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