Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Particle Image Velocimetry (PIV) analysis with OpenPIV. Use when extracting velocity fields from PIV image pairs, analyzing fluid dynamics or flow visualization experiments, cross-correlating interrogation windows, validating and replacing spurious PIV vectors, or computing vorticity, strain rate, and turbulence statistics from measured velocity fields.
OpenTelemetry Transformation Language (OTTL) for OTel Collector pipelines. Use when writing or debugging OTTL statements in the transform processor, filter processor, or routing connector in any collector configuration — context selection, path expression mistakes (`attributes` vs `resource.attributes`), `error_mode`, cardinality reduction with `keep_keys`, JSON body handling with `ParseJSON`, PII redaction with `SHA256` / `replace_pattern`, and span naming. Not for receiver/exporter connectivity, DNS, pipeline wiring, or telemetry that never reaches the processor — those are infra problems, not OTTL problems.
Reconstruct an open-source project's growth channels, key content, and stage-by-stage attribution from GitHub history and public web evidence. Use this skill when asked to research GitHub star growth, identify launch and amplification channels, explain viral milestones, find representative posts or videos, compare Product Hunt/Show HN/Reddit/KOL impact, or produce an evidence-backed OSS growth attribution report.
Ingest PDF/Markdown/TXT files into joelclaw's docs memory pipeline with Inngest durability, durable NAS artifacts, and OTEL verification. Use when adding docs, running batch reindex, reconciling coverage, or recovering stuck runs. Triggers on: 'ingest pdf', 'ingest markdown', 'docs add', 'pdf-brain ingest', 'backfill books', 'docs reconcile', 'reindex docs', 'batch reindex'.
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
When the user wants to predict future cash consumption and “Runway” using time-series AI models. Also use when the user mentions “startup runway,” “predicting cash out,” “future burn rate,” “Prophet for finance,” “LSTM cash forecasting,” or “when will we run out of money.”
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
PyGraphistry visualization: bindings, color/size/icon encodings, layout controls, static export, and privacy-safe link sharing. Use when asked to "color nodes by type", "set point size", "add icons to nodes", "layout my graph", "export graph as PNG", "share a graphistry link privately", or "url_params". Also triggers on "encode_point_color", "bind(point_label=...)", "settings(url_params=...)", "badge", "static graph image", or "iframe embed". Proactively suggest when the user has a working plot but wants to customize its appearance or control link sharing and privacy.
Control PyMOL molecular visualization through Claude Code. Use when asked to "visualize protein", "render structure", "show cartoon", "color by chain", "ray trace", "set up pymol", "install pymol", or work with molecular graphics. Handles setup, visualization commands, and publication-quality figure generation.
Evidence-driven scientific figure skill for Nature Portfolio and high-impact journals. Use it to design single and multi-panel figures from raw or summarized data, choose an appropriate graphics grammar, write Python/R plotting code, redraw existing figures, draft mechanism diagrams, write figure captions, and export editable SVG/PDF and high-resolution TIFF/PNG; it also checks data integrity, color accessibility, statistical annotations, and final-size readability. Trigger scenarios include Nature figure, scientific figure, publication plot, multi-panel figure, graphical abstract, mechanism diagram, and figure audit.
HogQL query examples and reference material for PostHog data. Read when writing SQL queries to find patterns for analytics (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces) and system data (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse). Includes HogQL syntax differences, system model schemas, and available functions.
Use when planning or reviewing production database migrations, adding columns, indexes, constraints, backfills, renames, table rewrites, or concurrent operations. Covers phased rollouts, lock behavior, rollback strategy, strong_migrations compliance, and deployment ordering for schema changes.
All-in-one Redis expertise covering data modeling and key naming, client connections (pooling, pipelining, client-side caching, timeouts), clustering and replication, Redis Search (FT.CREATE, vector and hybrid search, RAG pipelines), observability and incident triage, production security hardening, semantic caching for LLM responses via LangCache, and agent memory via Redis Iris (session events, long-term memory, background promotion). Use whenever designing, writing, reviewing, or debugging anything that touches Redis: choosing a data structure, naming keys, configuring a client, sharding across a cluster, building or tuning a search index, monitoring or diagnosing performance, hardening a deployment, caching LLM completions, or wiring persistent memory for an AI agent, merged into one skill so the right domain loads automatically without picking between separate ones.
Design and audit revenue forecasts, capacity planning, funnel assumptions, pipeline, conversion, expansion, churn, retention cohorts, gross margin, infrastructure capacity, support capacity, sales capacity, inventory/supply, scenario planning, leading indicators, and forecast governance. Use when growth plans, pricing, launches, or operations need financially credible capacity-aware planning.
