context-loader

# Context Loader Pre-fetches relevant memories to prime context before working on a task. ## When to use - Session start (invoke manually or auto-triggered by skill description matching) - User starts work on a specific feature or file set - Complex multi-step task begins - User says "what do we know about X" or "context for X" ## Steps 1. **Extract topics** from current message/task. Identify: file paths, module names, feature areas, error patterns. 2. **Run 2-4 parallel `search_memories` calls** with different angles: | Query angle | Filter | Purpose | |---|---|---| | Feature/module name | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}` | Architecture decisions | | File paths mentioned | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "convention"}}]}` | Coding patterns | | Error keywords (if any) | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "anti_pattern"}}]}` | Known pitfalls | | Broad project context | `{"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}` | Catch-all | 3. **Deduplicate** results by memory ID across all search responses. 4. **Output compact context block** (max 10 memories): ``` context-loader: loaded <N> memories for "<task summary>" - [decision] <content> [mem0:<short_id>] - [convention] <content> [mem0:<short_id>] - [anti_pattern] <content> [mem0:<short_id>] ``` 5. If **zero results**: output nothing. Don't announce empty context. ## Constraints - **Read-only** — never modify or delete memories - **Max 10 memories** returned (most relevant only) - **Silent on empty** — only surfaces findings if relevant context exists - Skip memories already visible in current session context