Skills
98,873 packaged capabilities and playbooks for agents. Install any of them with one command.
Development
Use when completing work on a feature branch, preparing to merge, or cleaning up after development is done. Triggers: branch work is complete, user says "merge", "create PR", "finish branch", "done with this branch", ready for code review.
Branching, merging, versioning, and release conventions for the Gitflow workflow, covering main/develop branches, feature/release/hotfix branches, semantic versioning, and pull request rules. Use when creating a branch, naming a branch, planning a release or hotfix, writing a commit message, or setting up branch protection for a repository that follows Gitflow.
Generates a deeply contextual, progressive-disclosure briefing for any GitHub repository. It builds a mental model of the code before analyzing issues and PRs to provide actionable insights. Use when asked for a "GitHub report," "repo status," "daily briefing," or to "catch up on a codebase."
Build or review Next.js App Router animation: GSAP page transitions, component enter/exit, scroll effects, interrupted motion, and cleanup. Use even when GSAP is unnamed, including choosing React View Transitions. Also covers loading flashes, data-ready entrances, layout stability, hidden-content recovery, and first-load performance. Not for Pages Router, other routers, or isolated GSAP API questions.
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
Write user stories that account for disability and diverse abilities from the start. Use when writing user stories, acceptance criteria, jobs to be done, or requirements. Triggers on: user story, user stories, acceptance criteria, jobs to be done, JTBD, requirements, as a user I want, definition of done, story writing.
The only skill in this repo that runs `git add`, `git commit`, `git push`, or `gh pr create`. Invoke ONLY when the user explicitly asks to commit, push, or open/prepare a PR (e.g. "commit this", "commit progress", "push this branch", "prepare the PR", "open the PR"). No other incu-way skill may invoke this automatically or run those git commands itself — they may only suggest it to the user.
Use this skill to drive an Android device from the host shell — inject taps, swipes, text, key events, and drag-and-drop via `adb shell input`; resize the display with `wm size` / `wm density`; flip hermetic-test settings with `settings put global window_animation_scale 0`; reset app state with `pm clear` vs `am force-stop`; launch Activities with `am start -n pkg/.Activity`; and use the modern `cmd <service>` wrapper (`cmd wifi`, `cmd connectivity`, `cmd uimode night`, `cmd locale set-app-locales`) instead of the deprecated `svc` aliases on API 30+. If the user mentions "input tap", "input swipe", "input text spaces", "KEYCODE_BACK", "wm size for screen-size matrix", "settings put animation_scale", "force-stop vs pm clear", "svc wifi no-op", "cmd wifi set-wifi-enabled", or "am start intent extras", use this skill.
Pattern library for Copilot Studio agent design. Contains proven and recommended implementation patterns with YAML examples. Used by the Advisor agent to suggest patterns and by the Author agent to reference YAML structure during implementation. USE FOR: design guidance, pattern suggestions, review against best practices, troubleshooting known pitfalls, YAML reference during implementation. DO NOT USE FOR: general YAML schema reference (use int-reference), topic creation mechanics (use new-topic).
Wire a PostHog product surface into the PostHog AI agent from the frontend. Use when attaching scene or entity context, injecting instructions to steer the agent, reacting to the agent calling a tool, applying an agent edit back into an open form, or rendering a product tool card in a thread.
Use when building or maintaining Laravel applications — Eloquent ORM, Blade, Livewire, queues, Pest testing, middleware, service providers, migrations. Trigger conditions: Laravel project setup, Eloquent model design, Blade or Livewire component creation, queue/job implementation, Pest test writing, middleware configuration, migration authoring, route definition, Form Request validation, policy authorization, Sanctum/Passport authentication, Horizon queue monitoring.