Implements the GDPR Article 17 right to erasure (right to be forgotten) workflow, covering all six grounds for erasure, five exceptions, technical deletion versus anonymization decisions, and third-party notification under Article 19. Activate for erasure request, deletion request, right to be forgotten, Art. 17 queries.
Extract full text from WeChat public account articles (mp.weixin.qq.com). Use when the user provides a WeChat article URL and asks for 提取文章, 抓取公众号, 读一下这篇, or when another skill needs to read a WeChat article before processing. Bypasses WeChat's access control via MicroMessenger UA spoofing. Stdlib only, no API key required.
This skill should be used when the user asks to "create a figure", "make a scientific figure", "create a paper figure", "make a figure for my paper", "make a figure for my manuscript", "compose figure panels", "assemble figure panels", "combine figure panels", "make a multi-panel figure", "figure composition", "export a figure", "generate a PDF figure", "create a Nature-style figure", "make a journal figure", "make a publication figure", or "create a figure for my grant". The skill composes multi-panel figures at exact journal dimensions (Nature 89/183 mm, Science 55/120 mm, Cell 85/174 mm, PNAS 87/180 mm) using svgutils, validates font sizes against journal minima before export, and writes PDF/PNG via Inkscape when available or cairosvg otherwise.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Use when fixing the debris an audit found in a Shopify catalog: bulk-archive dead/discontinued/zero-inventory products, clean up catalog debris, kill ghost review stars (empty star ratings a theme still renders after a review app was uninstalled), remove orphaned metafields left by an uninstalled app, strip HTML artifacts from product descriptions (double-nested bold, fully-bold paragraphs, empty tags), or fix messed-up / mangled product descriptions in bulk. Mutating cleanup work: archive vs delete, metafieldsDelete, description rewrites. Not for FINDING what's wrong (use shopify-catalog-audit) or filling missing metadata like SEO titles and alt text (use shopify-seo-metadata).
Spring Data R2DBC for reactive database access in Spring Boot 3.x. Covers R2dbcRepository, DatabaseClient, reactive transactions, and WebFlux integration. USE WHEN: user mentions "r2dbc", "reactive database", "R2dbcRepository", "DatabaseClient", "reactive SQL", "WebFlux database", "non-blocking database" DO NOT USE FOR: blocking JDBC - use `spring-data-jdbc` or `spring-data-jpa` instead, MongoDB reactive - use `spring-data-mongodb` with reactive repository
Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
Turns existing audit or health output, or saved recurring runs, into an HTML dashboard, SVG charts or Mermaid diagrams. Triggers: visualise the findings, dashboard, chart, graph the trends. Does not run audits (run one first); for a written brief use stakeholder-brief.
This skill helps users extract full article contents from WeChat using the BrowserAct API. The Agent should proactively apply this skill when users express needs like finding full WeChat articles for specific keywords, tracking WeChat public accounts for industry trends, extracting WeChat article contents for media research, monitoring public relations on WeChat platforms, collecting competitor updates from WeChat, getting full article body from WeChat links, monitoring brand exposure on WeChat articles, retrieving structured WeChat data for sentiment analysis, summarizing daily news from WeChat, getting author and publication date for WeChat articles, or automating WeChat content extraction without scraping.
Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video
Direct ALGOPACK HI2 API workflows for Herfindahl-Hirschman market concentration, hhi_* metrics, daily EQ/FO/FX concentration rows, raw hi2 endpoints, curl, JSON/CSV handoff, simple HTML output, ISS columns/data normalization, start pagination, and interpretation bands.
Direct ALGOPACK Mega Alerts API workflows for market anomaly alerts, alert_type thresholds, reference JSON, documented EQ/FO alert endpoints, curl, JSON/CSV handoff, simple HTML output, ISS columns/data normalization, start pagination, and observed abnormal volume or price signals.
Manage the full lifecycle of Alibaba Cloud Realtime Compute for Apache Flink SQL: analyze schemas, author and validate SQL, manage Drafts, Deployments, Jobs, Savepoints, Session Clusters, Catalogs, Connectors, UDFs, variables, and deployment targets, synchronize configuration to Git, and troubleshoot logs, Metrics, Checkpoints, exceptions, plans, and backpressure. Use when the user asks about Alibaba Cloud Flink, VVR, VVP, Flink SQL, its job lifecycle, or troubleshooting. Operate the platform through the Python CLI; historical Metrics use a user-supplied VVP Cookie. Verify scope and authentication before platform operations. High-risk actions usually require confirmation; explicit confirmation for the resolved target in the same request satisfies the gate.
Performs biostatistical analyses specialized for clinical and biomedical research including survival analysis, Kaplan-Meier estimation, Cox proportional hazards regression, longitudinal data modeling, and diagnostic test evaluation; trigger when users discuss clinical outcomes, survival curves, or biomedical study statistics.
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.
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.