研究与知识
研究、检索、摘要和知识工作
Skills 列表
scholar-evaluation
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
k-dense-aidatabase-lookup
Query documented public database APIs with explicit endpoints, filters, pagination, and provenance. Use when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.
k-dense-aibgpt-paper-search
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
k-dense-ailatex-posters
Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.
k-dense-airesearch-grants
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
k-dense-ai
ppt-master
基于 AI 驱动的多格式 SVG 内容生成系统。通过多角色协作，将源文档（PDF/DOCX/URL/Markdown）转换为高质量的 SVG 页面，并最终导出为 PPTX 演示文稿。当用户提出“做PPT”、“生成PPT”、“制作PPT”、“创建演示文稿”、“制作演示文稿”或提及“ppt-master”时触发使用。
hugohe3
arize-trace
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
githubarize-instrumentation
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
githubroundup-setup
Interactive onboarding that learns your communication style, audiences, and data sources to configure personalized status briefings. Paste in examples of updates you already write, answer a few questions, and roundup calibrates itself to your workflow.
github
deep-research
通用深度研究 Agent 团队。由 13 个 Agent 构成的流水线，可针对任意主题开展严谨的学术研究。提供 8 种模式：全流程深度研究、快速简报、论文评审、文献综述、事实查核、三段式文献扫描、苏格拉底式引导研究对话，以及可选 meta 分析的系统性综述。覆盖研究问题提炼、导师级苏格拉底式引导、研究方法设计、系统性文献检索、文献来源验证、跨来源综合合成、偏倚风险评估、meta 分析、APA 7.0 格式报告撰写、主编评审、魔鬼代言人质询、伦理审查以及研究后文献动态监测。触发词包括：research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘.
imbad0202academic-pipeline
全流程学术研究管线编排器：研究 -> 写作 -> 学术诚信检查 -> 审稿 -> 修改 -> 复审 -> 二次修改 -> 终极学术诚信检查 -> 终稿定稿。高效协同 deep-research、academic-paper 和 academic-paper-reviewer，打造包含强制性学术诚信校验、两轮同行评审与可复现质量关卡的无缝 10 阶段工作流。触发词：academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로。
imbad0202
anti-reversing-techniques
理解软件分析过程中遇到的反逆向、混淆和保护技术。在分析恶意软件规避技术、为CTF挑战实现反调试保护、逆向加壳二进制文件或构建需要检测虚拟化环境的安全研究工具时，使用此技能。
wshobsonruff-recursive-fix
Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions.
githubgtm-developer-ecosystem
Build and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.
githubacquire-codebase-knowledge
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
githubgtm-enterprise-account-planning
Strategic account planning and execution for enterprise deals. Use when planning complex sales cycles, managing multiple stakeholders, applying MEDDICC qualification, tracking deal health, or building mutual action plans. Includes the "stale MAP equals dead deal" pattern.
githubgtm-product-led-growth
Build self-serve acquisition and expansion motions. Use when deciding PLG vs sales-led, optimizing activation, driving freemium conversion, building growth equations, or recognizing when product complexity demands human touch. Includes the parallel test where sales-led won 10x on revenue.
githuboo-component-documentation
Create or update standardized object-oriented component documentation using a shared template plus mode-specific guidance for new and existing docs.
githubacademic-paper
由 12 个 Agent 协作组成的学术论文写作流水线。提供 11 种工作模式（全流程/规划/大纲/修改/审稿意见辅导/摘要/文献综述/格式转换/引用检查/AI使用声明/审稿反驳审计）。支持 6 种论文类型、5 种引用格式、中英双语摘要，可导出 LaTeX、DOCX（基于 Pandoc）、PDF 及 Markdown。内置文风校准（Style Calibration）、写作质量检查（Writing Quality Check）以及带“铁律（IRON RULE）”标记的违规避坑指南。触发词包含：write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지。
imbad0202scikit-learn
使用 scikit-learn 在 Python 中进行机器学习。适用于监督学习（分类、回归）、无监督学习（聚类、降维）、模型评估、超参数调优、预处理或构建机器学习流水线。提供算法、预处理技术、流水线和最佳实践的全面参考文档。
k-dense-aimarkdown-mermaid-writing
全面的 Markdown 和 Mermaid 图表编写技能。在创建任何科学文档、报告、分析或可视化时使用。将基于文本的图表确立为默认文档标准，包含完整的样式指南（markdown + mermaid）、24 种图表类型参考和 9 种文档模板。
k-dense-aistatsmodels
用于Python的统计模型库。当您需要具有详细诊断、残差和推断的特定模型类（OLS、GLM、混合模型、ARIMA）时使用。最适合计量经济学、时间序列、带有系数表的严格推断。如需带APA报告的引导式统计检验选择，请使用statistical-analysis。
k-dense-ai
tiptap
帮助 Coding Agent 集成并使用 Tiptap 富文本编辑器。当需要基于 Tiptap 构建或修改富文本编辑器、安装扩展组件，或者实现协同编辑、划词评论、AI 辅助、文档格式转换等功能时使用此 Skill。
ueberdosisdoublecheck
AI输出三层验证流水线。提取可验证的声明，通过网络搜索找到支持或矛盾的来源，运行对抗性审查以发现幻觉模式，并生成带有来源链接的结构化验证报告供人工审核。
github