Skills
99,064 packaged capabilities and playbooks for agents. Install any of them with one command.
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Configure, manage, and troubleshoot markdownlint in projects. Use when user wants to setup/install/configure markdownlint, add/remove/modify linting rules, fix markdown validation issues, customize .markdownlint.yaml, update ignore patterns, integrate with tools (Husky, CI), or troubleshoot markdown linting errors. Use even when user mentions markdown formatting problems, quality issues, or style consistency without explicitly saying "markdownlint".
When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth ideas,' 'how to market,' 'marketing strategies,' 'marketing tactics,' 'ways to promote,' or 'ideas to grow.' This skill provides 139 proven marketing approaches organized by category.
Plans video strategy, scripts, and optimization for YouTube, TikTok, Reels, and video ads. Triggers for 'video strategy', 'YouTube', 'video script', 'Reels script', 'video SEO', 'thumbnail', 'video ad script', 'webinar', 'Loom', 'explainer video', 'product demo video', 'short-form video', or 'video content calendar'.
Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.
Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.
Build, import, analyze, tolerate, and optimize optical systems using the Optical Design and Simulation Library. Use when the user asks about optical systems, ray tracing, geometric optics, Zemax import, optical coatings, tolerancing, or optical design optimization.
Upgrade C, C++, and Fortran MEX source files from the Separate Complex (SC) API to the Interleaved Complex (IC) API. Use when converting mxGetPr/mxGetPi to mxGetComplexDoubles, migrating MEX files to -R2018a, adding MX_HAS_INTERLEAVED_COMPLEX guards for SC/IC guarded builds, or modernizing legacy MEX code that uses mxGetData/mxSetData/mxGetImagData/mxSetImagData. Covers C (.c), C++ (.cpp, .cxx), and Fortran (.F, .f90) MEX functions. Triggers on: interleaved complex, IC MEX, mex upgrade, mex migration, separate complex, mxGetPi, mxGetPr replacement, -R2018a, complex MEX, Fortran MEX, .F MEX file, C++ MEX, .cpp MEX file, MEX performance, MEX slow complex, MEX call overhead, improve MEX performance complex.
Connect to and acquire images from cameras in MATLAB using Image Acquisition Toolbox. Use this skill when the user wants to: use a webcam, acquire images, capture video, stream from a camera, connect to a GigE Vision camera, preview live video, list available cameras, configure camera properties, use a CoaXPress frame grabber, or use a Camera Link frame grabber. Covers USB webcams, GigE Vision, USB3 Vision, CoaXPress, Camera Link, and GenICam-compliant cameras. Always prefer videoinput from Image Acquisition Toolbox over webcam support package or gigecam.
Add SQLite local database storage to .NET MAUI apps using sqlite-net-pcl. Covers data models with ORM attributes, async database service with lazy init, DI registration, WAL mode, and file management. Works with any UI pattern (XAML/MVVM, C# Markup, MauiReactor). USE FOR: "SQLite database", "local database", "sqlite-net-pcl", "offline storage", "CRUD database", "database service", "WAL mode", "CreateTableAsync", "local data persistence", "ORM attributes". DO NOT USE FOR: REST API data fetching (use maui-rest-api), secure credential storage (use maui-secure-storage), or file picking (use maui-file-handling).
Create software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams, sequence diagrams, flowcharts, ERDs, C4 architecture diagrams, state diagrams, git graphs, and other diagram types. Triggers include requests to diagram, visualize, model, map out, or show the flow of a system.
Build a metrics framework for any product, team, or business. Use when asked for a metrics tree, KPI framework, North Star metric, AARRR funnel, HEART framework, or OKR metrics. Produces a structured metrics hierarchy from North Star down to leading indicators, with measurement guidance.
Migrate configuration from Bluejay voice AI testing platform to Coval. Use when customer says "migrate from bluejay", "bluejay migration", "import bluejay config", or needs to transfer agents, simulations, metrics, and schedules from Bluejay to Coval.
