Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Creates publication-quality scientific diagrams with no API key — Claude draws the diagram as code (SVG, TikZ/PGF, schemdraw for circuits), renders it locally to PNG/PDF with exact, editable labels, then reviews the render itself against a document-type quality threshold (journal 8.5/10, poster 7.0, ...) and revises until it passes (max 2 iterations). Use when the request is for a rendered technical or scientific diagram — CONSORT/PRISMA flowcharts, neural-network architectures, system/block diagrams, biological pathways, circuits, or other complex scientific visuals. Optional AI raster generation (Nano Banana 2 with Gemini review, via OpenRouter) when OPENROUTER_API_KEY is set. For general photos, illustrations, or artwork use alterlab-generate-image; for infographics use alterlab-infographics; for editable text-based Mermaid diagrams use alterlab-mermaid. Part of the AlterLab Academic Skills suite.
Orchestrates matplotlib, seaborn, and plotly with opinionated publication styles to produce journal-ready figures. Use when preparing journal-submission figures that need multi-panel layouts with bold panel labels, statistical significance annotations, error bars, colorblind-safe palettes (Okabe-Ito), or specific journal formatting (Nature, Science, Cell). Does NOT cover raw low-level plotting or fine-grained control of individual plot elements; for building custom plots from scratch or tuning every artist and rcParam prefer alterlab-matplotlib instead. Part of the AlterLab Academic Skills suite.
Diagnose ClickHouse Kafka engine health, stuck consumers, thread pool capacity, consumer lag, slow materialized views, and Kafka rack-awareness or cross-AZ traffic. Use for Kafka lag, consumer errors, rebalances, thread starvation, or unexpected cross-AZ Kafka cost.
Create an Amazon-compliant white-background main image (pure #FFFFFF background, product fills ≥85% of the frame, no text, props or watermark) from a product photo with GPT Image 2.5, then verify it with a Python check script. Use for Amazon/Walmart/eBay main images, 白底图, 白底主图, hero image, listing main photo.
Use when generating YouTube timestamps and topic summaries for long livestreams locally on a single GPU (such as NVIDIA Tesla P100 16GB, LM Studio, Ollama) without cloud API costs. For cloud/frontier models (Claude 3.5 Sonnet, Gemini Pro) with parallel subagents, use anibon-timestamper.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Use this skill any time the user wants to analyze data, create charts, or build data visualizations. This includes: sales analysis, financial modeling, cohort analysis, funnel analysis, A/B test results, KPI tracking, data reports, revenue breakdowns, user retention analysis, conversion rate analysis, CSV summarization, and dashboard creation. Also trigger when: user says 分析这组数据, 做个图表, 数据可视化, 销售分析, 漏斗分析, 留存分析, 做个数据报表. If data needs to be analyzed or visualized, use this skill.
Guidelines for making .NET libraries and applications trimming-safe and Native AOT compatible. Covers the trimming/AOT model, the MSBuild properties that enable analysis (IsTrimmable, IsAotCompatible, PublishTrimmed, PublishAot), the trimming attributes (RequiresUnreferencedCode, RequiresDynamicCode, DynamicallyAccessedMembers, UnconditionalSuppressMessage), IL2xxx/IL3xxx warning codes, and a pattern playbook: source generators and UnsafeAccessor, generated-interception diagnostic suppressors, intentional runtime scanning boundaries, trimming-safe islands and feature switches, migration analyzers, and temporary warning-approval baselines. Use when introducing trimming/AOT support, resolving IL2xxx/IL3xxx warnings, or making reflection-heavy code trimming-safe in C# / .NET codebases. For System.Text.Json AOT scenarios, see also the serialization skill.
This skill should be used when the user asks to "test API security", "fuzz APIs", "find IDOR vulnerabilities", "test REST API", "test GraphQL", "API penetration testing", "bug bounty API testing", or needs guidance on API security assessment techniques.
Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS failures) and IaC templates containing API Gateway resources. For general REST API design unrelated to AWS, do not trigger.
Apply Apple's current design language (Human Interface Guidelines + Liquid Glass, mid-2026) to any UI work — app interfaces in the iOS/iPadOS/macOS style, and marketing/landing pages in the Apple.com style (huge headlines, alternating light/dark sections, scroll-driven product reveals). Use this skill whenever the user asks for "Apple style", "Apple-like", "HIG", "iOS style", "macOS style", "Liquid Glass", "iOS 26 style", "Cupertino", a "clean premium minimal" landing page, or a product page that feels like an iPhone/Mac launch page — even if they don't say "Apple" explicitly but describe frosted glass, floating tab bars, pill buttons, giant centered headlines, or scroll-scrubbed product animations. Also use when reviewing/critiquing a design for Apple-platform fit. Works for HTML/CSS/JS prototypes, React + Tailwind, and Figma design guidance
Build, revise, and restructure academic CVs for PhD applications, research master's programs, fellowships, research internships, RA roles, and lab applications. Use this skill whenever the user asks to turn a resume into an academic CV, improve research-experience bullets, organize publications/projects/teaching/awards, identify missing evidence for research readiness, or make a CV fit research admissions expectations.
