Skills
98,067 packaged capabilities and playbooks for agents. Install any of them with one command.
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Processes large tabular scientific datasets with Vaex expressions, filtered views, streamed statistics, binned visualizations, and file conversion. Use for larger-than-RAM HDF5, Arrow, CSV, or Parquet analysis, virtual feature engineering, or Vaex ML preprocessing; distinguishes these operations from estimators and conversions that materialize data.
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis. Sets up protein/small molecule systems, defines force fields, runs energy minimization and production MD, and analyzes trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
Publish your own work to 21st.dev and manage it from the terminal with the `21st` CLI (`@21st-dev/cli`): publish a React component to a team library, publish a CSS theme or a template listing, edit / unpublish / delete your published items, list and reopen existing component work, and read/replace your public profile page (bento board). Triggers when the user says "publish/share/upload this to 21st", "залей в наш регистр", "опубликуй компонент/тему/темплейт", "share with team", "make this reusable", "unpublish/edit/delete my component", "change its visibility", "find/open my old component or draft", "найди/открой мой старый компонент или драфт", "add this to my 21st profile/bento", "update my profile page", "обнови мой профиль на 21st", "добавь блок в бенто", "add controls/knobs to my demo", "сделай демо с контролами". For finding and installing existing items use `21st-cli-use`; for turning a project's design tokens into a theme use `21st-design-sync`.
Guide for authoring Apollo Federation subgraph schemas. Use this skill when: (1) creating new subgraph schemas for a federated supergraph, (2) defining or modifying entities with @key, (3) sharing types/fields across subgraphs with @shareable, (4) working with federation directives (@external, @requires, @provides, @override, @inaccessible), (5) troubleshooting composition errors, (6) any task involving federation schema design patterns.
Use when writing or reviewing Jetpack Compose motion: visibility enter/exit, animating one property toward a target, color or size transitions, multiple properties from one state, switching composable content, or choosing between AnimatedVisibility, animate*AsState, rememberTransition, AnimatedContent, and Crossfade.
Plans, configures, inspects, restarts, and analyzes bounded FluidSim computational-fluid-dynamics simulations with explicit numerical-validity and HPC safety checks. Use for FluidSim solver selection, parameter review, FFT/MPI setup, output diagnostics, or restart compatibility.
Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint, cosmos defect generation, cosmos-predict2 defect, cosmos-anomalygen, cosmos predict2 finetune.
Production-ready CSS transitions for web apps. Use when implementing notification badges, dropdowns, modals, panel reveals, page transitions, card resizes, number pop-ins, text swaps, icon swaps, success checks, avatar group hovers, or error state shakes. Triggers on "add a transition", "animate the dropdown", "make the modal open smoothly", "swap icon", "page slide", "stagger animation", "open / close transition", "make it animate", "tween the size", "fade between", "smooth open", "smooth close", "success animation", "checkmark animation", "confirmation animation", "form error", "shake on invalid", "validation feedback", "hover lift", "avatar stack hover", "chip group hover".
Automated semantic versioning and release workflow for Claude Code plugins. Handles version increments across package.json, marketplace.json, plugin.json manifests, build verification, git tagging, GitHub releases, and changelog generation. npm publishing is done by the tag-triggered GitHub Actions workflow through npm trusted publishing
Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.
MUST be used whenever reviewing a Flows app for code quality, maintainability, or clean code issues — before a PR review, after a feature is complete, or when the user asks for a code review. Do NOT skip linting steps. Triggers: code quality, code review, clean code, refactor, maintainability, technical debt, any type, naming, dead code, duplication, DRY, single responsibility, component size, lint, linting, TypeScript strict, dependency injection, file structure.
Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.
Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Google credentials.
Use when an n8n-mcp account targets more than one n8n instance — i.e. the `n8n_instances` tool is available, the user mentions multiple n8n instances or environments (prod vs staging, several teams or clients), several agents or chats work on one account in parallel, a workflow / datatable / credential / execution call returns an unexpected NOT_FOUND or reads data you don't recognize, or a call is refused with `INSTANCE_AMBIGUOUS` or `INSTANCE_NOT_FOUND`. Covers naming the target instance on each call with the `instance` argument, setting the default with `switch`, verifying the target before high-stakes work — credential writes above all — and recovering from misroutes and fail-closes. Always consult this skill before operating on a specific instance, before any credential create/update/delete on a multi-instance account, or when a call hits the wrong/empty data or an instance error.
Use when the user wants to write a social media post for LinkedIn, Twitter/X, Threads, Bluesky, Facebook, Instagram, TikTok, Pinterest, or YouTube. Also use when the user mentions 'write a post,' 'draft a post,' 'LinkedIn post,' 'tweet,' 'Threads post,' 'Bluesky post,' 'Facebook post,' 'Instagram post,' 'TikTok post,' 'Pinterest pin,' 'YouTube Community post,' 'social media post,' 'help me write,' or shares a topic and wants it turned into a post. For deeper visual-platform caption writing, see caption-writer-sms. For multi-part content, see thread-writer-sms. For carousels, see carousel-writer-sms. For opening lines, see hook-writer-sms.
Stores and queries chunked N-D scientific arrays with Zarr-Python 3, including codecs, sharding, S3/GCS storage, and NumPy/Dask/Xarray integration. Use for array layout, bounded I/O, format migration, or scientific metadata preservation.
