Research Claude Skills
2 269 research skills from 396 authors. They find sources and get you up to speed on unfamiliar ground. Half of them fit into 2 278 tokens or less — that is what one costs your context window when the agent loads it. 663 ship runnable scripts rather than instructions alone. 4 of them cannot work without an MCP server, most often rube. We also found 264 copies of these same skills sitting in other people's repositories — counted once here, not 264 times.
2 269 unique 396 authors 1 169 updated this month 93 from vendors
> recent-events tracking, last call's Q&A, and a key-things-to-watch framework for an upcoming research report (on request). Covers beat/miss, segments, margins, guidance, estimates, valuation. US / HK / A-share. Use whenever the user wants an earnings preview or a "Q1/Q2/Q3/Q4 results", "earnings report", "post-earnings analysis", "beat/miss", "guidance update", "earnings preview", "pre-earnings", "what to watch this earnings", "before earnings", "财报分析", "业绩更新", "季度业绩", "季报", "年报", "盈利分析", "财报点评", "财报前瞻", "业绩前瞻", "财报预览", "上季度指引", "財報分析", "業績更新", "季度業績", "季報", "年報", "財報點評", "財報前瞻", "業績前瞻", "財報預覽".
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Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.
Build a sourced retirement-plan prospecting brief with PlanProvider.Pro MCP data. Use when a user wants to research target employers, identify their current retirement-plan providers, or prioritize outreach based on Form 5500-derived plan signals.
Connect and use the PlanProvider.Pro Form 5500 MCP from Claude, Codex, Cursor, or another MCP-compatible agent. Use when a user wants to find a company retirement plan, search retirement plan providers, retrieve a provider profile, identify a plan's service providers, or ground Form 5500 research in PlanProvider.Pro source records.
Maintain a review-first Markdown knowledge base for Codex practices with source provenance, engineering capture, citation-aware queries, explicit archive and promotion, and deterministic linting. Use when asked to capture a durable engineering lesson, ingest Codex research, query or archive what the repository knows, check wiki health, reconcile conflicting guidance, or promote verified knowledge.
| Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
| Academic research assistant for literature reviews, paper analysis, and scholarly writing. analyzing methodologies, formatting citations, or when user mentions academic research, scholarly writing, papers, or scientific literature.
| Comprehensive research assistant that synthesizes information from multiple sources with citations. from multiple perspectives, or when user mentions research, investigation, or needs synthesized analysis with citations.
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
>- Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv, Scopus, ScienceDirect). Use when the user needs coordinated multi-step literature workflows beyond a single MCP call. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.
>- Add strict Nature/CNS citations to manuscript text by splitting long passages into citable segments, searching only accepted flagship and subjournal titles from Nature Portfolio, the AAAS Science family, and Cell Press, filtering by publication time range, and exporting one reference-manager-ready output by default. Use this skill whenever the user asks to input text and automatically get references, add citations to a paragraph/manuscript, find Nature-series or CNS support for statements, create text-to-reference correspondence, "分段引用", "自动给出引用", "Nature系列引用", "CNS及子刊", "支撑文献", "补引用", "找引用", or export EndNote/RIS/ENW/Zotero RDF. Also trigger on general academic-writing citation needs even without the word "Nature", such as adding references while writing a paper, finding sources/literature for a claim, building a reference list, citation/referencing for academic writing, and Chinese phrasings like 学术写作引用、写论文加引用、写paper找文献、加参考文献、配文献、引用文献、文献支撑.
>- 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 preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.
>- 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, figures4papers-style matplotlib 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 generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 论文配图、学术写作配图、科研绘图、科研作图、画图、作图、出图、论文图表、可视化.
Convert scientific papers, theses, technical reports, source code, figures, or research manuscripts into evidence-grounded Chinese invention patent drafts. Use when an AI agent must extract patentable technical contributions, map every claimed feature to source evidence, preserve core formulas as editable Office Math, generate claim-aligned flowcharts and methodology figures, compare a paper with an existing patent, audit support and consistency, or deliver separate Chinese DOCX files for claims, specification, abstract, and abstract figure.
Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a research paper, not only medical papers. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and runs an explicit self-review/corrective revision loop focused on figure quality, text overflow prevention, and non-template visual design before delivery. Also trigger on general academic-presentation requests even without the word "Nature", such as turning a paper into slides, building an academic talk deck, conference/defense presentations, and Chinese phrasings like 论文做PPT、论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、讲paper、读书报告PPT.
Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary. Also trigger on general paper-reading and translation requests even without the word "Nature", such as reading/translating an academic paper, literature reading, understanding a paper, and Chinese phrasings like 读论文、精读论文、论文翻译、文献翻译、文献阅读、学术阅读、帮我读这篇文章、翻译这篇paper.
>- Draft, audit, or revise point-by-point reviewer response letters for Nature-family manuscript revisions. Use when the user provides reviewer comments, editor decision letters, revision notes, response drafts, or asks how to respond to major/minor revision requests, rebuttal letters, response to reviewers, peer-review reports, 审稿意见回复, 逐点回复, 修回信, 大修回复, 小修回复, or 如何回复 reviewer. Also trigger on general peer-review response needs during academic writing/revision even without the word "Nature", such as replying to reviewers for any journal, writing a rebuttal/response letter, handling revision comments, and Chinese phrasings like 回复审稿人、审稿回复、返修、 修改稿回复、写rebuttal、回应审稿意见、应对审稿.
>- Simulate a Nature-style reviewer assessment from the referee perspective rather than an author rebuttal. Use when the user wants a pre-submission review, reviewer report, peer-review style critique, novelty/significance/technical soundness assessment, reviewer-style manuscript evaluation, 审稿人视角评估, 预审稿意见, or Nature reviewer report. Return 3 reviewer reports plus a cross-review synthesis, grounded only in the local Nature reviewer source basis. Also trigger on general pre-submission review requests during academic writing even without the word "Nature", such as getting a mock peer review for any journal, critiquing a draft as a reviewer would, assessing novelty/rigor before submission, and Chinese phrasings like 审稿人视角、模拟审稿、预审、帮我审一下论文、投稿前自审、审稿意见模拟、找论文问题.
Look up and cite verifiable GPU/compute rental reference prices (H100, A100, H200, and more; spot, on-demand, or DePIN markets) from the public FLOPS Index — no API key required. Use when the user asks what a GPU or accelerator costs to rent, wants the cheapest option for a given chip, wants to compare spot vs on-demand vs decentralized compute rates, or wants to fact-check / cite a compute price with a source they can independently verify. Keywords: GPU price, H100 cost, A100 rental, compute pricing, spot price, on-demand, DePIN, GPU-hour, accelerator rental, FLOPS index.
Work with a user's Semantic Scholar account to read Research Feeds, inspect private Library folders, add papers to folders, and resolve Semantic Scholar paper records from identifiers such as arXiv IDs.
Find papers and preprints that cite a given URL, especially blogs, docs pages, project pages, or other web content that standard citation indexes often miss. Use when the user provides a URL and wants confirmed citing works, evidence from PDF or HTML references, DOI or arXiv links, BibTeX, or a deduplicated citation report.
Conducts investigative-grade research with primary source analysis, cross-verification, and trial-level depth. Use when an album needs factual research, source material, or verification of claims.
Researches personal backgrounds, interviews, motivations, and humanizing details. Use when research needs biographical context about people involved in the album's subject.
Researches SEC filings, earnings calls, analyst reports, and market data. Use when the album subject involves financial crimes, corporate stories, or market events.
Researches archives, contemporary accounts, and timeline reconstruction. Use when the album subject involves historical events that need primary source verification.
Researches DOJ/FBI/SEC press releases, agency statements, and government sources. Use when research needs official government records or agency documentation.
Researches investigative articles, interviews, and news coverage. Use when research needs journalistic sources for cross-referencing or additional context.
Researches the subject's own words from tweets, blogs, forums, and chat logs. Use when research needs direct quotes or first-person accounts.
Researches court documents, indictments, plea agreements, and sentencing records. Use when the album subject involves legal proceedings or criminal cases.
Researches malware analysis, CVEs, attribution reports, and hacker community sources. Use when the album subject involves cybersecurity incidents or threat actors.
Researches project histories, changelogs, developer interviews, and open source documentation. Use when the album subject involves technology projects or developer stories.
Performs quality control, citation validation, and fact-checking before human review. Use after research is complete to verify all sources and claims before production.
> Automate source-grounded research with Google NotebookLM. Create notebooks from URLs, text, or local files; ask cited questions; run fast or deep web research; create articles and social drafts; and generate or download audio, video, cinematic video, slides, reports, study guides, quizzes, flashcards, mind maps, infographics, and data tables. Use when a user asks for NotebookLM, cited source analysis, research-to-content workflows, podcasts, slides, study material, artifact generation, RSS digests, or trend research.
Expand a shell into a full implementation plan. Verifies Consumes against the current codebase, runs a fresh pattern survey, escalates open questions, and fills in concrete file references and verification. Use when the user asks to \"expand a shell\", \"expand shell\", \"fill in the shell\", \"expand the shell\", or \"concretize the shell\".
Survey the codebase for analogous features, reusable utilities, and existing patterns relevant to a proposed change. Returns structured findings without writing code. Use when the user asks to \"survey patterns\", \"find existing patterns\", \"look for analogous features\", \"check how similar things are done\", \"find prior art for this change\", or needs pattern context before planning a change.
Upgrade project dependencies with breaking change research for major version updates. Use when the user asks to \"update dependencies\", \"upgrade packages\", \"upgrade dependencies\", \"update deps\", \"upgrade deps\", \"update npm deps\", \"update Swift packages\", \"cargo update\", \"go get updates\", \"bundle update\", or \"pip upgrade\".
Expand a shell into a full implementation plan. Verifies Consumes against the current codebase, runs a fresh pattern survey, escalates open questions, and fills in concrete file references and verification. Use when the user asks to \"expand a shell\", \"expand shell\", \"fill in the shell\", \"expand the shell\", or \"concretize the shell\".
Survey the codebase for analogous features, reusable utilities, and existing patterns relevant to a proposed change. Returns structured findings without writing code. Use when the user asks to \"survey patterns\", \"find existing patterns\", \"look for analogous features\", \"check how similar things are done\", \"find prior art for this change\", or needs pattern context before planning a change.
Upgrade project dependencies with breaking change research for major version updates. Use when the user asks to \"update dependencies\", \"upgrade packages\", \"upgrade dependencies\", \"update deps\", \"upgrade deps\", \"update npm deps\", \"update Swift packages\", \"cargo update\", \"go get updates\", \"bundle update\", or \"pip upgrade\".
> Write LaTeX ML/AI review articles for arXiv using the IEEEtran template and verified BibTeX citations. Use when writing, planning, or continuing an arXiv review/survey paper, or when validating and repairing citations in an existing LaTeX project.
> Post-process LaTeX project prose to improve readability through varied sentence and paragraph lengths. Removes filler phrases and unnecessary transitions while preserving all citations and semantic meaning. Use after drafting is complete, when sections read as monotonous or blocky.
Research any topic across Reddit, X, and web from the last 30 days. Get current trends, real community sentiment, and actionable insights in 7 minutes vs 2 hours manual research.
Generate an energy-optimized, time-blocked daily plan based on circadian rhythm research and GTD principles
Implements Teresa Torres' continuous discovery habits for weekly customer contact, opportunity solution trees, and assumption testing. Use when building discovery processes, conducting user research, validating assumptions, or establishing product trio workflows.
Builds feedback collection systems using Superhuman's PMF framework and YC's "talk to users" methodology. Use when implementing NPS surveys, scheduling user interviews, or measuring product-market fit.
Handles difficult colleagues and resolves conflicts using frameworks from Anneka Gupta and workplace dynamics research. Use when dealing with blocking colleagues, toxic dynamics, or maintaining relationships under stress.
Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.