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Found 17 Skills
Conduct a rigorous rapid evidence assessment or systematic-lite literature review for MEL/SRHR questions. Use when Ane asks for "evidence review", "literature review", "evidence synthesis", "REA", "what does the evidence say", "what do we know about", or similar. Produces a structured brief with question framing, method, findings by theme, confidence grading, and implications for programme or evaluation design. Does not invent citations.
ChatGPT-style deep research strategy with problem decomposition, multi-query generation (3-5 variations per sub-question), evidence synthesis with source ranking, numbered citations, and iterative refinement. Use for complex architecture decisions, multi-domain synthesis, strategic comparisons, technology selection. Keywords: architecture, integration, best practices, strategy, recommendations, comparison.
Design and execute reproducible systematic reviews, scoping reviews, and other evidence synthesis processes. Use when the user asks for "conduct a systematic review", "how to conduct a scoping review", "design a screening process", or requests the rw-review-methods workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Use when a complex MEL/SRHR task requires deep evidence synthesis before planning begins. Triggered by Ann between PHASE 1 and PHASE 2 for COMPLEX tasks, or directly by Ane for standalone literature reviews.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Apply meta-analysis to synthesize effect sizes across multiple studies, assess heterogeneity, and evaluate publication bias. Use this skill when the user needs to combine findings from prior research, compare fixed-effect vs random-effects models, compute pooled effect sizes, or when they ask 'what does the overall evidence say', 'how do I combine results across studies', or 'is there publication bias'.
[Hyper] Produce a multi-source, source-backed markdown research report for fact-finding, comparisons, market/trend analysis, or evidence-backed recommendations across live web, official docs, GitHub, and local repo sources. Use when synthesis and citations are needed, not for one-source lookups.
Synthesize evidence into a structured narrative (`output/SYNTHESIS.md`) grounded in `papers/extraction_table.csv`, including limitations and bias considerations. **Trigger**: synthesis, evidence synthesis, systematic review writing, 综合写作, SYNTHESIS.md. **Use when**: systematic review 完成 screening+extraction（含 bias 评估）后进入写作阶段（C4）。 **Skip if**: 还没有 `papers/extraction_table.csv`（或 protocol/screening 尚未完成）。 **Network**: none. **Guardrail**: 以 extraction table 为证据底座；明确局限性与偏倚；不要在无数据支撑时扩写结论。
Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.
Autonomous multi-step deep research engine implementing an iterative Think → Search → Extract → Synthesize → Stop loop. The LLM drives every decision: what to search, what's relevant, what's missing, and when to stop. Produces a cited, magazine-quality report with inline citations, category- specific formatting, and research stats. Trigger when the user asks for "deep research", "research report on", "comprehensive analysis of", "look into X in depth", "write a report on X", or any question needing multi-source synthesis beyond a single search. For entity vetting/dossiers use entity-research; for news digests use news-monitoring; for source-grounded Q&A use notebooklm-mode.
You must use this when conducting PRISMA-standard systematic reviews, protocol development, or Risk of Bias assessment.
Researches internal or external ecosystem evidence for a design decision and returns decision-oriented patterns with sources. Use during design, grilling, retrospectives, architecture work, or implementation planning when the user asks to research a topic, compare external systems, inspect prior sessions, analyze patterns, or gather evidence before deciding.