Decomposes goals into team blueprints using evidence-based scaling laws, topology selection, and role design. Determines whether a goal needs a single agent or a coordinated team (3-4 agents recommended, 5 max), selects the optimal communication topology (sequential pipeline, parallel-independent, centralized coordinator, hierarchical), and produces structured blueprints with artifact chains and quality gates. Use this skill when the user wants to build something, plan a project, assemble a team, figure out what roles they need, break down a complex goal, or asks "how should I approach [X]?" — even if they don't mention agents or teams. Also triggers on specific domains: "build a SaaS," "marketing campaign," "security audit," "write a book," "launch a product." Do NOT use for simple single-step tasks, direct coding questions, or file operations that need no planning.
MKD (`mkd` — Make Decision) — a local, browser-based decision-deck CLI the AI drives to collect structured human feedback: decisions with justified options, questions, report reactions and answerable tables, one screen at a time (Slack Catch-Up style). WHEN to use: the AI has MORE THAN ~3 decision points or a long multi-section report/plan that needs the user's verdict point by point. It beats `AskUserQuestion` (capped at ~4 questions × ~4 options, terminal-bound, no rich free-text, cannot show reference content) and beats an inline prose questionnaire (unanchored replies the AI must guess-map back). HOW it works: the AI writes a spec JSON of `items` (`decision` | `question` | `report` | `table`), runs `bun <skill-dir>/cli/index.ts <specPath>` (no install, no binary), and the CLI renders a self-contained deck page under `~/.mkd/`, opens the browser and EXITS immediately — the user answers at their own pace (progress rail, arrow keys, skip = decide later, live stats, localStorage persistence) and pastes the copied Result JSON back into the chat, which the AI treats as the execution contract. Inside Orca (multi-agent IDE) the default changes: the CLI serves the deck, opens it in a browser tab bound to the current worktree and BLOCKS until the user presses Send, then prints the Result JSON on stdout, so the AI must run it in the background (`--copy` restores the copy-paste flow). Add `--wait` outside Orca only when the answer is needed the same turn (blocking loopback server, Result JSON on stdout). In Claude Code with the `Artifact` tool, pass `--artifact`: the CLI renders a claude.ai Artifact page, the agent publishes it with `capabilities: {db: {}, comments: {}}` and arms a watch, and the user's Send to Claude wakes the session with the Result in the artifact's db (no localhost, no copy-paste); other harnesses keep the flows above. HARD RULE: every decision option MUST carry a written justification (value + cost), and the recommended option's justification must state WHY it is recommended — the validator rejects options without one. Triggers on: `mkd`, `make decision`, `decision deck`, `catch-up`, `catchup`, `decision form`, `feedback UI`, `ask me point by point`, `let me answer in the browser`, `review this plan point by point`, `react to each section`, `more than three questions`, `decisiones de auditoría`, `tomar decisiones`, `mkd artifact`, `answer in a claude.ai artifact`. Use this skill even when the user does not say `mkd` — if the task is to collect granular decisions or anchored feedback on many points, this is the right tool. Do NOT use for: a trivial 1-2 option pick or a single yes/no (use `AskUserQuestion`), or any non-interactive / CI / one-shot output where there is no human at a browser.
This skill should be used when the user asks to automate Azure ML and Azure Data Factory production workflows. PROACTIVELY activate for: (1) Azure ML code asset registration, azure-ai-ml SDK, AML code versions, `result.version`, requested-vs-actual versions, (2) ADF to Azure ML orchestration, ADF WebActivity, managed identity blob reads, `connectVia`, managed VNet IR, (3) code asset version pointer blobs, latest.json contracts, training/scoring code version propagation, (4) private storage firewalls, Microsoft-hosted CI agents, temporary network rules, storage data-plane smoke tests, (5) marshmallow<4 pinning, AML SDK import failures, runtime validation for Azure ML infrastructure. Provides: operationally safe Azure ML + ADF automation patterns.
When the user wants to apply machine learning to supply chain problems, build ML models, or use AI for predictions. Also use when the user mentions "machine learning," "deep learning," "neural networks," "AI supply chain," "predictive models," "classification models," "regression models," "anomaly detection," "computer vision," or "NLP for supply chain." For traditional forecasting, see demand-forecasting. For optimization, see optimization-modeling.
Manage refresh schedules, selected-lineage execution definitions, and job execution for existing Microsoft Fabric Materialized Lake Views (MLVs) via REST APIs. Configure, update, and delete schedules and execution definitions; trigger on-demand refreshes; monitor job status; and cancel running jobs. Use `spark-authoring-cli` to author MLV definitions. Discovery, visual lineage, and data quality metrics require the Lakehouse UI. Triggers: "schedule MLV refresh", "manage MLV refresh schedule", "trigger MLV refresh", "MLV job status", "cancel MLV refresh", "monitor MLV refresh", "recurring MLV refresh", "daily MLV refresh".
Generate interactive HTML mockups from a description, iterate on them, and deploy to GitHub Pages or Vercel for sharing. Use this skill when the user wants to create a mockup, prototype, UI exploration, or design comparison, or when they want to deploy or share one.
Walks the user through validating a product idea using The Mom Test method (from the book by Rob Fitzpatrick) - clarifying the idea and customer segment, finding the riskiest assumptions, crafting interview questions that pass the Mom Test, finding people to talk to, debriefing raw interview notes, and reaching a proceed/pivot/kill verdict. Includes a mock-interview practice mode. Use this skill whenever the user wants to validate a product, startup, SaaS, or app idea, prepare for or review customer discovery interviews, mentions The Mom Test, customer conversations, user interviews, talking to users or customers, or asks whether their business or product idea is any good - even if they never mention the book.
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
Get kid-friendly movies ready to post, without touching a single slider. Upload your images or video clips (MP4, MOV, JPG, PNG, up to 200MB), say something like "turn my pictures into a short movie with music and fun text", and download 720p MP4 when it's done. Built for kids and parents who move fast and want to make fun videos without complicated software.
Generates multi-user workflow documentation by interviewing the user about personas, exploring the codebase for multi-user patterns, then walking through the live app with per-persona Playwright CLI named sessions to co-author interleaved, persona-tagged workflows. Use when the user says "generate multi-user workflows", "create multi-user workflows", or "generate concurrent user workflows". Produces persona-tagged workflow markdown that feeds into the multi-user converter and Playwright runner.
Generate, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.
Prepare and run negotiations using tactical empathy and structured trade design. Use when handling high-stakes salary, vendor, contract, or scope discussions that require strategy-script separation, BATNA/ZOPA framing, and concession guardrails.
NetBox Branching plugin — create isolated branch schemas for safe change staging, review, and merge. Use when working with branch lifecycle, the X-NetBox-Branch header, async provisioning/sync/merge jobs, conflict resolution, or Change Request integration.
How to use NetBox's configuration template system for generating device configs. Covers Jinja2 templates, config contexts, template rendering API, and common patterns. Use when building config generation workflows, automating device provisioning, or integrating NetBox with config management tools.
Configure and operate Orb Agent for automated network discovery into NetBox. Use when writing agent.yaml configs, setting up network/device/SNMP/worker discovery backends, deploying Orb Agent containers, managing policies and secrets, or troubleshooting discovery pipelines.
Newsletter curation with content sourcing, editorial structure, and subscriber growth strategies. Covers issue formatting, link roundups, commentary style, and sending cadence. Use for: email newsletters, link roundups, weekly digests, curated content, creator newsletters. Triggers: newsletter, email newsletter, newsletter curation, weekly digest, link roundup, curated newsletter, newsletter writing, newsletter format, subscriber growth, newsletter strategy, content curation, newsletter template
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.