Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
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Player and avatar system in Decentraland. Read player position/profile, customize appearance (AvatarBase), trigger emotes (triggerEmote/triggerSceneEmote), read equipped wearables (AvatarEquippedData), attach objects to players (AvatarAttach), create NPC avatars (AvatarShape), avatar modifier areas, and locomotion settings. Use when the user wants player data, emotes, wearables, NPC avatars, avatar attachments, or movement speed changes. Do NOT use for wallet/blockchain interactions (see nft-blockchain).
Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.
Diff a specific regulatory change against the indexed policy library. Use when a regulation changes and you need to know which policies it touches and what the gaps are; when the user says "diff this reg against our policies", "which policies does this affect", "gap analysis"; or when reg-feed-watcher passes along a material item.
Draft an AI usage policy based on regulatory registries and where the company's existing practices sit. Use when a team has never had an AI usage policy, needs a fast first draft for legal review, or an existing policy needs a full rewrite against new regulations.
Use when working in one of the *-polite-mcp personal automation repos (olx-polite-mcp, emag-polite-mcp, altex-polite-mcp, freshful-polite-mcp, temu-polite-mcp) or product-research-pipeline — implementing a new tool, running a live spike/CDP exploration against a real logged-in site, deciding whether an action needs confirmation before running it, or asking the user for several small per-item decisions (quantities, yes/no per item) during a reorder/shopping flow. Covers: confirming before the first live mutating action against a real personal account, batching interactive AskUserQuestion decisions instead of asking for a typed list, and writing spike/research findings into PLAN.md before or alongside implementing.
Summarize the matter portfolio from _log.yaml — risk distribution, upcoming deadlines, stale matters, materiality roll-up, stage distribution and flagged anomalies. Use when the user asks "how's the portfolio looking", "how many open matters are there", or needs a portfolio summary.
Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
Create research posters using HTML/CSS that can be exported to PDF or PPTX. Use this skill ONLY when the user explicitly requests PowerPoint/PPTX poster format. For standard research posters, use latex-posters instead. This skill provides modern web-based poster design with responsive layouts and easy visual integration.
Turn a PRD into a multi-phase implementation plan using tracer-bullet vertical slices, saved as a local Markdown file and optionally published as a GitHub sub-issue. Use when user wants to break down a PRD, create an implementation plan, plan phases from a PRD, or mentions "tracer bullets". Accepts an optional GitHub issue reference (e.g. `#123` or a full issue URL) to link the plan as a sub-issue of that PRD-epic.
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Review enterprise procurement pricing and packaging covering SKU structure, tier names, seat/usage metrics, minimums, commits, discounts, price holds, order forms, PO/invoice needs, tax, legal/security review, renewal uplift, co-terming, true-ups, budget cycles, procurement objections, and buyer-facing value proof. Use when pricing must pass procurement without damaging margin or trust.
Design and audit product analytics instrumentation for events, properties, identity, sessions, funnels, cohorts, experiments, attribution, billing/support joins, privacy, consent, data quality, mobile/web/server tracking, dashboards, QA, release gates, and metric ownership. Use when teams need trustworthy behavior data before product decisions or launches.
Validates apps-in-toss project configuration and structure for both web and React Native apps. Use when checking if granite.config.ts is properly configured, verifying required files exist, validating package.json has framework dependencies, ensuring project structure follows apps-in-toss conventions, or debugging project setup issues.
Guidance for designing fusion protein gBlock sequences from multiple protein sources. This skill applies when tasks involve combining proteins from PDB databases, plasmid files, and fluorescent protein databases into a single optimized DNA sequence with specific linkers and codon optimization requirements.
TOC router for PyGraphistry Python SDK tasks. Use when asked to "plot a graph", "visualize my edges", "load a dataframe into graphistry", "import graphistry", "run UMAP on my graph", "query my graph with GFQL or Cypher", "pick an execution engine", "make my GFQL query faster", "connect graphistry to Neo4j/Splunk/Kusto", or any Python SDK graph workflow. Also triggers on "graphistry.register", "g.plot()", ".gfql()", "chain-list", "hypergraph", "engine='polars'", "polars-gpu", "cudf", or "pygraphistry". Dispatches to pygraphistry-core (auth/ETL/plot), pygraphistry-gfql (queries, engines, indexes), pygraphistry-visualization (styling/sharing), pygraphistry-ai (UMAP/DBSCAN/embeddings), or pygraphistry-connectors (external DBs). Proactively suggest when the user shares an edges/nodes DataFrame and asks about graph analysis or visualization.
PyGraphistry graph ML/AI: UMAP, DBSCAN, embeddings, and anomaly detection workflows. Use when asked to "run UMAP on my graph", "cluster nodes", "find anomalies in my network data", "embed nodes", "fit-transform pipeline", "semantic search over graph nodes", or "graph AI". Also triggers on "graphistry umap", "dbscan clusters", "node embeddings", "featurize", or "anomaly triage". Proactively suggest when the user has node feature columns and asks about outliers, clusters, or similarity without yet using UMAP or DBSCAN.
Construct and run GFQL graph queries in PyGraphistry using chain-list syntax or Cypher strings. Use when asked to "query my graph with GFQL", "MATCH pattern in graphistry", "find paths between nodes", "hop constraints", "let bindings", "GRAPH constructor", or "run Cypher on my graph". Also triggers on "g.gfql()", "n() e_forward() n()", "chain-list query", "subgraph extraction", "remote graph query", or "pattern matching in graphistry". Proactively suggest when the user wants multi-hop traversal or pattern matching on a graph already loaded in PyGraphistry.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets
Use when writing, reviewing or refactoring Python and you want one settled answer rather than an evaluation — choosing between a dataclass, a Pydantic model, a NamedTuple, a TypedDict, attrs or msgspec; handling dates, times and timezones, including DST folds and gaps and where to convert to UTC; loading settings and secrets; when to use a guard clause, an early return or EAFP; designing an exception hierarchy; keeping type ignores honest; laying a package out under `src/`; building a CLI; async and concurrency; and how an HTTP client should handle sessions, timeouts and retries. Also when a module has accumulated global state, grown hard to follow, or needs restructuring — how far to break it up, when a module-level singleton with lazy properties is the right shape, and how to keep objects immutable and functions stateless. For tests see the Python testing skill; for MCP server internals see the MCP Python skill.
Python data engineering implementation and review skill. Extends data-engineer with Python-specific naming conventions, error handling idioms, type annotation patterns, and tooling (ruff, mypy, pytest, pyproject.toml). Use when implementing or reviewing Python data pipelines or libraries.
Executes a four-phase feature addition workflow (Discovery, Planning, TDD Implementation, Verification) for Python projects. Use when adding a new feature end-to-end — discovering project structure and integration points, drafting a feature spec with MoSCoW-prioritized requirements and BDD acceptance criteria, implementing via test-first TDD cycles, then verifying with ruff lint, ty type checks, and behavior-focused regression and contract coverage.
Build a Quarterly Business Review (QBR) deck structure and narrative for a customer account. Use when asked to prepare a QBR, business review meeting, executive review, or quarterly check-in with a customer. Produces a slide-by-slide QBR structure with talking points, metrics review, value narrative, and mutual next steps.
Proactively find books based on the user's actual problem, obtain and read the relevant body text, turn the methods in the book into independently callable skills, then test them with a task. Use when the user is unfamiliar with a field, wants to build a workflow with the help of books, or needs to read the body text and methodological basis for an existing skill.
Maximize AI visibility through query fan-out coverage. Use when planning content clusters, targeting LLM sub-questions, or expanding semantic keyword coverage to get cited by AI systems. Covers semantic variation analysis and sub-question targeting strategies.
Comprehensive React Flow (@xyflow/react) patterns and best practices for building node-based UIs, workflow editors, and interactive diagrams. Use when working with React Flow for (1) building flow editors or node-based interfaces, (2) creating custom nodes and edges, (3) implementing drag-and-drop workflows, (4) optimizing performance for large graphs, (5) managing flow state and interactions, (6) implementing auto-layout or positioning, or (7) TypeScript integration with React Flow.
React Router v6+ for client-side routing. Covers BrowserRouter, Routes, navigation, data loading, nested routes, protected routes, and code splitting. USE WHEN: user mentions "React Router", "routing", "navigation", "BrowserRouter", "Routes", "useNavigate", "Link", "nested routes", asks about "client-side routing", "protected routes", "route parameters", "loaders" DO NOT USE FOR: Next.js routing - use Next.js skill instead, Server-side routing - use framework-specific skills, basic React navigation - use `react` skill for simple conditional rendering
Recall, search, continue, or analyze past conversations. Triggers on recall phrases ("what did we discuss", "continue where we left off", "we decided"), retrospective phrases ("do a retro", "post-mortem", "what went well", "lessons learned", "find antipatterns"), and implicit signals (past-tense references, possessives without context, assumptive questions like "do you remember").
Use when commits on a feature branch are messy, out of logical order, mix unrelated changes, or need restructuring before a PR - also triggered by "clean up history", "reorganize commits", "recompose branch", "make commits logical", or similar
Implement Recur checkout flows including embedded, modal, and redirect modes. Use when adding payment buttons, checkout forms, subscription purchase flows, or when user mentions "checkout", "結帳", "付款按鈕", "embedded checkout". Taiwan subscription billing via PAYUNi (recur.tw, 台灣訂閱金流).
Refactor ASP.NET Core/C# code to improve maintainability, readability, and adherence to best practices. Transforms fat controllers, duplicate code, and outdated patterns into clean, modern .NET code. Applies C# 12 features like primary constructors and collection expressions, SOLID principles, Clean Architecture patterns, and proper dependency injection. Identifies and fixes anti-patterns including service locator, captive dependencies, and missing async/await patterns.
Diff newly enacted or amended regulations against current personal information handling rules and practices — outputting a gap list and a remediation plan with owners and dates. Use when a new regulation lands and the user asks "does [regulation] affect us", "[regulation] gap analysis", "compliance check against [regulation]", or pastes regulation text.
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.