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 1 170 updated this month 93 from vendors
Use this skill for web search, extraction, mapping, crawling, and research via Tavily’s REST API when web searches are needed and no built-in tool is available, or when Tavily’s LLM-friendly format is beneficial.
| Monitor blockchain mempools for pending transactions, gas analysis, and MEV opportunities. Use when analyzing pending transactions, optimizing gas prices, or researching MEV. Trigger with phrases like "check mempool", "scan pending txs", "find MEV", "gas price analysis", or "pending swaps".
| Execute Exa findSimilar, getContents, answer, and streaming answer workflows. Use when finding pages similar to a URL, retrieving content for known URLs, or getting AI-generated answers with citations. Trigger with phrases like "exa find similar", "exa get contents", "exa answer", "exa similarity search", "findSimilarAndContents".
| Implement Exa search result processing, content extraction, caching, and RAG context management. Use when handling search results, implementing caching, building citation pipelines, or managing content payloads for LLM context windows. Trigger with phrases like "exa data", "exa results processing", "exa cache", "exa RAG context", "exa content extraction".
| Monitor cross-chain bridge TVL, volume, fees, and transaction status across networks. Use when researching bridges, comparing routes, or tracking bridge transactions. Trigger with phrases like "monitor bridges", "compare bridge fees", "track bridge tx", "bridge TVL", or "cross-chain transfer status".
| This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity".
| Survey Creator - Auto-activating skill for Business Automation. Part of the Business Automation skill category.
| This skill enhances Claude's ability to conduct web research and translate findings into actionable GitHub issues. It automates the process of extracting key information from web search results and formatting it into a well-structured issue, ready for team action. Use this skill when you need to research a topic and create a corresponding GitHub issue for tracking, collaboration, and task management. Trigger this skill by requesting Claude to "research [topic] and create a ticket" or "find [information] and generate a GitHub issue".
| Research competitor YouTube channels, niches, and trending topics for your content strategy. Use this skill whenever the user says "research channels", "analyze competitors", "find trending topics", "niche analysis", "competitive research", "what are other creators doing", "scrape YouTube channels", or wants to understand the competitive landscape for a specific tool or topic area. Use when working with yt research. Trigger with 'yt', 'research'.
Answer a question using the compiled wiki knowledge base, synthesising a response with [[wikilink]] citations. Always use this skill when the user says /wiki-query, 'what does my wiki say about', 'what do I know about', 'check my notes on', 'search my wiki for', or 'what's the current state of [topic] in my notes'. Also use when the user asks a question and signals they want to draw on their own accumulated knowledge rather than general knowledge - phrases like 'what have we worked on regarding', 'based on my notes', 'what's my thinking on', or any question where the answer should come from personal knowledge rather than the model. Optionally files valuable answers as new wiki pages. When in doubt whether personal knowledge retrieval is being requested, use this skill. Requires filesystem read access.
Distil a chat thread, research session, or working document into a structured wiki page capturing the current state of knowledge. Always use this skill when the user says /wiki-crystallize, 'save this to my wiki', 'capture what we've worked out', 'write this up as a wiki page', 'update my wiki from this conversation', 'we've covered a lot', 'before I close this chat', or 'let's wrap up this thread'. Also use proactively when the conversation has covered significant ground and capturing the findings would orient future sessions, the context is getting heavy and insights risk being lost, or the user signals they are ending a working session - even if they don't say 'wiki'. The chat is the scaffolding; the wiki page is the artefact. When in doubt whether a session wrap is being requested, use this skill. Requires filesystem read/write access.
Use when the user wants to research vulnerabilities and create opengrep/semgrep SAST rules, conduct security research on CVEs or CWEs with web search, generate detection rules from vulnerability analysis, or build comprehensive security scanning coverage for a codebase.
| Performs comprehensive due diligence on a cryptocurrency using CoinMarketCap MCP data. Use when users ask about a specific coin beyond just its price. This includes questions like "what is [coin]", "is [coin] legit", "analyze [coin]", tokenomics questions, holder distribution, or any request for deep information about a single cryptocurrency.
>- End-to-end research workflow skill for investment analysts and policy researchers. Three scope modes the user picks at trigger time — light (4-5 page decision memo, ~15 min, 0 charts), medium (12-15 page topic brief, ~1 h, 6-10 charts), heavy (flagship report 30-40 pages / 15k+ words, ~2-3 h, 25-35+ charts, multi-stage workflow, multi-LLM, PDF + Word + WeChat + HTML derivations). Reports default to English; the AI replies in the user's chat language. Battle-tested on real macro/policy/equity reports (e.g. Saudi Vision 2030 deep-dive). Triggers when user types /analyst-research, /flagship-research, or describes needs like "research report", "topic analysis", "investment research", "做研报", "投研报告", "主题分析", "深度分析", "policy topic-brief), slide decks (use deckster-slide-generator), one-shot Q&A.
