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Agent Skill · 研究检索

mem-skill(记忆技能)

mem-skill 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

mem-skill 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 67,168。

总安装 250周安装 10GitHub Stars 公开资料未说明下载 81核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mem-skill(记忆技能)
来源仓库:https://github.com/oliguo/mem-skill
仓库路径:skills/mem-skill
安装命令:
npx skills add https://github.com/oliguo/mem-skill --skill mem-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/oliguo/mem-skill --skill mem-skill

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

mem-skill 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。它可辅助记忆技能的聚合与初步筛选,提升 Agent 在认知增强阶段的效率。

通过 npx skills add 命令从指定仓库安装,需结合原始 README 核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。mem-skill 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/mem-skill

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/oliguo/mem-skill --skill mem-skill;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

mem-skill 怎么安装?

在终端执行 npx skills add https://github.com/oliguo/mem-skill --skill mem-skill。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mem-skill 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mem-skill 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

mem-skill 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

mem-skill 用的人多吗?

公开数据是累计安装 250 次,近一周安装 10 次,下载 81 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mem-skill 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mem-skill

记忆技能 · 本页

250—只读未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Mem-Skill: Self-Evolving Knowledge System

Initialization

When the user runs /mem-skill init, execute the following setup:

  1. Determine the current workspace root directory.
  2. Create the directory structure: <workspace>/ ├── knowledge-base/ │ └── _index.json └── experience/ └── _index.json
  3. Populate knowledge-base/_index.json with the starter template (see "Knowledge Base Index Format" below).
  4. Populate experience/_index.json with the starter template (see "Experience Index Format" below).
  5. Confirm to the user: "mem-skill initialized. Knowledge base and experience directories created."

Engine Selection

When the user runs /mem-skill init --mem-engine=qmd, optionally with extra --qmd-* flags:

Supported flags (all optional):

FlagValuesDefault
--qmd-scope=<scope>project, global*(ask user)*
--qmd-knowledge=<name>any string*(ask user)*
--qmd-experience=<name>any string*(ask user)*
--qmd-mask=<pattern>glob pattern**/*.md

Examples:

/mem-skill init --mem-engine=qmd
/mem-skill init --mem-engine=qmd --qmd-scope=project
/mem-skill init --mem-engine=qmd --qmd-scope=global --qmd-knowledge=my-kb --qmd-experience=my-exp
/mem-skill init --mem-engine=qmd --qmd-mask="**/*.md,**/*.txt"

Init procedure:

  1. Perform all standard init steps above.
  2. Check if QMD is installed: run which qmd or npx @tobilu/qmd status.
  3. If QMD is not installed, prompt: "QMD is not installed. Install it now with npm install -g @tobilu/qmd? (QMD requires Node.js >= 22)"
  4. Determine collection scope:

- If --qmd-scope was provided, use that value. - Otherwise, you MUST ask the user (do NOT guess or auto-choose): "Where should QMD collections be stored? Project — scoped to this workspace (recommended for multi-project setups) Global — shared across all workspaces" - If project: default collection name prefix is the sanitized workspace folder name (e.g., folder my-app → prefix my-app). - If global: default collection name prefix is mem.

  1. Determine collection names:

- If --qmd-knowledge was provided, use that value. - If --qmd-experience was provided, use that value. - For any name NOT provided via flags, you MUST ask the user (do NOT auto-generate): "What name for the knowledge collection? (default: <prefix>-knowledge)" "What name for the experience collection? (default: <prefix>-experience)" - Accept user input or use the defaults if the user confirms.

  1. Determine file mask:

- If --qmd-mask was provided, use that value. - Otherwise use **/*.md.

  1. After all values are confirmed, create the QMD collections: qmd collection add <workspace>/knowledge-base --name <knowledge-name> --mask "<mask>" qmd collection add <workspace>/experience --name <experience-name> --mask "<mask>" qmd context add qmd://<knowledge-name> "General knowledge base: reusable workflows, preferences, best practices" qmd context add qmd://<experience-name> "Skill-specific experience: pitfalls, parameters, solutions" qmd embed
  2. Create a .mem-skill.config.json at the workspace root: {"engine": "qmd", "version": "1.0.0", "scope": "<project|global>", "mask": "<mask>", "collections": {"knowledge": "<knowledge-name>", "experience": "<experience-name>"}}
  3. Confirm: "mem-skill initialized with QMD memory engine. Collections created and embeddings generated."