Learn about Moralis and Web3 development. Invoked without a question, gives a friendly platform walkthrough — what's available, what data you can fetch, and how everything fits together. Invoked with a question, answers it directly. Use for "what is Moralis", "can Moralis do X", "what chains are supported", "how do I get started", "which API should I use", pricing, feature comparisons, or any exploratory questions. Routes to the correct technical skill (@moralis-data-api or @moralis-streams-api) after answering.
Expert guidance for .NET MAUI Blazor Hybrid development combining native mobile/desktop apps with Blazor web UI. Use when working with: (1) .NET MAUI Blazor Hybrid apps, (2) BlazorWebView components, (3) Cross-platform apps sharing Blazor UI, (4) Platform integration in Blazor Hybrid, (5) MVVM with Blazor components, (6) Navigation between MAUI pages and Razor components, (7) Shared Razor Class Libraries (RCL) for MAUI and Web. Applies to any .NET MAUI project using Blazor for UI.
Use when the user needs to build MCP (Model Context Protocol) servers — tool definitions, resource management, prompt templates, transport layers, and client integration. Triggers: user says "MCP", "MCP server", "model context protocol", building tools for AI clients, creating AI integrations.
MISRA C:2025 expert that operates in two modes: (1) Review — scan existing C code for violations across all ~223 guidelines (22 directives + ~201 rules), report findings with rule IDs, corrected code, and deviation justification templates; (2) Develop — generate new C functions, modules, or data structures that are MISRA-compliant from the first line, applying the essential type model, safe control flow, and banned-construct avoidance from the start. Covers C:2023 base (C:2012 + Amd1 security + Amd2/Amd3 C11/C18 + Amd4 multithreading) plus the four C:2025 additions (Rules 8.18, 8.19, 11.11, 19.3), Rule 15.5 disapplication, and 69 refined guidelines. Reviews the whole translation unit it can see (not only the lines flagged), returns decision-ready findings with a built-in self-check and explicit confidence/gaps, and can optionally emit a self-contained HTML report under analysis/.
A skill for design decisions on feature boundaries, separation of responsibilities, module division, and abstraction. Use when deciding where to draw boundaries, how to separate responsibilities, whether an abstraction is appropriate, or when you want to revisit responsibility placement. Not for simple CRUD additions, diff reviews, or API surface design. Use refactor-mindset for general refactoring decisions, and this skill when boundary drawing and responsibility placement are the subject.
Complete Next.js deployment system (Next.js 15.5/16). PROACTIVELY activate for: (1) Vercel deployment and configuration, (2) Self-hosted Node.js deployment, (3) Docker containerization, (4) Static export with output:'export', (5) Edge Runtime configuration, (6) Environment variables, (7) CI/CD with GitHub Actions, (8) Health checks and monitoring, (9) Production optimization, (10) Turbopack production builds (Next.js 16). Provides: Dockerfile, docker-compose, PM2 config, vercel.json, GitHub Actions workflows, Turbopack config. Ensures production-ready deployment with proper configuration.
Complete Next.js Server Actions system (Next.js 15.5/16). PROACTIVELY activate for: (1) Defining Server Actions with 'use server', (2) Form handling with actions, (3) useActionState for form state, (4) useFormStatus for pending states, (5) Optimistic updates with useOptimistic, (6) Validation with Zod and next-safe-action, (7) Authorization in actions, (8) File uploads, (9) Revalidation after mutations, (10) Type-safe actions with next-safe-action library. Provides: Action patterns, form integration, validation, optimistic UI, error handling, next-safe-action integration. Ensures secure server mutations with proper validation and UX.
Build Next.js 16 + React 19 + TinaCMS sites with visual editing, blocks-based page builder, and complete SEO. Use this skill whenever the user mentions TinaCMS, Tina CMS, Next.js with a CMS, visual editing with Next.js, click-to-edit, content-managed Next.js site, blocks pattern page builder, or migrating to Next.js + TinaCMS. Also trigger for TinaCMS schema design, self-hosted TinaCMS, TinaCMS media configuration, or any TinaCMS troubleshooting. Covers Day 0-2 setup from scaffolding through production deployment on Vercel.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Build on Bitcoin Layer 1 with OP_NET - Bitcoin L1 consensus layer for trustless smart contracts. Use when building AssemblyScript smart contracts, TypeScript libraries, React frontends, or Node plugins for OPNet. Triggers on Bitcoin smart contract development, OP20 tokens, OP721 NFTs, WebAssembly contracts, verify-dont-custody patterns, epoch mining, and OPNet architecture questions.
