by kunickiaj · Claude Skill · ★ 80
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx -y codemem setup --opencode-only
npx -y codemem stats
Taken 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 | 80 |
| Forks | 13 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 65/100 |
| Open Issues | 11 |
| Last Updated | 2026-11-07 |
| Created | 2026-01-13 |
| Platforms | claude-code, node |
| Est. Tokens | ~18k |
codemem Persistent memory for OpenCode and Claude Code. codemem captures what you work on across sessions, retrieves relevant context using hybrid search, and injects relevant context automatically in OpenCode.
Looking for a codemem alternative? If you're comparing codemem with other claude skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Grov automatically captures the context from your private AI sessions and syncs it to a shared team memory. It
🏛 [UNDER CONSTRUCTION] A (roman) claude plugin marketplace
Auto-review and iterate until quality work is delivered - a better alternative to ralph-claude-code. Switch be
Shared memory for your AI agents: a fact one agent writes on one machine is there for the next agent and the n
Opensource Memory for Agents
DeepContext is an MCP server that adds symbol-aware semantic search to Claude Code, Codex CLI, and other agent
Explore other popular claude skill tools:
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: