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knowledge-ops

by @affaan-mv
4.4(27)

多層知識システムを管理し、複数のストレージ間で知識の取り込み、整理、同期、検索を実行します。コードリポジトリ、GitHub、Linearなどのワークスペースをサポートします。

knowledge-managementdocumentationinformation-retrievalsyncworkspace-automationGitHub
インストール方法
npx skills add https://github.com/affaan-m/everything-claude-code --skill knowledge-ops
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Before / After 効果比較

1
使用前

知識が複数のプラットフォームやツールに分散しており、手動での同期は時間がかかり、エラーが発生しやすい。情報検索には複数のシステムを切り替える必要がある。

使用後

コード、ドキュメント、ワークオーダーなどの複数ソースの知識を自動的に集約し、統一されたインデックスと検索により、関連情報にワンクリックでアクセスできる。

SKILL.md

knowledge-ops

Knowledge Operations

Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across multiple stores.

Prefer the live workspace model:

  • code work lives in the real cloned repos

  • active execution context lives in GitHub, Linear, and repo-local working-context files

  • broader human-facing notes can live in a non-repo context/archive folder

  • durable cross-machine memory belongs in the knowledge base, not in a shadow repo workspace

When to Activate

  • User wants to save information to their knowledge base

  • Ingesting documents, conversations, or data into structured storage

  • Syncing knowledge across systems (local files, MCP memory, Supabase, Git repos)

  • Deduplicating or organizing existing knowledge

  • User says "save this to KB", "sync knowledge", "what do I know about X", "ingest this", "update the knowledge base"

  • Any knowledge management task beyond simple memory recall

Knowledge Architecture

Layer 1: Active execution truth

  • Sources: GitHub issues, PRs, discussions, release notes, Linear issues/projects/docs

  • Use for: the current operational state of the work

  • Rule: if something affects an active engineering plan, roadmap, rollout, or release, prefer putting it here first

Layer 2: Claude Code Memory (Quick Access)

  • Path: ~/.claude/projects/*/memory/

  • Format: Markdown files with frontmatter

  • Types: user preferences, feedback, project context, reference

  • Use for: quick-access context that persists across conversations

  • Automatically loaded at session start

Layer 3: MCP Memory Server (Structured Knowledge Graph)

  • Access: MCP memory tools (create_entities, create_relations, add_observations, search_nodes)

  • Use for: Semantic search across all stored memories, relationship mapping

  • Cross-session persistence with queryable graph structure

Layer 4: Knowledge base repo / durable document store

  • Use for: curated durable notes, session exports, synthesized research, operator memory, long-form docs

  • Rule: this is the preferred durable store for cross-machine context when the content is not repo-owned code

Layer 5: External Data Store (Supabase, PostgreSQL, etc.)

  • Use for: Structured data, large document storage, full-text search

  • Good for: Documents too large for memory files, data needing SQL queries

Layer 6: Local context/archive folder

  • Use for: human-facing notes, archived gameplans, local media organization, temporary non-code docs

  • Rule: writable for information storage, but not a shadow code workspace

  • Do not use for: active code changes or repo truth that should live upstream

Ingestion Workflow

When new knowledge needs to be captured:

1. Classify

What type of knowledge is it?

  • Business decision -> memory file (project type) + MCP memory

  • Active roadmap / release / implementation state -> GitHub + Linear first

  • Personal preference -> memory file (user/feedback type)

  • Reference info -> memory file (reference type) + MCP memory

  • Large document -> external data store + summary in memory

  • Conversation/session -> knowledge base repo + short summary in memory

2. Deduplicate

Check if this knowledge already exists:

  • Search memory files for existing entries

  • Query MCP memory with relevant terms

  • Check whether the information already exists in GitHub or Linear before creating another local note

  • Do not create duplicates. Update existing entries instead.

3. Store

Write to appropriate layer(s):

  • Always update Claude Code memory for quick access

  • Use MCP memory for semantic searchability and relationship mapping

  • Update GitHub / Linear first when the information changes live project truth

  • Commit to the knowledge base repo for durable long-form additions

4. Index

Update any relevant indexes or summary files.

Sync Operations

Conversation Sync

Periodically sync conversation history into the knowledge base:

  • Sources: Claude session files, Codex sessions, other agent sessions

  • Destination: knowledge base repo

  • Generate a session index for quick browsing

  • Commit and push

Workspace State Sync

Mirror important workspace configuration and scripts to the knowledge base:

  • Generate directory maps

  • Redact sensitive config before committing

  • Track changes over time

  • Do not treat the knowledge base or archive folder as the live code workspace

GitHub / Linear Sync

When the information affects active execution:

  • update the relevant GitHub issue, PR, discussion, release notes, or roadmap thread

  • attach supporting docs to Linear when the work needs durable planning context

  • only mirror a local note afterwards if it still adds value

Cross-Source Knowledge Sync

Pull knowledge from multiple sources into one place:

  • Claude/ChatGPT/Grok conversation exports

  • Browser bookmarks

  • GitHub activity events

  • Write status summary, commit and push

Memory Patterns

# Short-term: current session context
Use TodoWrite for in-session task tracking

# Medium-term: project memory files
Write to ~/.claude/projects/*/memory/ for cross-session recall

# Long-term: GitHub / Linear / KB
Put active execution truth in GitHub + Linear
Put durable synthesized context in the knowledge base repo

# Semantic layer: MCP knowledge graph
Use mcp__memory__create_entities for permanent structured data
Use mcp__memory__create_relations for relationship mapping
Use mcp__memory__add_observations for new facts about known entities
Use mcp__memory__search_nodes to find existing knowledge

Best Practices

  • Keep memory files concise. Archive old data rather than letting files grow unbounded.

  • Use frontmatter (YAML) for metadata on all knowledge files.

  • Deduplicate before storing. Search first, then create or update.

  • Prefer one canonical home per fact set. Avoid parallel copies of the same plan across local notes, repo files, and tracker docs.

  • Redact sensitive information (API keys, passwords) before committing to Git.

  • Use consistent naming conventions for knowledge files (lowercase-kebab-case).

  • Tag entries with topics/categories for easier retrieval.

Quality Gate

Before completing any knowledge operation:

  • no duplicate entries created

  • sensitive data redacted from any Git-tracked files

  • indexes and summaries updated

  • appropriate storage layer chosen for the data type

  • cross-references added where relevant

Weekly Installs536Repositoryaffaan-m/everyt…ude-codeGitHub Stars157.6KFirst Seen11 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled oncodex504opencode488gemini-cli485kimi-cli484antigravity484amp484

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統計データ

インストール数4.3K
評価4.4 / 5.0
バージョン
更新日2026年8月6日
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ユーザー評価

4.4(27)
5
56%
4
33%
3
7%
2
4%
1
0%

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対応プラットフォーム

🔧Claude Code

タイムライン

作成2026年4月17日
最終更新2026年8月6日
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