KyaniteLabs/checkyourself

For AI app builders, CheckYourself runs local challenges to test claimed work, record evidence, and expose risks. Score is not a guarantee.
> **Check yourself before you wreck yourself — for the apps you ship.** Before you launch it, CheckYourself.
**CheckYourself is a free, open-source, model-agnostic completion-evidence workflow for apps built with AI coding assistants.** It turns any AI assistant — Cursor, Claude, ChatGPT, Gemini, Copilot, Windsurf, Replit, Lovable, Bolt, Codex, or a local agent — into a pre-launch reviewer that inspects your app, records observed and untested behavior, runs verifier-owned challenges, surfaces production gaps, proposes fixes for your approval, verifies local receipts, and writes a learning plan built from the exact gaps your own project had.
Under the hood it is a staged engineering system, not a single canned prompt: an ICM-style context workspace that routes the agent through each stage, an evidence-based 0–100 scoring method with severity caps, verifier-owned challenges across 20 canonical surfaces and 10 scored categories, a 19-capability production-hardening engine spanning auth, data, secrets, CI/CD, observability, privacy, and AI governance, JSON output schemas, report and risk templates, and a public validation suite. You install it as your AI assistant's operating context — no SaaS, no account, no lock-in to any one model.
The 2026-09-05 retrofit checkpoint is backed by **150 tests and 88 subtests**, and python3 tools/validate_public.py . passes. These are repository checks, not a production-safety certification.
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Table of contents
- What is CheckYourself?
- Why it exists
- Get started
- What it produces
- Verifier-owned challenge runner
- What it checks
- Works with every AI coding tool
- Who it is for
- How it works
- Optional local CLI
- Optional visual dashboard
- Token efficiency by design
- Safety model
- FAQ
- License
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What is CheckYourself?
CheckYourself is an open-source **reviewable completion-evidence system** — a structured, staged engineering framework of context files, scoring logic, output schemas, templates, and production-hardening guidance. It records what was observed, what was inferred, what remains untested, and which risks still block launch; its verifier executes committed local challenges, rechecks stored executed receipts at score time, and validates report verdict consistency. It does not certify production safety or provide independent external custody of the evidence.
It answers one question that matters to every "vibe coder," indie hacker, and AI-assisted builder: **"Is this app actually ready to ship, and if not, what exactly is wrong and how do I fix it?"**
Unlike a "top three issues" linter, CheckYourself builds a **complete findings register** and a **complete remediation backlog**, produces a bounded 0–100 evidence score, and walks you through fixes one safe, reversible batch at a time. When the audit is done, it generates a **bespoke learning plan** so you actually learn from what your project was missing. The score remains scoped evidence, not a production-safety guarantee.
It is also organized as an ICM-style context workspace: CONTEXT.md routes the agent to staged folders, each major stage has its own CONTEXT.md, and durable handoff artifacts belong in stage output/ folders. CheckYourself is not affiliated with the RinDig ICM project; it uses the same file-first idea so agents know what to read, do, and produce at each step.
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Why it exists
Apps built fast with AI tools tend to look finished long before they are safe to launch. The gaps are usually invisible from the happy path: missing auth checks, unvalidated inputs, leaked secrets, no backups, no rollback, no tests, no rate limits, no error tracking.
CheckYourself gives you reality **before production does the grading** — a calm, complete, plain-English second pass that any AI assistant can run on your behalf.
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Get started
1. Download or clone this repository.
2. Put the checkyourself folder in or next to your project.
3. Point your AI coding assistant at the folder as its **operating context**. Start at CONTEXT.md — it routes the agent through each stage without loading the whole repo. New to the system? Read START_HERE.md first.
4. Run a read-only diagnostic and review the **Production Reality Report**.