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: OpenAI Codex Memory (Quick Access)

• Path: ~/.codex/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 OpenAI Codex 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: Codex 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:

• Codex/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 ~/.codex/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