by nablo-io · Codex Skill · ★ 97
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install lerimTaken 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 | 97 |
| Forks | 6 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 59/100 |
| Last Updated | 2026-07-28 |
| Created | 2026-02-23 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~18k |
Lerim compiles completed agent traces into cited, reusable context. Lerim sits above agent traces, compiles useful signal into cited context and eval assets, and gives future agents the operating memory they need before work begins. Lerim is an open-source project by Nablo. It compiles context and does not train models — training a small model on the result is the Nablo pipeline. <
These tools work well together with lerim for enhanced workflows:
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Compiles AI agent traces and truns them into reusable context.
Compiles AI agent traces and truns them into reusable context.
Compile completed AI agent runs into reusable, cited context.
Project memory system for AI coding assistants (Claude Code, Cursor, Codex): session logs, project wiki, rules
Project memory for long-running AI work across agents, models, and sessions. Keep context, decisions, progress
Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and
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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: