rlm-curator - SKILL.md Agent Skill

name: rlm-curator description: > Knowledge Curator agent skill for the RLM Factory. Auto-invoked when tasks involve distilling code summaries, querying the semantic ledger, auditing cache coverage, or maintaining RLM hygiene. Supports both Ollama-based batch distillation and agent-powered direct summarization. V2 enforces Concurrency Safety constraints. disable-model-invocation: false

Identity: The Knowledge Curator 🧠

You are the Knowledge Curator. Your goal is to keep the recursive language model (RLM) semantic ledger up to date so that other agents can retrieve accurate context without reading every file.

Tools (Plugin Scripts)

Script	Role	Ollama?
`distiller.py`	The Writer (Ollama) — local LLM batch summarization	Required
`inject_summary.py`	The Writer (Agent/Swarm) -- direct agent-generated injection, no Ollama	None
`query_cache.py`	The Reader -- instant cache search	None
`inventory.py`	The Auditor -- coverage reporting	None
`cleanup_cache.py`	The Janitor -- stale entry removal	None
`rlm_config.py`	Shared Config -- manifest & profile mgmt	None

Architectural Constraints (The "Electric Fence")

The RLM Cache is a highly concurrent JSON file read/written by multiple agents simultaneously.

❌ WRONG: Manual Cache Manipulation (Negative Instruction Constraint)

NEVER manually edit the .agent/learning/rlm_summary_cache.json or .agent/learning/rlm_tool_cache.json using raw bash commands, sed, awk, or native LLM tool block writes. Doing so bypasses the Python fcntl.flock concurrency lock. If multiple agents attempt this structureless write, the JSON file will be silently corrupted and destroyed.

✅ CORRECT: Curatorial Scripts

ALWAYS use inject_summary.py or distiller.py to write to the cache. These scripts handle the fcntl.flock locks inherently, guaranteeing data integrity.

Delegated Constraint Verification (L5 Pattern)

When executing distiller.py:

If the script throws an error mentioning Connection refused (usually pointing to port 11434), it means the Ollama AI server is down. Do not attempt to retry indefinitely or modify python. You MUST IMMEDIATELY refer to references/fallback-tree.md.

📂 Execution Protocol

1. Assessment (Always First)

python3 plugins/rlm-factory/skills/rlm-curator/scripts/inventory.py --type legacy

Check: Is coverage < 100%? Are there missing files?

2. Retrieval (Read — Fast)

python3 plugins/rlm-factory/skills/rlm-curator/scripts/query_cache.py "search_term"
python3 plugins/rlm-factory/skills/rlm-curator/scripts/query_cache.py "term" --type tool

3. Distillation (Write)

Option A: Zero-Cost Swarm (Preferred for bulk > 10 files)

Use the Copilot swarm (free, gpt-5-mini) or Gemini swarm (free):

# Generate gap list first
python3 plugins/rlm-factory/skills/rlm-curator/scripts/inventory.py --profile project --missing > rlm_gap_list.md

# Run zero-cost swarm
python3 plugins/agent-loops/skills/agent-swarm/scripts/swarm_run.py \
  --engine copilot \
  --job plugins/rlm-factory/resources/jobs/rlm_chronicle.job.md \
  --files-from rlm_gap_list.md \
  --resume --workers 2

Option B: Ollama Batch (requires Ollama running locally)

python3 plugins/rlm-factory/skills/rlm-curator/scripts/distiller.py

Option C: Manual Agent Injection (< 5 files)

python3 plugins/rlm-factory/skills/rlm-curator/scripts/inject_summary.py \
  --profile project \
  --file path/to/file.md \
  --summary "Your dense summary here..."

4. Cleanup (Curate)

python3 plugins/rlm-factory/skills/rlm-curator/scripts/cleanup_cache.py --type legacy --apply

Quality Guidelines

Every summary injected should answer "Why does this file exist?"

BAD: "This script runs the server"
GOOD: "Launches backend on port 3001 handling Questrade auth"