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ogham-maintain奥格姆维护

Agent Skill

ogham-maintain 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

282

周安装

12

GitHub Stars

96

下载量

99
CodexClaudeCursorGemini CLI

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:ogham-maintain(奥格姆维护)
来源仓库:https://github.com/ogham-mcp/ogham-mcp
仓库路径:skills/ogham-maintain
安装命令:
npx skills add https://github.com/ogham-mcp/ogham-mcp --skill ogham-maintain
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/ogham-mcp/ogham-mcp --skill ogham-maintain

简介

用于查找、检索和筛选相关信息。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 适合在关键词搜索或任务场景中快速定位候选结果。
  • 可结合仓库 README 核验具体用法和参数含义。
  • 安装前建议确认权限范围、维护状态及是否会执行命令或读写文件。
  • ogham-maintain 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Ogham maintenance

You handle admin tasks for Ogham shared memory. Most of these are infrequent operations -- provider switches, bulk cleanup, health checks.

Available operations

Health check

Run health_check first if the user reports problems. It tests database connectivity, embedding provider, and configuration. Report what it finds plainly -- if something is broken, say what and suggest a fix.

Stats overview

Run get_stats and list_profiles to give the user a picture of their memory:

  • Total memories and breakdown by profile
  • Top sources (which clients are storing)
  • Top tags (what categories dominate)
  • Cache stats via get_cache_stats if they ask about performance

Present it as a concise summary, not raw JSON.

Cleanup expired memories

  1. Run get_stats to show how many memories exist
  2. Check if any profiles have TTLs set (this info comes from list_profiles)
  3. If there are expired memories, tell the user how many before running cleanup_expired
  4. Run cleanup_expired only after confirming with the user -- deletion is permanent

Export

Run export_profile with the format the user wants (JSON or Markdown). Tell them where the output goes and how to use it.

If they want to export a specific profile, switch to it first with switch_profile, export, then switch back.

Re-embed all memories

This is needed after switching embedding providers (e.g. Ollama to OpenAI). It regenerates every vector in the active profile.

Before running:

  1. Confirm the user has switched providers in their config
  2. Warn that this takes time -- roughly 100ms per memory with a remote provider
  3. Run re_embed_all which reports progress as it goes

After: suggest running link_unlinked to rebuild the knowledge graph with the new embeddings, since similarity scores will be different.

Backfill knowledge graph links

link_unlinked scans memories that don't have relationship edges yet and creates links where embedding similarity is above threshold.

  • Default threshold 0.85 is conservative -- only very similar memories get linked
  • Suggest 0.7 for broader connections in diverse collections
  • The user can set batch_size to control how many are processed at once

Report how many links were created when it finishes.

Condense old memories

compress_old_memories shrinks old, inactive memories to save space and reduce search noise.

Three levels:

  • Full text (default, recent memories)
  • Condensed (key sentences, code blocks preserved, ~30% of original)
  • Trace (one-line summary with tags)

Before running:

  1. Explain that condensing is based on age and activity -- important, frequently-accessed, or high-confidence memories resist condensing
  2. Explain that original content is always preserved and can be restored
  3. Run compress_old_memories -- it reports how many were condensed at each level

Profile management

  • list_profiles -- show all profiles with memory counts
  • switch_profile -- change active profile (session only)
  • set_profile_ttl -- set auto-expiry. Explain that expired memories are filtered from searches immediately but not deleted until cleanup_expired runs
  • To remove a TTL, call set_profile_ttl with ttl_days=None

General approach

These are power-user operations. Be direct about what each one does, what it costs (time, data loss), and whether it's reversible. Deletion and re-embedding are not reversible. Exports, stats, and health checks are read-only and safe to run anytime.

If the user asks for something vague like "clean up my ogham", start with stats to understand what they have, then suggest specific actions based on what you see.

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

34.9%
按下载量换算35

Claude

31.55%
按下载量换算31

Cursor

17.05%
按下载量换算17

Gemini CLI

8.22%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。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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