richfrem/rlm-curator
plugins/rlm-factory/skills/rlm-curator/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add richfrem/agent-plugins-skills rlm-curatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 27, 2026, 7:41 AM55.0s22 files scanned
SKILL.md
- name:
- rlm-curator
- plugin:
- rlm-factory
- description:
- >
- 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-searchskill and itsquery_cache.pyscript.
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) orgemini(higher throughput) - Job: provide a job file describing the summarization task
- Files: gap list from
inventory.py --missing - Workers:
2for copilot (rate-limit safe),5for 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"
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