by tamaratran · Agent Tool · ★ 7.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Featured in: TypeSafe Jev
npm install fast-jev-compaction
claude plugin install fast-jev-compaction@fast-jev-compactionTaken from the project's README on GitHub. Check the security audit before running it.
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.
One of 11 tools we ran end to end on the same task for our TypeSafe Jev page (2026-10-11).
Our pick (To cut what Claude Code spends on compaction): It ran in one Jev request (under half a second) and returned keep-or-drop decisions for each tool call. The README's example is cut short, so expect to write a few more messages before Jev is asked anything.
Result: its quick start ran on a Jev key alone · 4 steps beyond the README · 52 s to install and run
| Stars | 7,840 |
| Forks | 578 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 72/100 |
| Open Issues | 124 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-17 |
| Platforms | claude-code, node |
| Est. Tokens | ~16k |
fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one fast request, stale ones are dropped or truncated, everything kept stays verbatim. Also usable as an npm library. What and why Most context compaction asks an LLM to summarize old turns.
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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.
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