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name: co-storm description: "Operate collaborative Co-STORM workflows in knowledge_storm: configure models and retrievers, warm start a shared mind map, step through user/system turns, generate reports, and persist state/logs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Co-STORM operating sub-skill

Use this sub-skill when the task is about collaborative Co-STORM: human-in-the-loop knowledge curation with a warm-started discourse, moderator/multi-expert turn policy, dynamic mind map, final report generation, and state/log export.

Do not use this sub-skill for the non-collaborative STORM article pipeline; route that to sub-skills/storm-wiki. Do not use it for building a VectorRM/Qdrant corpus; route that to sub-skills/vector-corpus unless the caller has already supplied a ready retriever object for CoStormRunner.

Operating map

1. Install the public package in the active Python environment: pip install knowledge-storm.
2. Configure model and embedding credentials. Co-STORM creates an Encoder during CoStormRunner.__init__, so ENCODER_API_TYPE and the matching embedding credentials must be set before runner construction.
3. Select an internet retriever: bing, you, brave, serper, duckduckgo, tavily, or searxng.
4. Create CollaborativeStormLMConfigs, RunnerArgument, LoggingWrapper, retriever, optional callback handler, then CoStormRunner.
5. Call warm_start() before step(). step() assumes conversation_history is non-empty.
6. Alternate user injection (step(user_utterance="...")) and system observation (step()), then reorganize the knowledge base and call generate_report().
7. Persist report.md or report.txt, instance_dump.json, and log.json.

Bundled references

• Workflows: concrete CLI and Python workflows for dry-run, warm start, user injection, observation, report generation, and output validation.
• API reference: tables for CollaborativeStormLMConfigs, RunnerArgument, CoStormRunner, callbacks, retrievers, turn policy fields, and output contracts.
• Logging and state: LoggingWrapper, ConversationTurn, KnowledgeBase, to_dict, from_dict, redaction, and log/state inspection.
• Troubleshooting: missing model/search/encoder keys, rate limits, warm-start cost, empty history, logging stage nesting, from_dict caveat, and empty report failures.

Bundled helper

• scripts/run_costorm.py runs a self-contained Co-STORM session using LitellmModel, supports --topic, --output-dir, --retriever, Co-STORM hyperparameters, --observe-turns, --user-utterance, and --dry-run, and writes report/state/log files. --dry-run performs no LLM or network calls.