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KyaniteLabs/checkyourself

by Various

Local-first production-readiness system for AI-built apps: read-only audit, evidence-based 0-100 score, guided fixes, learning plan, dashboard, CLI, and MCP.

K

MCP

KyaniteLabs/checkyourself

Added 7 June 2026

#ai #ai-agents #ai-coding-assistant #ai-tools #claude #cli #code-audit #code-review

Overview

A local-first tool that performs read-only audits of applications built with AI. It produces an evidence-based 0-100 production readiness score and provides guided fixes along with a learning plan. Includes a dashboard, CLI, and MCP for workflow integration.

Best for

Best for
Developers building apps with AI who need a private, local assessment of production readiness with concrete improvement steps.

Use cases

  • Auditing an AI-built app before production deployment
  • Generating a production readiness score with actionable evidence
  • Receiving guided fixes and a learning plan from audit results

Notes

A local-first tool that performs read-only audits of applications built with AI. It produces an evidence-based 0-100 production readiness score and provides guided fixes along with a learning plan. Includes a dashboard, CLI, and MCP for workflow integration.

1 stars on GitHub. Last updated 2026-06-07. Licensed Apache-2.0.

Use cases

  • Auditing an AI-built app before production deployment
  • Generating a production readiness score with actionable evidence
  • Receiving guided fixes and a learning plan from audit results

Pros

  • Local-first design keeps your code and data private
  • Read-only audit ensures no accidental modifications
  • Guided fixes and learning plan make results actionable

Cons

  • Very early-stage project with minimal community adoption (1 star)
  • Limited to Python runtime environment
  • Niche focus on AI-built apps may not suit general projects

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Local-first design keeps your code and data private
  • Read-only audit ensures no accidental modifications
  • Guided fixes and learning plan make results actionable

Cons

  • Very early-stage project with minimal community adoption (1 star)
  • Limited to Python runtime environment
  • Niche focus on AI-built apps may not suit general projects