memory

Gobby's memory system (gobby-memory MCP) stores persistent facts across sessions. Use progressive discovery for tool schemas — this skill teaches judgment, not API reference.

Note: Claude Code's native memory system (~/.claude/projects/.../memory/) is disabled by Gobby rules. All memory operations go through gobby-memory MCP. Do not read or write to the native memory filesystem — those operations will be blocked.

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When to Use Memory

Memory is one of several persistence mechanisms. Pick the right one:

| What you learned | Store it as | Why | |-----------------|-------------|-----| | User preference or convention | Memory | Durable, cross-session, hard to rediscover | | A bug or issue to fix | Task | Actionable, trackable, closeable | | Implementation approach for current work | Plan | Scoped to conversation, structured | | Something the code already shows | Nothing | Code is the source of truth | | Something git log already shows | Nothing | Git is the source of truth |

Rule of thumb: Would rediscovering this require multi-step exploration in a future session? If yes, memorize it. If you can find it by reading code or running git log, don't.

How to Write Durable Memories

Memories that survive are specific, contextual, and time-resilient.

Good memories:

• "Josh prefers squash merges from worktrees — a Gemini session once created hundreds of micro-commits"
• "The PostgreSQL hub migration baseline is 260; versions below _MIN_MIGRATION_VERSION are intentionally unsupported."
• "Pipeline bugs tracked in Linear project INGEST"

Bad memories:

• "The auth module is in src/gobby/auth/" — code structure changes; find is faster
• "Fixed bug in task validation" — the fix is in git; the commit message has context
• "Currently working on skill audit" — ephemeral, only relevant this session

Durability test: Will this be useful in 3 months? If the answer depends on code not changing, it's not durable.

What to Remember

• User preferences — coding style, communication preferences, workflow choices
• Conventions — naming patterns, architectural decisions that aren't in docs
• Non-obvious relationships — "X depends on Y because of Z" where Z isn't documented
• External references — where to find things outside the repo (Linear projects, Slack channels, dashboards)
• Design rationale — why something was built a certain way, especially if counterintuitive

What NOT to Remember

• Code paths and file locations — they change; use search tools
• Recent git activity — git log is authoritative
• Bug fixes or solutions — the fix is in the code, context in the commit
• Anything in CLAUDE.md — already loaded every session
• Ephemeral state — current task, temporary config, in-progress work

Tags

Tags enable precise recall. Extract them from content:

| Content signal | Tag | |---------------|-----| | Testing, fixtures, pytest | testing | | Security, auth, permissions | security | | Architecture, design decisions | architecture | | User preferences, conventions | convention | | External systems, integrations | reference |

Use tags_all for AND queries, tags_any for OR, tags_none to exclude.

Anti-Patterns

• Memorizing tool schemas — progressive discovery handles this; schemas go stale in memory
• Memorizing code structure — files move; grep/glob is faster and always current
• Creating duplicate memories — always search before creating; create_memory returns similar existing memories for exactly this reason
• Storing one-time instructions as conventions — only save if the user explicitly asked to remember, or if rediscovery would be expensive

Maintenance

Memories decay. Periodically:

• Audit — find stale, duplicate, or code-derivable memories that should be cleaned up
• Cleanup — remove memories that are no longer accurate or useful
• Rebuild cross-references — keeps the relationship graph between memories fresh
• Reindex embeddings — improves semantic search quality after bulk changes

Use progressive discovery to find the maintenance tools on gobby-memory when needed.

Knowledge Graph

The knowledge graph extracts entities and relationships from memories into a searchable graph. Use it when you need to understand connections between concepts rather than searching for specific content.