context-retrieval

agents	["system-architecture-specialist","workflow-quality-specialist","knowledge-operations-specialist"]
category	retrieval
description	Actively retrieve context from the Dual-Storage Cognitive Memory system (Qdrant & SQLite) for seamless IDE integration.
knowledge	["cognitive-memory-patterns.json"]
name	context-retrieval
related_skills	["managing-memory"]
templates	["none"]
tools	["search_memory"]
type	skill
version	1.0.0
references	["none"]
settings	{"auto_approve":true,"retry_limit":3,"timeout_seconds":60,"safe_to_parallelize":true,"orchestration_pattern":"standard"}

Context Retrieval (Antigravity Architecture)

A cognitive agent must not operate in isolation. You have access to the search_memory tool provided by the RAG MCP Server. Use this skill to actively recall experiences, procedural knowledge, tools, and entities.

When to Use

• You are starting a new implementation plan and need to check how similar tasks were resolved.
• You hit a stack-trace or bug and want to check if the error is resolved in memory_semantic.
• You interact with Plane MCP tools and need to recall specific task UUIDs from memory_entity.
• You are looking for an exact factory workflow to execute in memory_procedural.

Process

1. Decide which memory tier contains the data type you need:
   • memory_procedural: Workflows, Skills, Scripts.
   • memory_toolbox: Capabilities, MCP actions, script descriptions.
   • memory_summary: Post-session logs and high-level conversation synopses.
   • memory_semantic: Condensed rules, facts, architectural patterns.
   • memory_entity: Proper nouns, system names, file paths, UUIDs.
2. Use the search_memory MCP tool to retrieve documents from that specific collection.
3. Inject the retrieved insights into your immediate task logic.

Example: Error Resolution

# Thought: I hit a ModuleNotFoundError on 'scripts.memory'. Let's check semantic memory.
call:mcp_server:search_memory(query="ModuleNotFoundError scripts.memory PYTHONPATH", collection="memory_semantic")

Example: Finding a Workflow

# Thought: The user told me to do an Alpha Factor Mining run. Let's retrieve the workflow.
call:mcp_server:search_memory(query="alpha factor mining", collection="memory_procedural")

Prerequisites

• The Qdrant Docker memory backend must be running on localhost:6333.
• The qdrant-rag MCP server must be active (provides search_memory tool).
• Access to the Python scripting environment in /scripts/memory.

Best Practices

• Do not guess paths: If you need a script, retrieve its location from memory_procedural or memory_toolbox.
• Pre-fetch: Always fetch context before proposing architectural designs in implementation_plan.md.
• Tag the Memory: In the future when triggering tool calls (like creating an issue), ensure the description explicitly mentions the entities so the background sessionEnd hook writes it clearly to the cognitive index.