plugins/obsidian-wiki-engine/skills/obsidian-rlm-distiller/SKILL.md
Distills wiki source files into the RLM summary layer (summary.md, bullets.md, deep.md) using the cheapest available LLM CLI. Routes to Copilot gpt-5-mini first, then Claude Haiku, then Gemini Flash. Never uses Ollama. Use when wiki nodes need RLM summaries generated or refreshed.
npx skillsauth add richfrem/agent-plugins-skills obsidian-rlm-distillerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Requires Python 3.8+ and at least one CLI installed: copilot, claude, or gemini.
pip install -r requirements.txt
Status: Active
Author: Richard Fremmerlid
Domain: Obsidian Wiki Engine
Replaces: rlm-distill-ollama (fully deprecated)
Distills registered wiki source files into the three-layer RLM summary structure
inside {wiki_root}/rlm/{concept}/. Delegates work to the cheapest available
LLM CLI — never a local Ollama server.
1. copilot CLI available? → use gpt-5-mini (fastest, free with Copilot Pro)
2. claude CLI available? → use claude-haiku-4-5 (fallback)
3. gemini CLI available? → use gemini-3-flash-preview (final fallback)
4. none found → exit with instructions
rlm-distill-ollamais fully deprecated. Onlyrlm-distill-agentpointing at cheap cloud models is supported.
{wiki_root}/rlm/{concept}/
summary.md ← 1-5 sentence distilled summary
bullets.md ← key idea bullets (6-10 points)
deep.md ← full multi-pass distillation
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root --source arch-docs
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root --engine claude
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root --engine gemini
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root --engine copilot
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root \
--rlm-cache-dir /path/to/project/.agent/learning/rlm_wiki_cache
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root --dry-run
distill_wiki.py calls shutil.which() for each CLI in priority order.
The first one found and authenticated is used for the entire batch:
ENGINE_PRIORITY = [
("copilot", "gpt-5-mini"),
("claude", "claude-haiku-4-5"),
("gemini", "gemini-3-flash-preview"),
]
distill_wiki.py writes summaries directly into its own RLM cache directory.
No cross-plugin script calls are made (ADR-001 compliant).
Default cache: {wiki-root}/rlm/{concept}/
To colocate with rlm-factory under .agent/learning/, pass --rlm-cache-dir:
python ./scripts/distill_wiki.py --wiki-root /path/to/wiki-root \
--rlm-cache-dir /path/to/project/.agent/learning/rlm_wiki_cache
The cache location is determined by configuration in .agent/learning/rlm_profiles.json
(the cache key of the wiki profile) — not by hard-coded cross-plugin paths.
/wiki-ingest populates new wiki nodes/wiki-query for optimal recall/wiki-rebuild full pipelinedistill_wiki.py — cheap-model fallback orchestratorraw_manifest.py — WikiSourceConfig loaderaudit.py — identifies stale/missing RLM summariesdata-ai
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Interactively prepares a targeted Red Team Review package. It conducts a brief discovery interview to determine the threat model, generates a strict security auditor prompt, compiles a manifest of relevant project files, and bundles them into a single Markdown artifact or ZIP archive ready for an external LLM (like Grok, ChatGPT, or Gemini) or a human reviewer.
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