by Extraltodeus · LLM Plugin · ★ 228
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git clone https://github.com/extraltodeus/j-wash.gitTaken 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 | 228 |
| Forks | 25 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 69/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-06 |
| Created | 2026-07-13 |
| Platforms | python |
| Est. Tokens | ~16k |
J-Wash Reshape a model's identity and behavior by editing token directions, then bake those edits into a real checkpoint you can run anywhere. No training, no dataset, no fine-tuning. J-Wash is a local studio (FastAPI + React) for exploring and editing the J-space of any Hugging Face decoder LLM.
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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: