by NanoFlow-io · Codex Skill · ★ 98
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git clone https://github.com/NanoFlow-io/engram.gitTaken 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 | 98 |
| Forks | 1 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 66/100 |
| Last Updated | 2026-06-08 |
| Created | 2026-05-20 |
| Platforms | node |
| Est. Tokens | ~3k |
Engram engram (n.) — the physical/biochemical trace a memory leaves behind. The thing that makes "remembering" actually possible. Engram is the hybrid long-term memory plugin for OpenClaw agents. It gives your agent a brain that doesn't forget between sessions — backed by SQLite + FTS5 for exact, structured recall and LanceDB for fuzzy semantic search over embeddings.
These tools work well together with engram 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: