| name | deep-research |
| description | Deep Research (8-step methodology) — Transform vague topics into high-quality, deliverable research reports.
Systematic fact extraction, source tiering (L1>L2>L3>L4), time-sensitivity assessment, and verifiable "Fact→Conclusion" chains.
Use when: deep research, comprehensive report, thorough investigation, concept comparison, decision support, trend analysis.
Inspired by wshuyi/deep-research + OpenAI Deep Research + HKUDS.
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Deep Research Skill
8-step methodology: problem decomposition → source tiering → fact extraction → comparison framework → derivation chain → validation → final report.
Usage
python3 _scripts/deep_research.py "REST API vs GraphQL" --methodology --sources --debate --save
python3 _scripts/deep_research.py "What are the tradeoffs of AI agents?" --save
Options
query — Research question or topic (required)--methodology — Full 8-step flow with intermediate artifacts saved to Sources/Deep Research/<topic>/--sources — Include external source recommendations--debate — Include steelmanned opposing views--top-k N — Max notes to retrieve (default: 15)--depth N — Graph expansion depth (default: 2)--save — Save final report to vault
Source Tiering
L1 (official docs/papers) > L2 (official blogs) > L3 (media/tutorials) > L4 (community/forums). Conclusions must trace to L1/L2.
Output
Sources/Deep Research - YYYY-MM-DD.md (or full artifact folder with --methodology)
