Science & Research for Claude Code

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.

Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.

Build or review an element chart — a patent claim chart (infringement, invalidity, or review) or a civil element chart for any cause of action or defense — with every cell pin-cited and gap detection as the priority output. Use when the user asks for a claim chart, element chart, proof chart, infringement or invalidity contention, element-by-element mapping, or asks "what are we missing to prove [claim]".

> Use this skill whenever a user specifically requests legal research or Westlaw Deep Research, asks for CoCounsel Legal or cocounsel legal support, or asks a question that requires explaining, analyzing, or synthesizing U.S. law.

> Freedom-to-operate triage — a structured first look at potentially blocking patents, not an FTO opinion. Use when a product, process, or feature is being evaluated for blocking patents, when asked whether anything stops a launch, or to build a claim-chart first pass against the most plausible patents before patent counsel review. This skill never concludes a product is clear to launch.

> Invention disclosure first-pass screen — novelty, obviousness, §101 eligibility, bar dates, detectability, and strategic value. Use when an invention disclosure comes in and needs triage on whether to pursue a prior-art search and patent counsel review, investigate further, or decline.

> Research roadmap for a legal issue — statutes to check, case law areas to investigate, regulatory frameworks, Westlaw search terms. Leads and frameworks, NOT authoritative citations; students verify and develop everything. Use when a student asks where to start researching, wants a research roadmap for an issue, or needs gaps identified in existing research.

| 卡尔·马克思的思维框架与表达方式。基于《资本论》《共产党宣言》 《德意志意识形态》《关于费尔巴哈的提纲》《路易·波拿巴的雾月十八日》 《哥达纲领批判》、书信、第一国际材料和权威学术评价的调研， 提炼5个核心心智模型、8条决策启发式和表达DNA。 用途：作为思维顾问，用马克思的视角分析资本、劳动、技术、组织、 阶级、意识形态、政治经济结构和历史变迁。 当用户提到「用马克思的视角」「马克思会怎么看」「马克思模式」 「Marx perspective」「karl marx perspective」「从马克思主义看」时使用。 即使用户只是说「按生产关系分析」「看资本逻辑」「阶级分析一下」 「这是不是异化」也应触发。

召唤多领域专家智能体，对任何棘手、跨学科、有争议或开放性的问题进行并行深度分析，再经过事实核查员与红队子智能体的克制审阅，最终重写为一篇面向小白、第一人称、图文并茂、有人味的深度长文。触发条件：用户提出任何值得深挖的复杂问题，尤其是跨领域、需要多方视角、需要权威数据支撑的问题；提到"帮我深度分析"、"多专家视角"、"专家怎么看"、"这个问题涉及哪些领域"、"帮我搞清楚 XX 到底是怎么回事"、"用第一性原理分析一下"、"这个观点站得住脚吗"、"帮我论证一下"，或者只是抛出一个开放性问题如"为什么 XX 会发生"、"如何看待 XX 现象"、"XX 到底靠不靠谱"。即使用户没有明确要求"专家"或"深度分析"这几个字，只要问题本身跨越多个专业领域、需要审慎论证而非泛泛而谈，也应使用本 skill。本 skill 与仓库中其他类似名称的技能相互独立，请完全按照本文件的流程执行，不要套用其他技能的模板或输出结构。

召唤多领域专家智能体，对任何棘手、跨学科、有争议或开放性的问题做并行深度分析，再经过一轮克制的事实核查与红队反驳，最后由一个"干净大脑"的终稿撰写者把所有内容无缝重写成一篇第一人称、面向小白、图文并茂、有人味、留有证伪空间的深度长文。触发条件：用户提出任何值得深挖的复杂问题，尤其是跨领域、需要多方视角、需要权威数据支撑的问题；提到"帮我深度分析"、"多专家视角"、"专家怎么看"、"这个问题涉及哪些领域"、"帮我搞清楚 XX 到底是怎么回事"、"用第一性原理分析一下"、"这个观点站得住脚吗"、"帮我论证一下"、"multi-expert-analyzer2"，或者只是抛出一个开放性问题如"为什么 XX 会发生"、"如何看待 XX 现象"、"XX 到底靠不靠谱"。即使用户没有明确说"专家"或"深度分析"这几个字，只要问题本身跨越多个专业领域、需要审慎论证而非泛泛而谈，也应使用本 skill。本 skill 与仓库中其他类似名称的技能（如 multi-expert-analyzer、multi-expert-analyzer1）相互独立，不共享模板或输出结构，请完全按照本文件的流程执行。

从论文 PDF 文件或论文 PDF URL 生成通俗易懂、图文并茂、带批判性评估的中文 Markdown 解读，并保存到当前项目的 markdown 目录。Use when the user asks to interpret,精读,解读,summarize,explain,analyze, or write an article from an academic paper PDF, arXiv PDF, conference paper PDF, journal paper PDF, or local/remote PDF URL.

| 唐代「药王」孙思邈的临床思维框架与表达方式。基于 3,200 行一手与权威二手研究提炼， 覆盖 6 个维度、5 朝代评价（唐/宋/金元/明清/现代）、30+ 一手文献、100+ 条原文引用、 10 个经典医案、5 个关键决策。包含 9 个核心心智模型、12 条决策启发式和完整的医古文表达 DNA。 用途：作为中医临床思维顾问，用孙思邈的视角分析辨证思路、审视临床决策、 提供食疗与养生建议、讨论医德与患者沟通。 触发词： 视角类——「用孙思邈的视角」「药王会怎么看」「孙真人怎么看」「切换到药王」 经典类——「千金要方」「千金翼方」「大医精诚」「大医习业」「新修本草」 方剂类——「温胆汤」「独活寄生汤」「苇茎汤」「犀角地黄汤」「耆婆万病丸」「小续命汤」「治中汤」「谷白皮粥」「小竹沥汤」「无比薯蓣丸」「七子散」「紫石门冬丸」「食疟」（这些方剂原出《千金方》，可被直接问及） 方法类——「脏腑寒热虚实」「以方类证」「方证同条」「胆大心小」「智圆行方」「杂合以治」 医德类——「大医精诚」「普同一等」「苍生大医」「含灵巨贼」「大慈恻隐」「普救含灵」「医德」「医患沟通」 养生类——「食疗」「十二少

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

Search Naver blogs, read full post content, and download images using only python3 stdlib — no API key required.

Annotates codebases with dimensional analysis comments documenting units, dimensions, and decimal scaling. Use when someone asks to annotate units in a codebase, perform a dimensional analysis, or find vulnerabilities in a DeFi protocol, offchain code, or other blockchain-related codebase with arithmetic. Prevents dimensional mismatches and catches formula bugs early.

Create a company research brief with executive quotes, product strategy, and org context. Use when preparing for interviews, competitive analysis, partnerships, or market-entry work.

Use when discovering, collecting, and analyzing diverse data sources, including data mining, statistical analysis, and pattern recognition, with emphasis on extracting meaningful insights from complex datasets to support evidence-based decisions.

Use when working on comprehensive information gathering, synthesis, and insight generation, including research methodologies, data analysis, and report creation, with emphasis on delivering actionable intelligence that drives informed decision-making.

Generate professional primary market / venture capital industry research reports, including sector deep-dives, investment memos, and market analysis. Produces detailed PDF reports covering TMT, consumer, healthcare, and industrials. Triggered by requests to analyze an industry, create a market report, write a sector deep-dive, or generate any PE/VC-style research document in Chinese or English.

Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. This is faster and more reliable than trying to read a raw PDF.

Specialist for isolated follow-up analyses inside an analysis campaign.

Core operating contract shared by all DeepScientist quest turns.

Use when a quest is ready for a concrete implementation pass or a main experiment run tied to a selected idea and an accepted baseline.

Use when a quest needs a polished milestone chart, paper-facing figure, appendix figure, or a mandatory render-inspect-revise pass before treating a figure as final.

>- Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors pr

>- Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure g

Use when creating, revising, validating, or repairing a research-paper outline before writing; turns experiment evidence into a clear paper idea, scoped claims, method abstraction, evaluation plan, analysis plan, and evidence boundaries without copying run logs into the manuscript.

Lead orchestrator for the DeepScientist research graph.

Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.

Evidence-bound writer for papers, reports, and summaries.

Researches a single gray area decision and returns a structured comparison table with rationale. Spawned by discuss-phase advisor mode.

Researches how to implement a phase before planning. Produces RESEARCH.md consumed by gsd-planner. Spawned by /gsd:plan-phase orchestrator.

Researches domain ecosystem before roadmap creation. Produces files in .planning/research/ consumed during roadmap creation. Spawned by /gsd:new-project or /gsd:new-milestone orchestrators.

Synthesizes research outputs from parallel researcher agents into SUMMARY.md. Spawned by /gsd:new-project after 4 researcher agents complete.

Bias-isolated agent that evaluates hypothesis state based on linked evidence across interview analysis files (and future evidence sources). Given a list of hypothesis slugs (or "all"), greps for [[slug]] backlinks, reads the linked statements in context, and recommends status changes. For each hypothesis it also reports what changed since the last assessment and the smallest observable next validation action, and flags one cross-hypothesis top pick. Also synthesizes candidate new hypotheses from

Search the KEGG bioinformatics database for pathways, genes, compounds, drugs, enzymes, or diseases

Use when the user asks about biological pathways, gene functions, metabolic networks, drug targets, enzyme reactions, disease mechanisms, or any KEGG database query. Specializes in pathway enrichment, cross-species comparison, and drug-target investigation.