persistent-memory

ALWAYS USE THIS SKILL when handling persistent memory in this workspace, including task-start memory recall, explicit "remember" instructions, storing durable preferences/facts, and retrieving prior context. This skill owns the local memory workflow and CLI for init/sync/search/add/recent/stats.

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Install skill "persistent-memory" with this command: npx skills add ropl-btc/agent-skills/ropl-btc-agent-skills-persistent-memory

Persistent Memory

Use this skill as the single memory system for this repository.

Commands

Use either command style:

  • python3 .agents/skills/persistent-memory/scripts/memory.py <command>
  • .agents/skills/persistent-memory/scripts/pmem <command>

Supported commands:

  • init
  • sync (database-only health check)
  • cleanup-legacy
  • backfill-embeddings --batch 500
  • prune --source "<label>" [--older-than <days>]
  • search "<query>" --limit 8
  • add "<memory text>" --tags "<comma,tags>" --source "assistant"
  • recent --limit 10
  • stats

Required Workflow

  1. Initialize memory in a fresh workspace:
  • pmem init
  1. At the start of substantial tasks:
  • pmem sync (database-only health check)
  • pmem search "<topic keywords>" --limit 8
  1. When user explicitly says remember or when a durable preference/fact is learned:
  • pmem add "<memory text>" --tags "<tags>" --source "assistant"
  1. Before finalizing memory-sensitive work, verify recall state:
  • pmem stats

One-Time Migration (If Upgrading From Older Setup)

  1. Remove legacy imported rows:
  • pmem cleanup-legacy
  1. Generate vectors for existing notes:
  • pmem backfill-embeddings

Storage Rules

  • Store durable preferences, long-lived facts, stable workflows, and repeated constraints.
  • Do not store noisy one-off transient details unless requested.
  • Keep entries concise and specific.
  • Prefer tags that improve retrieval quality (preferences, calendar, comms, product).

Retrieval Rules

  • Use targeted search queries instead of broad terms.
  • Keep default --limit low unless deeper recall is needed.
  • search automatically reinforces recalled entries by updating hits and last_seen_at.
  • hits are analytics-oriented and not used as a direct ranking boost.
  • Search uses hybrid retrieval: lexical + semantic.
  • Semantic search tries sqlite-vec first and auto-falls back to Python cosine if needed.

Bootstrapping and Recovery

  • If .memory/ is missing, run pmem init.
  • pmem sync is a lightweight database-only check (no markdown import/export).
  • If semantic mode degrades, run pmem stats to inspect semantic_backend and embedding_coverage.
  • For command examples and quick troubleshooting, read references/usage.md.

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