把一条平台观察、政策解读或产品判断编译成「立体品牌图标横幅」生图提示词：近白影棚虚空里悬一枚厚圆角立体 App 图标，图标上是真实品牌 logo，左侧压三层中文文字（小标签 / 反白块大标题 / 灰副标题），5:2 超宽横板。用户提到品牌图标封面、平台观察封面、3D App 图标横幅、logo 立体封面、政策解读头图、brand icon banner，或要给一组平台/产品文章成套做同骨架换品牌的横幅时使用。先调研品牌当前 logo 版本，再编译单条确定性提示词。不用于插画风格、写实摄影、信息图或纯字体海报。
把文章、观点或产品主张编译成「一个实物替一句判断」的手作拟物静物提示词——剪纸质感、微缩场景、轻拟物、克制静物摄影感。用户提到拟物风封面、拟物风章节图、剪纸质感、微缩静物、实物隐喻配图、craft diorama、cut-paper still life、object metaphor cover、diorama illustration，或要为一篇内容做封面加逐章配图时使用。默认输出封面 5:2、章节图 16:9 的完整可直接生图提示词；调色板由用户自填或按内置预设映射。不用于摄影写实、3D 渲染或人物场景插画。
把产品、服务或工作流主题编译成「暖纸拟物流程主视觉」提示词——奶油纸底上，扁平UI卡片 + 写实拟物道具 + 半写实绘画人物三种质感共存，用左→右流程图讲一个"输入→核心→标准→交付→回流"的叙事，配衬线大标题。用户提到拟物流程图、纸感hero图、官网主视觉、营销主图、SaaS hero、拟物UI配真人、产品叙事图、landing page key visual、workflow hero，或给出一张同风格参考图要求做同款时使用。不用于纯扁平图标风、写实摄影、3D渲染或产品UI截图。
把主题、句子、物件、情绪或文章摘要编译成「安静纸面标本」封面提示词：大留白陈纸背景、单一可想象的主体（十大意象家族任选）、中文手写大标题、一个落在主体上的高饱和色点、复印/网点/铅笔/凸版/扫描纸质感。用户提到纸面标本、留白封面、陈纸封面、单物件封面、手写标题封面、极简纸感海报、安静编辑设计、quiet paper specimen，或要把随笔、情绪、记忆向主题做成安静留白封面时使用。先内部选模块，再输出一条确定性提示词，不展示模块菜单。不用于满版商业海报、信息密集封面或其他设计流派。
当用户要写出高质量 Goal Prompt 和交付后继续进化用的 Loop Prompt，把模糊、战略性、多步骤、证据不足、持续/自动化或容易跑偏的请求整理成可执行、可验证、可暂停且带时间参数的目标契约时使用。适用于写 goal、优化任务提示词、明确 done/success criteria、deep research 后定战略、大改前 inventory、修复跑偏计划、识别可委托的重复 workflow、为 Codex 或 Claude Code 准备执行任务；默认只生成提示词，不执行 goal 或 loop，也不创建自动化。
Find the papers that answer a research query with Firecrawl Research, using semantic search, semantic and structural expansion, and in-body verification. Always use this skill for any literature-finding / paper-retrieval task — single-paper lookups or full multi-paper sets.
> Focused Signals scout for PostHog projects running surveys. Watches active surveys for score regressions (NPS / CSAT / rating drops), response-volume drops, abandonment spikes, and targeting drift, AND aggregates open-text responses into recurring themes the team should know about (clusters of complaints, praise, feature requests). Emits findings only when a theme or anomaly clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
Create and configure surveys in PostHog through guided conversation. Use this skill when a user wants to create a survey, collect user feedback, run NPS/CSAT/CES/PMF surveys, gather product feedback, or understand user sentiment. The skill guides Product Managers through survey design by matching their goals to proven templates (or creating custom surveys), then configuring targeting and scheduling before creating via PostHog MCP tools.
Query 40+ bioinformatics web services through one consistent Python API with bioservices (UniProt, KEGG, ChEMBL, Reactome, Ensembl, NCBI and more). Use when a workflow must hit multiple databases together, map identifiers across services, or run cross-database analyses — for quick single-database lookups use gget, for sequence and file manipulation use biopython. Part of the AlterLab Academic Skills suite.
Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.
Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
Manipulate biological sequences, parse FASTA/GenBank/PDB files, run phylogenetics, and access NCBI/PubMed programmatically via Biopython (Bio.SeqIO, Bio.Entrez, Bio.PDB, Bio.Blast). Use when scripting custom bioinformatics pipelines, batch-processing sequence files, automating BLAST, or fetching records from Entrez — for quick one-off database lookups use gget, for unified multi-service integration use bioservices. Part of the AlterLab Academic Skills suite.