Use the autocli CLI to interact with 55+ social and content websites (HackerNews, Reddit, Twitter/X, Bilibili, Zhihu, Weibo, Xiaohongshu, YouTube, Medium, Substack, Douban, WeRead, Linux-do, V2EX, Bloomberg, Google, Arxiv, Wikipedia, StackOverflow, Steam, Hugging Face, Apple Podcasts, Xiaoyuzhou, BBC, SinaFinance, DevTo, Lobsters, Xueqiu, BOSS直聘, Jike, Facebook, Instagram, TikTok, LinkedIn, Reuters, SMZDM, Ctrip, Coupang, Yahoo Finance, Barchart, Grok, Jimeng, Yollomi, Chaoxing, Weixin, Doubao, Cursor, Codex, ChatWise, ChatGPT, Notion, Discord, Antigravity, etc.) through the user's Chrome login session. ALWAYS prefer autocli over Playwright/browser automation for supported sites. Trigger when the user asks to browse, search, fetch hot or trending content, post, or read messages on any website; also use 'autocli read <url>' to extract main article content as Markdown (prefer it over WebFetch for JS-rendered or login-gated pages).
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.
Builds and audits reproducible NeuroKit2 research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Use when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Monitor Nx Cloud CI pipeline and handle self-healing fixes. USE WHEN user says "monitor ci", "watch ci", "ci monitor", "watch ci for this branch", "track ci", "check ci status", wants to track CI status, or needs help with self-healing CI fixes. Prefer this skill over native CI provider tools (gh, glab, etc.) for CI monitoring — it integrates with Nx Cloud self-healing which those tools cannot access.
When the user wants to plan, script, produce, or optimize App Store Preview videos or Google Play promo videos — the autoplay videos that show in App Store/Play Store search and product pages. Use when the user mentions "App Preview", "preview video", "app store video", "promo video", "Play Store video", "video poster frame", "YouTube promo for Play Store", "30 second app video", "video script", "video specs", or "should I add a preview video". For static screenshots, see screenshot-optimization. For A/B testing the video, see ab-test-store-listing. For broader creative briefs, see screenshot-optimization (covers stills).
Best practices and guidelines for working with Postgres. Covers schema design, indexing strategies, query optimization, migrations, and common pitfalls. Use when writing SQL, designing database schemas, optimizing queries, or setting up a Postgres database.
Install a per-turn canary signal (e.g. starting every reply with the user's name and a turn counter) so silent context degradation becomes visible the moment it happens, and run a recovery protocol when the canary trips. Use when the user mentions a "canary", "context canary", or "canary check", asks to detect context rot / compaction / drift, says "you stopped using my name" or "did you lose context", asks "how degraded is your context", or wants an early-warning system for long agent sessions.
Query and analyze Logfire telemetry data — traces, logs, spans, metrics, summaries, and SQL results. Use this skill when the user asks to "query logfire", "search traces", "find logs", "query data", "search spans", "look up errors in logfire", "get metrics from logfire", "analyze telemetry", "summarize errors", "find root cause", or add Logfire querying capabilities to code. Do not use this skill for direct Logfire UI, browser, live-view, Explore-page, or link-opening requests; use logfire-ui instead. If "show" or "view" wording is ambiguous, ask whether the user wants a UI view or query analysis.
Help users apply behavioral science to product design. Use when someone is designing for habit formation, reducing friction, applying psychology to UX, increasing retention through behavioral principles, or using nudges to influence user behavior.
Turn a website's observable HTTP traffic into a best-effort OpenAPI 3.1 spec by analyzing a `browser-trace` capture. Use when the user wants to discover/extract API endpoints from a browser session, build an OpenAPI doc from network traffic, or document a third-party site's XHR/fetch surface for client integration.
Applies the DHDNA framework as an exploratory rubric for reasoning and writing patterns in supplied text. Used for explicit requests for DHDNA, cognitive-style reflection, a thinking-pattern profile, or comparisons of textual reasoning. Scores describe evidence in the sample, not validated psychological traits or personal identity.
Builds a panel of personas from real material, interviews them under decision pressure (never stated preference), and walks a flow through their eyes — on a brief, a spec, a prototype, or the running app. Fresh panels, repeated runs, believe only what repeats; saturation and a parity check against real interviews when they exist. Three stances. Output is hypotheses tagged synthetic, never evidence. Use for "synthetic users", "walk this as the persona".
Featurizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML. Covers ECFP/MACCS fingerprints, RDKit descriptors, pharmacophores, pretrained embeddings, configuration persistence, and molecule-to-label alignment.
Performs constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.
Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks.
Predicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or sequence plus SMILES/SDF/MOL2. Covers batch docking, pose triage, confidence interpretation, and validation. Use for molecular docking and virtual-screening pose generation, not binding-affinity prediction.
Build Python APIs with FastAPI, Pydantic v2, and SQLAlchemy 2.0 async. Covers project structure, JWT auth, validation, and database integration with uv package manager. Prevents 7 documented errors. Use when: creating Python APIs, implementing JWT auth, or troubleshooting 422 validation, CORS, async blocking, form data, background tasks, or OpenAPI schema errors.
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.