agentic-memory

name	agentic-memory
description	This skill allows the agent to manage its own semantic memory, extracting lessons, patterns, and user preferences to improve long-term effectiveness.
trigger	at the end of each significant session, when detecting repetitive error patterns or user preferences.

Skill: Agentic Memory (Brain)

This skill enables the agent to manage its own semantic memory by extracting lessons, patterns, and user preferences to enhance long-term effectiveness.

Extraction Guidelines

The agent must actively look for the following elements during interaction:

1. Error Patterns: Solutions to bugs that took more than 3 attempts or required deep investigation.
2. Implicit Preferences: Style or architectural decisions that the user repeatedly approves.
3. Technical Hotspots: Areas of code that are difficult to test or refactor.
4. Design Decisions: Justifications for why one implementation was chosen over another.

Update Protocol (AGENT_LESSONS.md)

At the end of each significant session, the agent must:

1. Verify Path: Ensure you are NOT in antigravity-framerepo/ or scaffold/. All persistence must happen in the project root's .agent/ directory.
2. Target Metrics: Update .agent/memory/agent_metrics.json (NEVER a scaffold file).
3. Synthesize: Summarize findings in entries of at most 3 lines in AGENT_LESSONS.md.
4. Categorize: Use tags such as [TECHNICAL], [USER_PREFERENCE], [ARCHITECTURE].
5. Structure: Maintain a YAML-friendly format for future RAG integrations.

Structured Entry Example

- date: 2026-02-05
  category: TECHNICAL
  topic: QgsGeometry Mocking
  lesson: "The 'is3D' method fails in simple mocks; requires QGIS context injection."
  action: "Use QgsGeometry.fromWkt() whenever possible to avoid manual mocks."

Pre-flight Self-Audit

Before concluding any task or workflow, the agent must perform a self-audit:

1. Lessons Check: "Have I applied relevant lessons from AGENT_LESSONS.md?"
2. Context Integrity: "Does my solution follow the project's architectural standards (e.g., Core/GUI separation)?"
3. Structured Output: "Does my final response provide a clear, structured summary of what was done and tested?"

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Skill generated for the evolution of the SecInterp Agent Architecture.