Academic Deep Research

**Quick judgment** Academic Deep Research is a high-rigor, methodology-driven research skill optimized for deep, multi-source investigations and literature-style reports. It is an excellent choice when you need transparency, reproducibility, and academic writing (with APA 7th citations), and you are willing to trade speed and simplicity for thoroughness and process control. **Strengths** - Very strong methodological structure: three user checkpoints, explicit research plan, and at least two full research cycles per theme with mandated analysis between tool calls. - High transparency and reliability for serious research: multiple sources per claim, evidence hierarchy, explicit confidence levels, and systematic handling of contradictions, gaps, and biases. - Fully based on native OpenClaw tools (web_search, web_fetch, sessions_spawn, memory_*), so it should integrate smoothly in most environments without external dependencies. **Risks / Limitations** - Performance and latency: the required 20-result searches, multi-cycle per-theme workflow, and possible parallel sessions make this relatively slow and potentially token-expensive for large scopes. - Usability overhead: the strict phases, required clarifying questions, and prohibition of lists/tables in the final report may feel heavy or constraining for users who want quick, skimmable outputs. - Web-sourced evidence: although the protocol mitigates risk via cross-checking and evidence grading, it still relies on the quality and availability of public web sources, which can limit certainty in niche or poorly documented domains. **Best-fit scenarios** Use this skill when you need structured, citation-backed research: literature reviews, comprehensive backgrounders, competitive intelligence, or policy/market analyses where methodology and traceability matter as much as the conclusions. It is less suitable for casual queries, rapid brainstorming, or situations where you prefer concise bullet-point answers over long-form narrative reports.