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the QC, normalization, clustering, UMAP, and differential-expression analysis pipeline prefer alterlab-scanpy instead, and for RNA velocity from spliced/unspliced layers prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.
Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.
Predict genome-wide functional genomics tracks from DNA sequence with Borzoi (Linder 2025) — a sequence-to-function model outputting RNA-seq, CAGE, ATAC, and ChIP coverage across long context, used to score non-coding and regulatory variant effects. Use when predicting functional tracks from a DNA sequence, scoring a non-coding/regulatory variant's effect on expression or chromatin, or doing in-silico mutagenesis of a locus. To LOOK UP a variant's population frequency prefer alterlab-gnomad; for its clinical significance prefer alterlab-clinvar; for protein-structure effects prefer alterlab-alphafold; for single-cell foundation models prefer alterlab-scgpt. Part of the AlterLab Academic Skills suite.
Infer gene regulatory networks (GRNs) from expression matrices using arboreto's scalable GRNBoost2 and GENIE3 tree-ensemble algorithms with Dask-distributed computation. Use when analyzing bulk or single-cell RNA-seq transcriptomics to map transcription-factor-to-target-gene regulatory interactions, build adjacency networks, or run the GRN-inference step of a SCENIC pipeline on large datasets. Part of the AlterLab Academic Skills suite.
Query the CZ CELLxGENE Census (61M+ cells) programmatically via cellxgene-census and TileDB-SOMA, slicing expression by tissue, disease, or cell type and returning AnnData. Use when pulling reference single-cell RNA-seq data from the largest curated public atlas, running population-scale queries, or benchmarking your data against a reference — for analyzing your own dataset use scanpy or scvi-tools. Part of the AlterLab Academic Skills suite.
Parse and write FCS (Flow Cytometry Standard) files v2.0-3.1 with FlowIO — extract event data as NumPy arrays, read $-keyword metadata and channel/parameter definitions, and convert events to CSV or pandas DataFrame. Use when loading raw .fcs flow-cytometry files, inspecting channels and metadata, or preprocessing cytometry data for downstream gating and analysis. Part of the AlterLab Academic Skills suite.
Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles (computeMatrix, plotHeatmap, plotProfile). Use for coverage tracks, signal heatmaps/profiles, normalization (RPGC/CPM/RPKM), and effective-genome-size lookups for ChIP-seq, ATAC-seq, MNase-seq, or RNA-seq. NOT for per-read/CIGAR/MAPQ BAM record access — that is pysam. Part of the AlterLab Academic Skills suite.
Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.
Run ESM protein language models — ESM3 for generative multimodal protein design across sequence, structure, and function, and ESM C for efficient embeddings and representations — locally or via the cloud Forge API. Use when working with protein sequences, structures, or function prediction, designing novel proteins, generating protein embeddings, performing inverse folding, or doing protein-engineering tasks. Part of the AlterLab Academic Skills suite.
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and metabolic-engineering analyses on SBML genome-scale models. Part of the AlterLab Academic Skills suite.
Manipulate, annotate, and render phylogenetic trees programmatically with the ETE Toolkit (ete3) — parse and edit Newick/NHX, detect duplication/speciation events, infer orthology and paralogy, query NCBI taxonomy, and export PDF/SVG figures. Use when traversing or reformatting tree files, doing phylogenomic comparative analysis, or producing publication tree graphics in Python. Part of the AlterLab Academic Skills suite.
Run fast one-liner queries to 20+ bioinformatics databases from the gget CLI or Python — gene info (Ensembl), BLAST, AlphaFold structures, Enrichr enrichment, and more. Use for quick interactive lookups of genes, sequences, structures, or pathways — for batch processing or advanced BLAST use biopython, for multi-database Python workflows use bioservices. Part of the AlterLab Academic Skills suite.
Analyze and engineer protein glycosylation — scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and reach curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). Use when identifying or designing glycosylation sites, optimizing therapeutic-antibody or biologic glycoforms, or doing glycoprotein engineering and vaccine-design work. Part of the AlterLab Academic Skills suite.
Manage, annotate, and trace biological data with LaminDB, an open-source FAIR data framework that makes datasets queryable, versioned, and reproducible. Use when registering or querying biological datasets (scRNA-seq, spatial, flow cytometry), validating and curating data against ontologies (genes, cell types, diseases, tissues), tracking data lineage and computational workflows, building data lakehouses, or wiring integrations with Nextflow, Snakemake, W&B, or MLflow. Part of the AlterLab Academic Skills suite.