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
Automate controlled local ArcGIS Pro and ArcPy workflows through arcgis-mcp-bridge: inspect .aprx projects and file geodatabases, run geoprocessing, projection, raster, network, spatial-statistics, editing, symbology, and layout export with path and mutation guards. Use when the user explicitly names ArcGIS Pro, ArcPy, .aprx, .gdb, Esri geoprocessing, muend/arcgis-mcp-bridge, its health_check, PathGuard, or confirmation gates, or sketch-to-GIS extraction. Do not trigger for ArcGIS Online or Enterprise administration, QGIS or PyQGIS, generic open-source GIS, or live GUI control of an already-open ArcGIS Pro session.
Guide for using Argo to create polished product demo videos from Playwright scripts with AI voiceover and animated overlays. Use this skill whenever the user mentions Argo, demo videos, product demos, screen recordings with narration, Playwright video demos, voiceover generation, or wants to automate creating marketing/sales/onboarding videos from a web app. Also use when you see imports from '@argo-video/cli', files named '*.demo.ts' or '*.scenes.json', or an 'argo.config.js' in the project. Even if the user just says "record a demo" or "make a video of my app", this skill applies.
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.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Active knowledge intelligence. Runs Mine → Grow → Defrag cycle. Mine extracts signal from git/.agents/code. Grow validates existing learnings against current reality, synthesizes cross-domain insights, traces provenance chains, and identifies knowledge gaps. Defrag cleans up. Triggers: "athena", "knowledge cycle", "mine and grow", "knowledge defrag", "clean flywheel", "grow knowledge".
Improve any existing Claude Code skill, CLAUDE.md, subagent definition, slash-command, or prompt markdown file by combining web-researched best-practice rewrites with autonomous Karpathy-style binary eval loops. Use when the user wants to "auto-research", "self-improve", "auto-improve", "optimize", "modernize", or "research-improve" any existing SKILL.md, CLAUDE.md, agent file under .claude/agents/, slash-command file under .claude/commands/, or any prompt markdown file. Triggers on phrases like "use auto-research to improve skill X", "auto-improve this CLAUDE.md", "research best practices and rewrite Y", or "self-research and optimize this agent". Researches authoritative sources (Anthropic docs, anthropic.com engineering blog, anthropics/skills GitHub) in parallel sub-agents, proposes a cited rewrite, runs an autonomous binary-assertion eval loop with git commit and reset bookkeeping for testable artifacts, and asks the user to confirm only at the end with a before-and-after score and full source list.
Curated collection of DESIGN.md files from real websites. Each DESIGN.md contains complete design system specifications including color palette, typography, component styles, spacing, and visual guidelines that AI agents can use to generate pixel-perfect UI matching the source website's design. Use when: user wants to create UI that looks like a specific website (e.g., "build a page like Notion", "make it look like Linear's design"), or needs design tokens and component specifications for a particular design style. Also use when the user needs to browse or compare the design library visually before choosing a DESIGN.md.
Creates selected backend Markdown documentation from code evidence, including architecture/design docs, bounded context maps, developer onboarding, API contracts, and gap analysis for code smells, project-rule violations, DDD issues, coupling, and improvement opportunities. Use when the user asks to document a backend, map backend architecture or domains, onboard backend developers, audit backend documentation gaps, or produce objective backend docs. Do NOT use for frontend docs, generic README writing, or implementing backend changes.
Batch-replace backgrounds or recolour SKU variants across a folder of product photos (white background, brand colour, seasonal scene, colour variants) while keeping the product untouched, with resume, retries, budget cap and a CSV log. Use for 批量换背景, 批量白底, SKU 换色, colour variants, background replacement, bulk edit.
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
Provides Biopython (Bio.Seq, Bio.SeqIO, Bio.Align, Bio.Entrez, Bio.Blast, Bio.PDB, Bio.Phylo, Bio.motifs, Bio.SeqUtils, Bio.Restriction) for sequence handling, file parsing, NCBI access, BLAST, structures, and phylogenetic trees in Python. Reads, writes, and converts FASTA, GenBank, FASTQ, PDB, mmCIF, Newick, and NEXUS files. Use when manipulating or translating DNA, RNA, or protein sequences, converting sequence file formats, fetching records from NCBI via Entrez, running or parsing BLAST searches, doing pairwise or multiple sequence alignment, analyzing PDB structures, or building and editing phylogenetic trees. For quick lookups use gget; for multi-service integration use bioservices.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Use when you need to generate, revise, or compare business-reviewable OpenBKN BKN ontology modeling proposals based on PRDs, meeting minutes, process descriptions, system/data materials, BKN_Creator handoff summaries, or existing plans.
Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
Helps agents write and review Scala Cats Effect Resource code. Use for preventing resource leaks and use-after-release bugs, modeling disposable values with Resource, designing Resource-returning factories and constructor-injected services, wrapping Java AutoCloseable values, composing resources, and handling cancellation-safe release.
CLI tools execution specification (gemini/claude/codex/qwen/opencode) with a unified prompt template, mode options, and auto-invoke triggers for code analysis and implementation tasks. Supports configurable CLI endpoints for analysis, write, and review modes.
Provides a workflow for building and reviewing high-quality React and React Native UI with the Coinbase Design System (CDS). Use this skill to create UI or make visual changes to existing UI with CDS components, tokens, style props, icons, and illustrations. Use it for explicit CDS-adherence audits and reviews, not non-visual logic, data, analytics, navigation, tests, generated code, or refactoring. Examples: "build this screen", "fix spacing on this component", "replace this UI with CDS components", "perform a CDS audit on our changes", "check our codebase for CDS adherence", "does this feature use CDS well?"
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.