no-no-debug: Self-evolution system for AI coding assistants

by summerliuuu · Agent Tool · ★ 151

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentclaudeclaude-codedebugerror-trackingself-evolutionself-improvementskill

Quick Facts

Stars151
Forks7
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score70/100
Last Updated2026-05-07
Created2026-04-10
Platformsclaude-code, python
Est. Tokens~4k

About no-no-debug

no-no-debug A self-evolution system for AI coding assistants v1.3.0 — periodic review now actually works. Earlier versions promised "auto-triggers every 3 days" but had no enforcement mechanism — the review only ran when manually invoked, and the tracker went stale silently. v1.3.0 ships , a hook that checks the tracker on each message and reminds the AI to run the review when it's overdue.

Compatible Skills

These tools work well together with no-no-debug for enhanced workflows:

  • altk-evolve — semantic(0.27)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • sibyl-research-system — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • AutoResearch-SibylSystem — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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