Finn-loop: The Finn-loop: a 3-skill AI software factory for Claude Code — spec, build, review. Humans merge

by finna · Claude Skill · ★ 321

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

Security audit verdict: SAFE · quality 66/100

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.

agentic-workflowsai-agentsclaude-codegithublinearsoftware-factory

Quick Facts

Stars321
Forks62
LanguageJavaScript
CategoryClaude Skill
LicenseMIT
Quality Score66/100
Open Issues4
Last Updated2026-07-23
Created2026-07-22
Platformsclaude-code, node
Est. Tokens~6k

About Finn-loop

Finn-loop Three Claude Code skills that turn Linear + GitHub into a small, human-gated AI software factory: idea → interviews you and files the issue → you label it → claims it and opens a PR → posts a verdict → you merge. Three skills, one approval label, one rule: humans merge.

Compatible Skills

These tools work well together with Finn-loop for enhanced workflows:

  • linear-mcp-server — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • awesome-agv — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)
  • session-orchestrator — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • cc-plugin-codex — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)

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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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