REA-Reconstruction-Companion: Open-source, model-independent Agent Skill for evidence-based software reconstruction with REA

by ShadowSwaps · Claude Skill · ★ 107

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

Security audit verdict: SAFE · quality 67/100

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. Full audit →

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.

agent-skillagent-skillsai-agentai-agent-frameworkai-agentsai-agents-frameworkclaudeclaude-aiclaude-codeclaude-code-skills

Quick Facts

Stars107
Forks18
LanguageJavaScript
CategoryClaude Skill
LicenseMIT
Quality Score67/100
Last Updated2026-10-22
Created2026-10-15
Platformsclaude-code, codex, node
Est. Tokens~18k

About REA-Reconstruction-Companion

REA-Reconstruction-Companion An open-source, model-independent Agent Skill for understanding existing software and planning or building a similar implementation with REA. The companion turns reverse-engineering evidence into an actionable specification, architecture, implementation plan, and validation workflow.

Compatible Skills

These tools work well together with REA-Reconstruction-Companion for enhanced workflows:

  • cesiumjs-skills — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (62%)
  • superloopy — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (62%)

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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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