IMPORTANT: Never silently create QMD collections without confirming scope and names with the user. If no --qmd-* flags were provided, every question above MUST be asked interactively.

For the default engine (no --mem-engine flag), create .mem-skill.config.json with:

{
  "engine": "default",
  "version": "1.0.0"
}

For detailed engine-specific behavior, see references/qmd-engine.md and references/engines.md.

Manual Recording Command

When the user runs /mem-skill recordnow, immediately trigger the recording flow for the current conversation — even if Step 5 was not triggered automatically.

This is useful when:

  • Multiple tasks were completed in one session and the agent forgot to ask.
  • The user wants to record something that didn't trigger the satisfaction keywords.
  • The user remembers later that a solution was worth saving.

Procedure:

  1. Review the full conversation history for completed tasks.
  2. For each completed task, summarize it into a one-line essence.
  3. Evaluate: "Will this save time next time?"
  4. Present all recordable items to the user as a numbered list: "I found these completed tasks worth recording: [summary of task 1] → knowledge-base [summary of task 2] → knowledge-base [summary of skill usage] → experience Which ones should I record? (all / 1,2 / none)"
  5. On approval, write each selected item following the same write procedure as Step 5 (including QMD post-write sync if applicable).
  6. If no recordable tasks are found, respond: "I reviewed the conversation but didn't find any completed tasks worth recording. Is there something specific you'd like me to save?"

Core Loop (Mandatory Every Turn)

Execute these steps on every conversation turn. Do not display internal cache state to the user.

Step 0: In-Conversation Cache (Internal)

Maintain these variables silently within the conversation:

  • last_keywords — keywords from the previous turn
  • last_topic_fingerprint — top 3 keywords as a fingerprint
  • last_index_lastUpdated — timestamp of last index read
  • last_matched_categories — categories matched on last read
  • last_used_skills — non-mem-skill skills used this turn
  • missing_experience_skills — skills with no experience entry
  • loaded_experience_skills — skills whose experience has been loaded this session

Step 1: Extract Keywords (No File I/O)

  • Extract 3–8 core nouns/phrases from the user's current message.
  • Deduplicate and normalize casing.
  • Generate topic_fingerprint from the top 3 keywords.

Step 2: Detect Topic Switch (No File I/O)

A topic switch occurs when any of these conditions are met:

  • Explicit transition words: "also", "switch to", "by the way", "next", "instead"
  • Current keywords differ from last_keywords by >= 40%
  • User explicitly requests a new category or topic

Step 3: Cross-Skill Experience Read (Forced — Ignores Topic Switch)

Whenever a non-mem-skill skill is used this turn:

  • If the skill-id is already in loaded_experience_skills, skip (do not re-read or re-announce).
  • Otherwise:

1. Read experience/_index.json. 2. If a matching skill-id entry exists, load experience/skill-<skill-id>.md. 3. Add the skill-id to loaded_experience_skills. 4. Include in response: "Loaded experience: skill-<skill-id>.md" 5. If no entry exists, add to missing_experience_skills.

Engine-specific retrieval:

  • Default engine: Read experience/_index.json and match by skillId.
  • QMD engine: Read collection names from .mem-skill.config.json, then run qmd search "<skill-id>" -c <experience-collection> --json -n 5 for keyword match, or qmd query "<skill-id> <context>" -c <experience-collection> --json -n 5 for deeper retrieval.

Step 4: Knowledge Base Read (Only on Topic Switch)

Execute only on the first turn of the conversation or when a topic switch is detected:

Default engine:

  1. Read knowledge-base/_index.json.
  2. Match current keywords against all category keywords arrays.
  3. Load every matched category file (no priority ranking — load all matches).
  4. If no category matches, follow the "Dynamic Category" flow (see below).
  5. If any files were loaded, include in response: "Loaded knowledge: <file1>.md, <file2>.md"

If no topic switch occurred, reuse last_matched_categories without re-reading.

QMD engine:

  1. Read collection names from .mem-skill.config.json.
  2. Run qmd query "<keywords joined by space>" -c <knowledge-collection> --json -n 10 --min-score 0.3.
  3. Load top results as context.
  4. Include in response: "Retrieved knowledge via QMD: <titles>"

Step 5: Proactive Recording (Most Important)

Trigger conditions:

  • The current task is clearly completed at high quality.
  • The user expresses satisfaction ("great", "perfect", "that works", etc.).

