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생산성 및 협업 · SKILL.md

storm-wiki

storm-wiki

Generate STORM Wikipedia-like articles with STORMWikiRunner, LiteLLM model configs, internet retrievers, staged execution, output inspection, callbacks, and demo-light setup guidance.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: storm-wiki description: "Generate STORM Wikipedia-like articles with STORMWikiRunner, LiteLLM model configs, internet retrievers, staged execution, output inspection, callbacks, and demo-light setup guidance." disable-model-invocation: true metadata: disco-role: operating license: MIT

STORM Wiki Article Generation

Use this sub-skill when the task is to generate, resume, inspect, or troubleshoot a STORM Wikipedia-like article with the installed knowledge-storm Python package.

When to use

  • Configure STORMWikiLMConfigs, STORMWikiRunnerArguments, and a search retriever.
  • Run STORMWikiRunner.run(...) through research, outline, article, and polish stages.
  • Inspect STORM output files such as conversation_log.json, storm_gen_outline.txt, storm_gen_article.txt, storm_gen_article_polished.txt, run_config.json, and llm_call_history.jsonl.
  • Add BaseCallbackHandler callbacks for progress reporting.
  • Distill demo-light Streamlit setup patterns without copying the full UI.

Route elsewhere

  • CSV, Qdrant, VectorRM, and user-corpus grounding belong to the vector-corpus sub-skill.
  • Collaborative discourse, warm_start, step, generate_report, mind map behavior, and Co-STORM logging belong to the co-storm sub-skill.
  • Full Streamlit UI source code and assets are not bundled here; use only the distilled setup notes in this sub-skill.

Operating map

  1. Read workflows for commands, staged/resume runs, output validation, and callback use.
  2. Read API reference for runner classes, method signatures, stage flags, callback hooks, and output-file contracts.
  3. Read model and retriever options before choosing LiteLLM models or search providers.
  4. Read demo-light notes only when asked to build or reason about the local Streamlit demo.
  5. Use troubleshooting for missing credentials, optional packages, rate limits, resume-file errors, topic sanitization, and deprecated wrapper fixes.
  6. Use the bundled helper scripts/run_storm_wiki.py for a self-contained CLI. Start with --help and --dry-run before any network or LLM call.

Minimal safe path

pip install knowledge-storm
python scripts/run_storm_wiki.py --help
python scripts/run_storm_wiki.py \
  --dry-run \
  --topic "Example topic" \
  --output-dir ./storm-results \
  --retriever bing

If the dry-run reports missing model or retriever credentials, set environment variables or pass a --secrets-file TOML before running without --dry-run.

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