by zengxiao-he · LLM Plugin · ★ 561
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git clone https://github.com/zengxiao-he/tessera && cd tesseraTaken 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.
| Stars | 561 |
| Forks | 8 |
| Language | Python |
| Category | LLM Plugin |
| Quality Score | 62/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-09 |
| Created | 2026-06-05 |
| Platforms | python |
| Est. Tokens | ~7k |
Tessera A small, from-scratch LLM stack built around one goal: distill a large teacher into a small student, then serve that student efficiently. Keeping that goal end-to-end means touching most of the pieces that matter in practice — custom GPU kernels, sharded training, an inference engine, quantization, and a serving front end — without any of it being a toy.
These tools work well together with tessera 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: