| name | rlm-curator |
| plugin | rlm-factory |
| 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.
|
| allowed-tools | Bash, Read, Write |
Dependencies
This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
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 |
|---|
swarm_run.py | The Writer (Swarm) — automated batch summarization |
inject_summary.py | The Writer (Single) -- direct agent-generated injection |
inventory.py | The Auditor -- coverage reporting |
cleanup_cache.py | The Janitor -- stale entry removal |
rlm_config.py | Shared Config -- manifest & profile mgmt |
Searching the cache? Use the rlm-search skill and its query_cache.py script.
Architectural Constraints (The "Electric Fence")
The RLM Cache is an optimized architecture producing isolated Markdown files per component.
❌ WRONG: Manual Cache Manipulation (Negative Instruction Constraint)
NEVER manually create the .agent/learning/rlm_summary_cache/*.md files using raw bash or tool blocks. Doing so could result in skipped indexing or lost metadata fields.
✅ CORRECT: Curatorial Scripts
ALWAYS use inject_summary.py or swarm_run.py to write to the cache directories. These scripts handle the atomic file writing and schema consistency perfectly.
📂 Execution Protocol
1. Assessment (Always First)
python ./scripts/inventory.py --type legacy
Check: Is coverage < 100%? Are there missing files?
2. Retrieval (Read -- Fast)
Use the rlm-search skill for all cache queries:
python ./scripts/query_cache.py --profile plugins "search_term"
python ./scripts/query_cache.py --profile tools --list
3. Distillation (Write)
Option B: Zero-Cost Swarm (Preferred for bulk > 10 files)
Use the Copilot swarm (free, gpt-5-mini) or Gemini swarm (free).
Delegate to the agent-loops:agent-swarm skill, providing:
- Engine:
copilot (free default) or gemini (higher throughput)
- Job: provide a job file describing the summarization task
- Files: gap list from
inventory.py --missing
- Workers:
2 for copilot (rate-limit safe), 5 for gemini
Option C: Manual Agent Injection (< 5 files)
python ./scripts/inject_summary.py \
--profile project \
--file path/to/file.md \
--summary "Your dense summary here..."
4. Cleanup (Curate)
python ./scripts/cleanup_cache.py --profile project --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"
