recall-loom: Project memory for long-running AI work across agents, models, and sessions

by Frappucc1no · Claude Skill · ★ 154

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install recall-loom

npx skills add https://github.com/Frappucc1no/recall-loom --skill recallloom

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 63/100

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.

agent-memoryagent-skillsai-agentsai-codingai-memoryclaudeclaude-codeclaude-code-skillsclaude-skillscodex

Quick Facts

Stars154
Forks5
LanguagePython
CategoryClaude Skill
LicenseApache-2.0
Quality Score63/100
Open Issues2
Last Updated2026-08-06
Created2026-04-13
Platformsclaude-code, cli, codex, gemini, python
Est. Tokens~16k

About recall-loom

🧵 RecallLoom 让项目自己记住自己。 RecallLoom 是给 AI 协作使用的文件化项目记忆层：把背景、进展、关键决策和下一步保存到项目旁边/项目内的 Markdown / JSON 文件里，不需要数据库、RAG 或后台服务。换会话、换模型、换工具，下一次 AI 协作更容易接上当前状态。 English · 简体中文 [!TIP] 如果你已经被“隔一段时间回来又要复述十分钟”的重启税折磨过，RecallLoom 的目标很直接：让项目状态、关键决策和下一步留在项目里，让下一次 AI 协作接得上。 快速跳转： 为什么需要 · 30 秒开始 · 特性 · 项目记忆循环 · 适合与不适合 · [方案对比](#与

Compatible Skills

These tools work well together with recall-loom for enhanced workflows:

  • agentkeeper — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (60%)
  • aictx — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (57%)
  • anywhere-agents — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • memstack — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)

recall-loom alternative? Top 6 similar tools

Looking for a recall-loom alternative? If you're comparing recall-loom 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.

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How this security grade is produced

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:

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