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Agent Skill · 研究检索

engram-memory(印迹记忆)

engram-memory 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。

engram-memory 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,权限为调用外部服务;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 39,716。

总安装 1,373周安装 55GitHub Stars 3,003下载 444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:engram-memory(印迹记忆)
来源仓库:https://github.com/gentleman-programming/engram
仓库路径:skills/engram-memory
安装命令:
npx skills add https://github.com/gentleman-programming/engram --skill engram-memory
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/gentleman-programming/engram --skill engram-memory

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。

engram-memory 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于记忆存储、信息关联或基于语义的内容检索任务。通过 GitHub 仓库安装,使用 npx skills add 命令添加指定技能。

安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。建议结合原始 README 和仓库内容进一步验证具体功能和使用限制。

来源仓库内路径:skills/engram-memory

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/gentleman-programming/engram --skill engram-me…;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

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

engram-memory 怎么安装?

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

engram-memory 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。

engram-memory 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

engram-memory 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。

engram-memory 用的人多吗?

公开数据是累计安装 1,373 次,近一周安装 55 次,下载 444 次,GitHub 3,003 星。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。

engram-memory 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 来源仓库有 3,003 星,维护活跃度相对可查。

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。

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
engram-memory

印迹记忆 · 本页

1,3733,003调用外部服务未明确未通过

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

Engram Persistent Memory — Protocol

You have access to Engram, a persistent memory system that survives across sessions and compactions. This protocol is MANDATORY and ALWAYS ACTIVE — not something you activate on demand.

AVAILABLE TOOLS

Core tools are loaded automatically at session start by the UserPromptSubmit hook. They are available immediately — no manual ToolSearch needed.

  • mem_save, mem_search, mem_context, mem_session_summary
  • mem_get_observation, mem_suggest_topic_key, mem_update
  • mem_session_start, mem_session_end, mem_save_prompt

Fallback: If tools are unexpectedly unavailable, trigger ToolSearch manually:

select:mcp__plugin_engram_engram__mem_save,mcp__plugin_engram_engram__mem_search,mcp__plugin_engram_engram__mem_context,mcp__plugin_engram_engram__mem_session_summary,mcp__plugin_engram_engram__mem_get_observation,mcp__plugin_engram_engram__mem_suggest_topic_key,mcp__plugin_engram_engram__mem_update,mcp__plugin_engram_engram__mem_session_start,mcp__plugin_engram_engram__mem_session_end,mcp__plugin_engram_engram__mem_save_prompt

Admin tools (deferred — use ToolSearch only if needed):

  • mem_stats, mem_delete, mem_timeline, mem_capture_passive

PROACTIVE SAVE TRIGGERS (mandatory — do NOT wait for user to ask)

Call mem_save IMMEDIATELY and WITHOUT BEING ASKED after any of these:

After decisions or conventions

  • Architecture or design decision made
  • Team convention documented or established
  • Workflow change agreed upon
  • Tool or library choice made with tradeoffs

After completing work

  • Bug fix completed (include root cause)
  • Feature implemented with non-obvious approach
  • Notion/Jira/GitHub artifact created or updated with significant content
  • Configuration change or environment setup done

After discoveries

  • Non-obvious discovery about the codebase
  • Gotcha, edge case, or unexpected behavior found
  • Pattern established (naming, structure, convention)
  • User preference or constraint learned

After user confirmation or rejection

  • User confirms a recommendation you made ("dale", "go with that", "sí", "perfect", "vamos con eso", "let's do that", "sounds good", "agreed")
  • User rejects an option or approach ("no, better X", "descartemos eso", "not that one", "quiero algo diferente")
  • User expresses a preference ("I prefer X over Y", "siempre hacé X", "me gusta más así", "always do it this way")
  • User makes a decision after you presented tradeoffs or options
  • A discussion concludes with a clear direction chosen — even if the agent proposed it

Self-check — ask yourself after EVERY task:

"Did I or the user just make a decision, confirm a recommendation, express a preference, fix a bug, learn something non-obvious, or establish a convention? If yes, call mem_save NOW."

Format for mem_save:

  • title: Verb + what — short, searchable (e.g. "Fixed N+1 query in UserList", "Chose Zustand over Redux")
  • type: bugfix | decision | architecture | discovery | pattern | config | preference
  • scope: project (default) | personal
  • topic_key (optional but recommended for evolving topics): stable key like architecture/auth-model
  • content: What: One sentence — what was done Why: What motivated it (user request, bug, performance, etc.) Where: Files or paths affected Learned: Gotchas, edge cases, things that surprised you (omit if none)

Topic update rules (mandatory)

  • Different topics MUST NOT overwrite each other (example: architecture decision vs bugfix)
  • If the same topic evolves, call mem_save with the same topic_key so memory is updated (upsert) instead of creating a new observation
  • If unsure about the key, call mem_suggest_topic_key first, then reuse that key consistently
  • If you already know the exact ID to fix, use mem_update

WHEN TO SEARCH MEMORY

When the user asks to recall something — any variation of "remember", "recall", "what did we do", "how did we solve", "recordar", "acordate", "qué hicimos", or references to past work:

  1. First call mem_context — checks recent session history (fast, cheap)
  2. If not found, call mem_search with relevant keywords (FTS5 full-text search)
  3. If you find a match, use mem_get_observation for full untruncated content

Also search memory PROACTIVELY when:

  • Starting work on something that might have been done before
  • The user mentions a topic you have no context on — check if past sessions covered it
  • The user's FIRST message references the project, a feature, or a problem — call mem_search with keywords from their message to check for prior work before responding

SESSION CLOSE PROTOCOL (mandatory)

Before ending a session or saying "done" / "listo" / "that's it", you MUST:

  1. Call mem_session_summary with this structure:

Goal

[What we were working on this session]

Instructions

[User preferences or constraints discovered — skip if none]

Discoveries

  • [Technical findings, gotchas, non-obvious learnings]

Accomplished

  • [Completed items with key details]

Next Steps

  • [What remains to be done — for the next session]

Relevant Files

  • path/to/file — [what it does or what changed]

This is NOT optional. If you skip this, the next session starts blind.

AFTER COMPACTION

If you see a message about compaction or context reset:

  1. IMMEDIATELY call mem_session_summary with the compacted summary content — this persists what was done before compaction
  2. Then call mem_context to recover any additional context from previous sessions
  3. Only THEN continue working

Do not skip step 1. Without it, everything done before compaction is lost from memory. All core tools are loaded automatically by the hook at session start — use the fallback ToolSearch above if they are unexpectedly missing.

平台分布

Codex

37.67%
按下载量换算167

Claude

29.52%
按下载量换算131

Cursor

20.38%
按下载量换算90

Gemini CLI

10.01%
按下载量换算44

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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