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engram-memory印迹记忆

Agent Skill

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。

总安装

1,373

周安装

55

GitHub Stars

3,003

下载量

444
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

external-service

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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