Recording procedure:

  1. Summarize: Distill the solution into a one-line essence.
  2. Evaluate value: "Will this save time next time?"
  3. Ask permission: Always say something like: "We solved [problem description]. I'd like to record this experience so I can reference it next time. Is that okay?"
  4. Write on approval:

- Skill experience (if a non-mem-skill skill was used and the skill has no entry or has new techniques): Write to experience/skill-<skill-id>.md and update experience/_index.json. - General knowledge (if it's a reusable workflow, preference, or solution): Write to knowledge-base/<category>.md and update knowledge-base/_index.json.

QMD engine post-write: After writing any .md file, run:

qmd update
qmd embed

Forced rule — always ask when experience is missing: If a non-mem-skill skill was used this turn and that skill has no entry in experience/_index.json, you must ask at task completion:

"We used this time, but there's no experience record yet. Can I record this session's approach for future reference?"

If Step 5 was not triggered (e.g., multi-task sessions where satisfaction signals were missed), the user can run /mem-skill recordnow at any time to manually trigger recording. See "Manual Recording Command" above.

Index Formats

Knowledge Base Index Format

knowledge-base/_index.json:

{
  "lastUpdated": "YYYY-MM-DD",
  "version": "1.0.0",
  "totalEntries": 0,
  "categories": [
    {
      "id": "category-id",
      "name": "Category Name",
      "keywords": ["keyword1", "keyword2"],
      "file": "category-id.md",
      "count": 0
    }
  ]
}

Experience Index Format

experience/_index.json:

{
  "lastUpdated": "YYYY-MM-DD",
  "version": "1.0.0",
  "skills": [
    {
      "skillId": "skill-id",
      "file": "skill-<skill-id>.md",
      "keywords": ["keyword1", "keyword2"],
      "count": 0
    }
  ]
}

Entry Formats

Knowledge Base Entry

## [Short Title]
**Date:** YYYY-MM-DD
**Context:** One-line description of the use case
**Best Practice:**
- Key point 1
- Key point 2 — parameter notes and tuning guidance
**Keywords:** keyword1, keyword2, keyword3

Experience Entry

## [Problem/Technique Title]
**Date:** YYYY-MM-DD
**Skill:** <skill-id>
**Context:** One-line description of the issue
**Solution:**
- Concrete step 1
- Concrete step 2
**Key Files/Paths:**
- /path/to/relevant/file
**Keywords:** keyword1, keyword2, keyword3

Dynamic Category (Knowledge Base Only)

When user keywords do not match any existing category:

  1. Suggest creating a new category.
  2. Ask the user for a category name and keywords.
  3. Create a new <category-id>.md file and update knowledge-base/_index.json.

Recording Criteria

Core question: Will this save the user time next time?

Knowledge Base — Should Record:

  • Reusable workflows and decision steps (cross-domain procedures)
  • High-cost mistakes and their correction paths
  • Critical parameters, settings, or prerequisites
  • User preferences and style rules (tone, format, design)
  • Multi-attempt solutions (include failure reasons and success conditions)
  • Reusable templates, checklists, and output formats
  • External dependencies or resource locations

Knowledge Base — Should NOT Record:

  • Single Q&A with no reusable process
  • Pure conceptual explanations without concrete steps
  • Context-free, non-reusable conclusions

Experience — Should Record:

  • Pitfalls and their fixes when using a specific skill (include error messages)
  • Critical parameters or configurations that affect outcomes
  • Reusable templates, prompts, or workflows for that skill
  • Dependency or asset paths (fonts, images, project entry points)
  • Steps requiring a specific order or technique to succeed

Experience — Should NOT Record:

  • Pure theory or conceptual explanations (those belong in knowledge-base)
  • Conclusions without reproducible steps
  • One-off, non-reusable operations

Storage Paths

  • Knowledge index: knowledge-base/_index.json
  • Knowledge content: knowledge-base/<category-id>.md
  • Experience index: experience/_index.json
  • Experience content: experience/skill-<skill-id>.md
  • Config: .mem-skill.config.json

QMD Upgrade Suggestion

When the knowledge base exceeds 50 entries, proactively suggest upgrading to QMD:

"Your knowledge base has grown to [N] entries. For faster semantic search, consider upgrading to QMD: run /mem-skill init --mem-engine=qmd."

For full QMD engine details, see references/qmd-engine.md. For the engine abstraction and adding new engines, see references/engines.md.

平台分布

Codex

35.32%
按下载量换算29

Claude

31.58%
按下载量换算26

Cursor

19.53%
按下载量换算16

Gemini CLI

11.02%
按下载量换算9

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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