by JunsW · Codex Skill · ★ 107
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx skills add JunsW/feature-track
/plugin marketplace add JunsW/feature-trackTaken from the project's README on GitHub.
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.
| Stars | 107 |
| Forks | 2 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 56/100 |
| Last Updated | 2026-07-16 |
| Created | 2026-07-06 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~3k |
Feature Track Lightweight, repo-native shared memory for AI coding agents. Feature Track keeps Codex, Claude Code, and other AI coding tools aligned on each long-lived feature: its status, source-of-truth docs, decisions, risks, and recent changes. Spec frameworks help agents plan the next change. Feature Track helps agents remember the feature.
These tools work well together with feature-track for enhanced workflows:
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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: