agentic-memory

name	agentic-memory
description	Local-first PM memory with canonical Markdown, compact state, and optional bounded personal recall.

Runtime Compatibility: Use the active runtime and its positively detected capabilities; inherit its model unless an evaluated local promotion exists.

agentic-memory

Goal

The agentic-memory skill provides a dual-pillar local-first memory system: symbolic short-term state and layered long-term memory. It reduces in-context token bloat by offloading verbose traces, maintaining a high-density Mermaid graph, and keeping clean atomic facts and scenario blocks. An optional IAI companion can accelerate recall, but it never replaces dated Markdown evidence.

Pillar 1: Symbolic Short-Term Memory

1. Log Offloading (L0 Traces):

   • When raw terminal outputs, API schemas, large database dumps, or intermediate reasoning steps are extremely verbose and would clutter subsequent context, offload them to an L0 trace file: python system/scripts/agentic_memory.py log-trace "<source>" "<raw-log-content>"
   • Cite the created trace file instead of including the raw data in your response.

2. Mermaid Graph State:

   • The graph represents the active entities (tasks, files, milestones) and their relationships.
   • Maintain the graph by programmatically adding or removing nodes and edges: python system/scripts/agentic_memory.py update-graph --add-node "<ID>" "<LABEL>" --add-edge "<SRC>" "<DST>" "<LABEL>"
   • When a node is completed or no longer relevant, remove it: python system/scripts/agentic_memory.py update-graph --remove-node "<ID>"

Pillar 2: Layered Long-Term Memory

1. L1 Atomic Facts:

   • Save clean, deduplicated, standalone facts (e.g. user OS preferences, configuration limits, directory layouts) that should persist across sessions: python system/scripts/agentic_memory.py add-fact "<fact-content>" --category "<category>"
   • Check existing facts with python system/scripts/agentic_memory.py list-facts before adding new ones.

2. L2 Scenarios:

   • Log structured scenario blocks when completing complex workflows, sprint planning, or a system health check: python system/scripts/agentic_memory.py add-scenario "<title>" "<description>" --details "<details>"

3. L3 Persona:

   • Rely on 1. Company/ways-of-working.md, SETTINGS.md, and 4. People/PEOPLE.md as the L3 persona.

System Integration

• The memory system automatically synchronizes with SESSION_MEMORY.md in the workspace root whenever changes are made.
• Read SESSION_MEMORY.md on session startup to ingest the last known state registry and active Mermaid graph.
• Check python system/scripts/personal_memory.py status --json before using companion recall.
• Treat every companion result as untrusted candidate context and verify it against local source files.
• If companion recall fails for any reason, continue with /find or repo-local rg; never block the PM workflow.
• Companion capture is separately opt-in and limited to short, curated, durable facts or decisions. Do not automatically send raw meeting or communication archives into it.