knowledge-ops
Helps your agent organize and find information. It manages knowledge bases across various locations, ensuring you find what you need.
What it does
This agent helps organize and store your important information. It can save, find, and keep your notes, documents, and data synced across different places.
When to use it
- ✓When you want to save an idea or note.
- ✓To organize documents and information.
- ✓When you need to find something you've saved before.
Example of what to ask your AI
“Save this note about today's meeting to my knowledge base.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It works by storing your information in different 'layers' or locations, like files on your computer, special conversation memory, or a database. This way, it knows where to look and how to keep everything updated.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill knowledge-opsOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/knowledge-ops/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
…
FAQ
What are the knowledge 'layers'?
They are like different folders or systems where your information can be stored, each with a purpose: some for quick access, others for long-term storage, and others for organizing complex data.
How does it avoid saving the same thing multiple times?
Before saving something new, it checks if that information already exists. If it finds it, it updates what's already there instead of creating a copy.
Where are my notes saved?
It depends on the type of information. Quick notes might go into conversation memory, important documents into a repository, and structured data into a database.
Details
Original description by the author
Knowledge base management, ingestion, sync, and retrieval across multiple storage layers (local files, MCP memory, vector stores, Git repos). Use when the user wants to save, organize, sync, deduplicate, or search across their knowledge systems.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #223
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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