Skill

rlm-curator

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.

From rlm-factory

Install

Run in your terminal

npx claudepluginhub richfrem/agent-plugins-skills --plugin rlm-factory

Tool Access

This skill is limited to using the following tools:

BashReadWrite

Supporting Assets

View in Repository

acceptance-criteria.md

assets/diagrams/rlm_late_binding_flow.mmd

assets/diagrams/rlm_tool_enrichment_flow.mmd

evals/evals.json

evals/results.tsv

fallback-tree.md

references/BLUEPRINT.md

references/RLM_ARCHITECTURE.md

references/gap_analysis_rlm_v1.md

references/prompt.md

references/research-summary.md

requirements.txt

resources/distiller_manifest.json

resources/manifest-index.json

resources/prompts/rlm/rlm_summarize_general.md

resources/prompts/rlm/rlm_summarize_tool.md

resources/rlm_manifest.json

scripts/cleanup_cache.py

scripts/debug_rlm.py

scripts/distiller.py

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Stats

Parent Repo Stars1

Parent Repo Forks1

Last CommitApr 4, 2026

Actions

View Source View Plugin View on GitHub View README

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	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
`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

Searching the cache? Use the rlm-search skill and its query_cache.py script.

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 ./fallback-tree.md.

📂 Execution Protocol

1. Assessment (Always First)

python3 ./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:

python3 ./scripts/query_cache.py --profile plugins "search_term"
python3 ./scripts/query_cache.py --profile tools --list

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).

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 B: Ollama Batch (requires Ollama running locally)

python3 ./scripts/distiller.py

Option C: Manual Agent Injection (< 5 files)

python3 ./scripts/inject_summary.py \
  --profile project \
  --file path/to/file.md \
  --summary "Your dense summary here..."

4. Cleanup (Curate)

python3 ./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"