore covers framework/runtime artifacts such as .next, dist, build, .turbo, .vercel, .wrangler, .parcel-cache, coverage, node_modules, local SQLite files, logs, and upload/cache folders.
    • Use git ls-files to distinguish ignored local junk from files already tracked by Git.
  3. Check secrets and environment variables

    • Search for tokens, private keys, API keys, JWT secrets, database URLs, webhook secrets, cloud credentials, and hardcoded production URLs.
    • Verify required env vars are documented in .env.example, deployment docs, or typed config. Flag required envs that are used in code but undocumented.
    • Check for accidental logging of secrets, auth headers, cookies, session payloads, or provider responses.
  4. Check database readiness

    • Identify whether the app uses Prisma, Drizzle, TypeORM, Sequelize, Rails migrations, Django migrations, Alembic, Knex, raw SQL, or another migration system.
    • Confirm schema changes have matching migrations and that migrations are committed.
    • Flag schema drift, missing deploy migration commands, destructive migrations without a backfill/rollback plan, seed data required in production, and raw SQL that is not parameterized.
    • If a database-backed app has no ORM or migration tool, call that out as a launch risk unless the repository has a clear alternative migration process.
  5. Check app correctness and security basics

    • Run or inspect available lint, typecheck, test, and build scripts when practical.
    • Review auth boundaries, protected routes, admin-only actions, server/client separation, CORS, CSRF where relevant, rate limits, file upload validation, SSRF surfaces, open redirects, and unsafe eval/shell execution.
    • Check error handling and observability: production errors should not leak stack traces, secrets, or internal IDs unnecessarily.
  6. Check dead and legacy code

    • Search for unused routes, duplicate pages/components, abandoned API handlers, old feature flags, large commented blocks, stale TODO/FIXME/HACK notes, generated placeholders, console debugging, unused dependencies, and test/demo data paths.
    • Prefer repository-aware tools when available: TypeScript compiler, ESLint, depcheck, framework route manifests, import graph tools, or existing CI checks.
    • Treat dead code as lower severity unless it affects security, deploy size, routing, migrations, or user-visible behavior.
  7. Check deployment shape

    • Inspect Dockerfiles, wrangler/vercel/netlify/cloudflare config, GitHub Actions, release scripts, cron configuration, and required runtime versions.
    • Flag missing production build commands, wrong package manager commands, missing migration steps, secrets expected at build time vs runtime, cache directories mounted incorrectly, and local-only assumptions.

Useful Commands

Adapt commands to the repository; do not run broad destructive commands.

bash
git status --short
git ls-files
rg -n --hidden --glob '!node_modules' --glob '!.git' 'AKIA|BEGIN (RSA |OPENSSH |EC )?PRIVATE KEY|DATABASE_URL|JWT_SECRET|SECRET_KEY|API_KEY|ACCESS_TOKEN|REFRESH_TOKEN|WEBHOOK_SECRET|STRIPE_SECRET|OPENAI_API_KEY|ANTHROPIC_API_KEY|PASSWORD=' .
rg -n --hidden --glob '!node_modules' --glob '!.git' 'TODO|FIXME|HACK|console\\.log|debugger|ts-ignore|eslint-disable' .

For JavaScript/TypeScript projects, inspect package.json scripts first, then run only relevant existing scripts such as npm run lint, npm run typecheck, npm test, or npm run build.

Severity

  • P0 Blocker: likely secret exposure, data loss, auth bypass, production deploy failure, broken migration, or user-data corruption.
  • P1 High: strong launch risk such as missing env documentation, unsafe database access, unprotected sensitive route, failing build/test, or tracked cache/build artifacts.
  • P2 Medium: maintainability or reliability issue likely to slow future work, such as stale duplicate code, missing focused tests, weak error handling, or unused dependencies.
  • P3 Low: cleanup or polish that is useful but not launch-blocking.

Host-Specific Review Output

Detect host-specific review capabilities from active system/developer/app instructions, available tools, or local agent docs. Do not infer support from the model name alone, and do not invent pseudo-directives for a host.

  • Codex App: for findings tied to a specific file and line, emit one inline review comment directive per issue:

    text
    ::code-comment{title="[P1] Short issue title" body="Explain the concrete risk and the smallest practical fix. Redact any secret value." file="/absolute/path/to/file.ts" start=42 end=42 priority=1}

    Use absolute file paths, tight line ranges, and priority matching severity (P0/P1 = 1, P2 = 2, P3 = 3). Keep repo-level findings, missing-file findings, command failures, and residual risks in the normal findings list.

  • Claude Code: do not assume an inline review comment output directive. Use normal review findings with file:line references unless the current Claude Code environment explicitly provides a comment protocol or tool.

  • Antigravity: do not assume a portable text directive for artifact or inline comments. If the active environment exposes a native artifact/comment tool, use that tool; otherwise use normal review findings with file:line references.

  • Zed: do not assume a response-level inline comment directive. Zed may show agent edit review UI, but audit findings should stay in normal review format unless the active Zed environment explicitly provides a comment protocol or tool.

Output Format

Lead with findings, ordered by severity. For each finding include:

  • Severity and short title.
  • Evidence: file path, line, command, or missing expected file.
  • Impact: what can break or leak.
  • Fix: the smallest practical next step.

Then add:

  • Verified: commands actually run and their result.
  • Not verified: checks skipped and why.
  • Residual risk: anything the repository shape prevented you from proving.

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 Wtf AI skill do?

Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

Why use Wtf on TypingMind?

Because you install it once and use it with any model. Wtf 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 Wtf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/noobnooc/agent/tree/main/skills/wtf. 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 Wtf?

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 Wtf?

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

Is the Wtf AI skill free?

It is published on GitHub by noobnooc. 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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