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Found 37 Skills
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.
Polish a single H3 unit file under `sections/` into survey-grade prose (de-template + contrast/eval/limitation), without changing citation keys. **Trigger**: subsection polisher, per-subsection polish, polish section file, 小节润色, 去模板, 结构化段落. **Use when**: `sections/S*.md` exists but reads rigid/template-y; you want to fix quality locally before `section-merger`. **Skip if**: subsection files are missing, evidence packs are incomplete, or `Approve C2` is not recorded. **Network**: none. **Guardrail**: do not invent facts/citations; do not add/remove citation keys; keep citations within the same H3; keep citations subsection-scoped.
Use when writing or revising scientific manuscripts, abstracts, figures, or references for journal submission and you need full-paragraph prose, scientific structure, citation-style guidance, or reporting-guideline support.
执行完整的 7 阶段深度研究流程。接收结构化研究任务，自动部署多个并行研究智能体，生成带完整引用的综合研究报告。当用户有结构化的研究提示词时使用此技能。
Bind addressable evidence IDs from `papers/evidence_bank.jsonl` to each subsection (H3), producing `outline/evidence_bindings.jsonl`. **Trigger**: evidence binder, evidence plan, section->evidence mapping, 证据绑定, evidence_id. **Use when**: `papers/evidence_bank.jsonl` exists and you want writer/auditor to use section-scoped evidence items (WebWeaver-style memory bank). **Skip if**: you are not doing evidence-first section-by-section writing. **Network**: none. **Guardrail**: NO PROSE; do not invent evidence; only select from the existing evidence bank.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
Use when "literature review", "research synthesis", "systematic review", "academic search", or asking about "find papers", "cite sources", "research gaps", "meta-analysis", "bibliography"
Use when the user needs research methodology, long-form content creation, academic-style citations, fact-checking, or evidence-based writing with proper source attribution. Trigger conditions: whitepaper drafting, research article writing, source evaluation, citation management, fact-checking protocol, case study creation, evidence-based argumentation.
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Use when normalizing BibTeX, RIS, CSL JSON, citation keys, DOI/arXiv/PMID metadata, references, unused citations, missing citations, or bibliography quality for papers and SOTA work.
Search, summarize, and synthesize economics literature