技能潮 SkillTide 提交收录
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liza-studio/skillmem

liza-studio/skillmem

Self-improving skill memory for
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add skillmem -- npx -y skillmem
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
9 ★ · liza-studio/skillmem · 取自该仓库的 GitHub 页面 · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
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综合分 703 · 9 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 liza-studio/skillmem;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

skillmem

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

**Self-improving skills for Claude Code and Codex — your agents learn, recall, reinforce, and forget.**
Strength has to be earned — saying a skill helped is not evidence, a passing test is:
Generated from a real run: scripts/demo.sh --record | python3 scripts/cast_to_svg.py > docs/demo-evidence.svg.
skillmem gives Claude Code and the Codex CLI a local, persistent skill & memory layer. After every non-trivial task the agent can record *how it was done* as a skill; before the next task it recalls the relevant ones; skills that keep proving useful get stronger, and skills nobody uses fade away — the way human memory works.
- **$0 per write and per read** — no LLM calls, no cloud, no API keys. Plain SQLite on your disk.
- **Bilingual hybrid search, fully local** — FTS5 BM25 + Snowball stemming (EN/RU) matches inflected forms within a language; the multilingual ONNX embedder is what lets a Russian query find an English skill, so install the semantic extra if you work across both. All on CPU, offline.
- **Ebbinghaus strength model, earned not claimed** — strength rises only on evidence from outside the agent's own judgement, falls after a failure, and fades on a schedule when unused; dead skills are swept to a backed-up archive (never deleted). Rules that are rare by nature can be pinned out of decay.
- **Provenance, and trust the owner grants** — every memory records where it came from (owner / agent / imported / derived), and only the owner approves one as a rule (skillmem trust ). Anything unapproved — an imported pack, a summary of a transcript that quoted a web page, a rule an agent was talked into saving — is injected inside a marked block that says it is data, not instructions. An agent cannot change a memory the owner wrote or approved: it writes a proposal under a new slug.
- **Tamper-evident history** — every edit is appended to a SHA256 hash-chain; skillmem verify detects any after-the-fact tampering.
- **Deep Claude Code integration** — hooks on five events + 9 MCP tools installed with one command.
- **One memory, several agents** — Claude Code and Codex share a single database, and every
record carries the agent that wrote it, taken from the MCP handshake, so authorship stays
readable when they learn side by side.
- **Cross-platform** — macOS (launchd), Windows (schtasks), Linux (systemd user timers, cron fallback).
- **No vendor lock** — export-all dumps everything to plain markdown with YAML frontmatter; re-importing the dump yields the same records. One destination per database: the exporter prunes its own stale files via a manifest, and refuses a directory another database exports to rather than overwrite its backup.
Why
Agents repeat their mistakes because each session starts from zero. Existing "memory" tools store facts; skillmem stores *procedures* — trigger, steps, outcome, lessons — and ranks them by how often they actually helped. The write path costs nothing, so the agent can afford to learn from every task.
What 0.10.0 changed
Memory that an agent writes is not the same thing as a rule you set, and until 0.10.0 this
project treated them the same. An external text — a README, a web page — reaches a transcript,
a model distils it into a note, and the note comes back in the next session under a heading
that reads like your own rules. A document could also talk an agent into saving a rule through
mem_learn, and that rule looked exactly like one you wrote.
Now provenance is a field, trust is an act, and the summariser that reads your transcripts runs
with **no tools at all** (--tools "" plus --strict-mcp-config; a CLI that does not understand
those flags gets no recap rather than an uncaged one). The full list — including the migration
and what it does and does not approve on upgrade — is in the CHANGELOG.
The seven releases before it, in one line each, because they were all about the same hook:
0.9.3 stopped the Stop hook recursing into itself (one machine spawned 4083 summary sessions in
a day); 0.9.4 put a rate limit on it and stopped a failing model buying a call per turn; 0.9.5
fixed four silent defects, including recall being dead for notebook edits; 0.9.6 stopped a slow
来源
awesome · https://github.com/liza-studio/skillmem
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