Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
All
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
Machine learning in Python with scikit-learn. Use for supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Scrapling web scraping reference for this project. Provides fetcher selection (Fetcher/DynamicFetcher/StealthyFetcher), selector patterns, session management, anti-bot bypass, adaptive/self-healing selectors, Spider framework for crawling, proxy rotation, CLI commands, and MCP server for AI integration. Use when implementing web scraping, handling protected sites, or building data extraction pipelines.
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.
【Suite-internal skill · Users usually do not need to call this directly for the full workflow; simply describe the request to the main controller short-drama-director, which will route to this skill automatically】【Manju Laoli V7.4.26 suite · subskill · 〇·1 source-lock · benchmark scouting】A lightweight drama scouting and benchmarking intelligence engine. When the user says “I want to benchmark a hit,” it retrieves the drama's public information from across the web, including international sites, and delivers a 《Benchmark Intelligence Card》 in a fixed format. Core capabilities: a tiered short-drama source list (official platform rankings / data index sites / industry media / social media discussions / international indices); four scouting steps (set target → select sources → search → compile card); a 13-field benchmark intelligence card template (including transferable and non-transferable elements); and six evidence rules (no fabrication / always mark timestamps / always cite sources / distinguish first-hand from relayed information / separate facts from inferences / only independent source counts indicate popularity). Triggers: benchmark a hit, dig into it, scout, find materials, collect information, trending topic, hit analysis, use this as a reference, genre research, segment research, market research. This skill is part of the suite; the full workflow starts with the main controller short-drama-director, which selects the 〇·1 source lock.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Query decomposition and multi-source search orchestration. Breaks natural language questions into targeted searches per source, translates queries into source-specific syntax, ranks results by relevance, and handles ambiguity and fallback strategies.
Backend security auditor for authentication, authorization, token handling, payment abuse, file uploads, data leaks, injection, and broken access control. Inspects every layer of the stack and adapts to Express, NestJS, Django, Rails, Go, and other backends. Use when asked to security review, audit, harden, or check backend code for vulnerabilities.
Application security specialist focused on authentication and authorization, data protection, and compliance auditing. Use this Skill when the user needs to: (1) design a secure login and authentication system (2) conduct a security code review (3) check GDPR/privacy compliance (4) prevent common security vulnerabilities (OWASP Top 10).
Write a STRIDE-based threat model for a service or feature. Use when asked to produce a threat model, document security risks, identify attack vectors, assess a service's security posture, or prepare for a security design review. Produces a structured threat model covering assets, trust boundaries, STRIDE threat enumeration per component, risk scores, mitigation controls, and residual risk sign-off.
End-of-semester case handoff memos — the mirror of /ramp. Generates per-case handoff memos and a cohort summary so an outgoing cohort hands work off cleanly to a new cohort. Reads deadlines, client communications, and case history. Use when a supervisor or outgoing student needs to close out a semester, build handoff memos, or help graduating/exiting students transition out.
Simula un reclutador senior para practicar entrevistas. Usar cuando el usuario quiera entrenar para entrevistas, practicar respuestas, recibir feedback sobre sus answers, o hacer mock interviews. Activa con palabras como mock interview, practicar entrevista, simular reclutador, entrevistame, hazme preguntas, evalua mi respuesta, feedback de entrevista. Especializado en roles Senior Full-Stack Developer, SaaS, y posiciones remotas.
Persona and expertise framework for a Senior UI/UX Designer specializing in B2B e-commerce floral marketplaces. Use this skill when designing interfaces, creating wireframes, prototypes, or design systems for wholesale flower businesses, floral supply chains, bulk ordering platforms, florist-to-supplier marketplaces, or any B2B floriculture digital product. Triggers include: floral marketplace design, wholesale flower platform UX, B2B floral e-commerce, florist ordering systems, flower supply chain interfaces, bulk floral ordering UI.
Build, review, debug, configure, migrate, or plan Sentry observability in TypeScript/JavaScript apps with current docs and official @sentry/* packages. Use for Sentry JS SDK 11 (and v10→v11 migration), Sentry.init, DSN, dataCollection, span streaming, beforeSendSpan, ignoreSpans, errors, tracing, Session Replay, profiling, logs, metrics, source maps, releases, Seer, OpenTelemetry, enableOpenTelemetrySetup, @sentry/nextjs, @sentry/react, @sentry/node, @sentry/browser, @sentry/vue, @sentry/svelte, @sentry/sveltekit, @sentry/nuxt, @sentry/astro, @sentry/remix, @sentry/react-router, @sentry/solid, @sentry/solidstart, @sentry/tanstackstart-react, @sentry/nestjs, @sentry/hono, @sentry/elysia, @sentry/bun, @sentry/deno, @sentry/cloudflare, @sentry/aws-serverless, @sentry/electron, @sentry/react-native, @sentry/bundler-plugins, wizard, sentry CLI, vite/webpack plugins, captureException, setUser, and setAttribute.
Run a focused N-question drill on one subject — objective questions, free-response questions, or flashcards. Tracks performance and updates the study plan. Use when the user says "give me 10 [subject] questions," "run a [subject] drill," "make 5 [subject] cards," or wants to practice a fixed number of items and have the plan adjust accordingly.
Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's ticket tracker (GitHub or local markdown), triage label vocabulary, domain doc layout, and review-prep conventions (commit style and breadcrumb schema). Run before first use of `to-tickets`, `to-spec`, `tdd`, `improve-codebase-architecture`, `wayfinder`, or `prepare-for-review` — or if those skills appear to be missing context.
Use 山西证券 Tushare (sxsc_tushare) to obtain professional structured historical financial market data. Characteristics: data is T-1 day (no real-time quotes), and the data conventions suit batch processing and export. Triggers: when the user asks to obtain/pull/export/batch-fetch tushare, sxsc_tushare, or 山西证券 tushare data, or mentions historical quotes batch pulling, batch export of financial statements, backtest data preparation, data export to CSV/Parquet, macro historical data (CPI/PPI/PMI/社融/利率), index constituents, 申万/中信 industry classification, convertible bond data, futures data, bond data, historical fund flows, historical K-lines, limit-up/limit-down statistics, dividends and stock dividends, restricted share unlocking, fund NAV history, Hong Kong stock data, treasury bond yields, Shibor rates, suspension/resumption information, or the top ten traded stocks of 沪深股通, or says "拉份数据", "导出CSV", "批量取数". This skill only covers data fetching and data delivery, not analysis of instruments; if the user requests analysis/evaluation/comparison/deep research on instruments, that should be handled by the sxsc-tushare-analysis skill. Applicable scenarios: batch retrieval of historical data, preparation of backtest data tables, data export.
Use this skill to run the Shipshape Shipwright role: in-harbour code inspection. Discovers existing behaviour and policy violations from production code, writes @captain-tagged scenario skeletons and @planks annotations for Captain review. Run for fitting out, onboarding an existing codebase, and between releases.
Build Shopify applications, extensions, and themes using GraphQL/REST APIs, Shopify CLI, Polaris UI components, and Liquid templating. Capabilities include app development with OAuth authentication, checkout UI extensions for customizing checkout flow, admin UI extensions for dashboard integration, POS extensions for retail, theme development with Liquid, webhook management, billing API integration, product/order/customer management. Use when building Shopify apps, implementing checkout customizations, creating admin interfaces, developing themes, integrating payment processing, managing store data via APIs, or extending Shopify functionality.
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
High-performance routing engine for AI AgentSkills. Classifies user intent, scans for specialized skills, and generates optimal dispatch decisions (HANDOFF, SEQUENCE, or NO_MATCH). Use this to manage complexity in large skill environments and ensure the best-equipped skill handles every task.
Package and distribute Claude Code skills for sharing via GitHub, Claude.ai uploads, or team deployment. Creates install scripts, documentation, and .skill packages. Use when user says "publish skill", "share skill", "package skill", "distribute skill", or "release skill".
Install community skills from watched registries. Reads the allowlist first, fetches, shows the raw SKILL.md (not just a summary), runs structural trust checks, runs skills-qa, and writes files only after explicit user approval. Use when the user says "install [skill]," picks install while browsing, or provides a direct skill URL.
Reference: detailed uninstall, disable, and re-enable workflows for community skills installed through the legal builder hub. Safe by default — refuses to touch first-party plugin skills, confirms before deleting files, and logs every action. Loaded by the /legal-builder-hub:uninstall and /legal-builder-hub:disable skills.
Evaluate a skill against the legal skill design framework — thirteen design parameters (including trust surface, freshness, schema validation, and conflict detection), three legal failure modes, and a three-tier verdict (ready / some concerns / major concerns). Use before deciding whether to trust a community skill for installation, before deploying a first-party skill to the team, or when the user asks "should I trust this?" or "is this skill well designed?" Runs automatically as part of /legal-builder-hub:skill-installer.
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.