Shared optimization guidance plus CuTe Python DSL overlays. Use when: (1) selecting optimizations for a CuTe Python DSL kernel, (2) deciding whether a finding is shared or cute-dsl-specific, (3) recording CuTe Python DSL implementation notes, (4) reviewing the knowledge layout for cute-dsl work, (5) mapping shared patterns to a CuTe Python DSL implementation surface.
Shared optimization guidance plus cuTile Python DSL-specific overlays. Use when: (1) selecting optimizations for a cuTile Python DSL kernel, (2) checking cuTile-specific implementation traps, (3) deciding whether a profiling finding belongs in shared knowledge or a cuTile overlay, (4) updating cuTile Python DSL optimization docs, (5) reviewing how a shared pattern maps to cuTile.
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
Full optimization workflow, sub-agent launch templates, agent communication contracts, default configurations, tuning strategy, and knowledge base update protocol. Use when: (1) starting an optimization cycle, (2) launching a Profiler or Designer sub-agent, (3) interpreting or formatting agent communication, (4) updating the knowledge base after a profiling or implementation iteration, (5) deciding default configurations or tuning strategy for a kernel.
Pipeline skill for automating prior authorization decision workflows. Use when the user asks to parse PA request data (X12 278 or FHIR PAS bundles), extract clinical features for adjudication, build rules-based PA decision engines, train ML classifiers on historical PA decisions, analyze denial patterns, or generate SHAP explanations for PA outcomes. Triggers include "parse 278", "FHIR PAS bundle", "PA automation", "adjudication logic", "PA classifier", "denial analysis", "prior auth ML", "SHAP explainability", "PA feature extraction", "rules engine PA", "PA decision pipeline", "authorization workflow", "clinical criteria extraction", "denial pattern mining", "PA turnaround time".
Use when optimizing application performance, reducing load times, improving database queries, meeting performance budgets, or diagnosing bottlenecks in web applications or APIs. Triggers: slow page loads, poor Web Vitals, database timeouts, large bundle size, user-reported sluggishness, scaling preparation.
This skill should be used when the user asks to "add a glow", "blur the background", "add a screen effect", "mask a sprite", "add post-processing", "shader effect", "add lighting", "flashlight cone", "vision cone", "normal maps", "make the screen flash", "color grading", "tint a sprite", "screen shake with effects", "damage flash", "hit flash", "vignette", "bloom", "convert my preFX/postFX code", "BitmapMask replacement", "custom shader", "SpriteGPULayer", "Mesh2D", "stencil mask", or any Phaser 4 visual effect, filter, mask, light or renderer question.
Adds or modifies a camera backend under physicalai.capture. Use when implementing a new Camera type, extending create_camera in src/physicalai/capture/factory.py, discovery helpers, optional pip extras for vendor SDKs, SharedCamera transport, or tests under tests/unit/capture with fake devices.
Configures preprocessors, postprocessors, and runners around InferenceModel via manifest specs and ComponentRegistry. Use when editing physicalai.inference.preprocessors or postprocessors, manifest preprocessor/postprocessor lists, instantiate_component, registered type names, or class_path init_args for inference pipeline components.
Loads and validates policies exported from Physical AI Studio for Runtime deployment. Use when working on InferenceModel, InferenceModel.from_pretrained, manifest.json, adapter auto-detection (onnx, openvino), backend/device kwargs, Hugging Face Hub policy packages, or the Runtime side of the export/load contract that Studio produces with physicalai export.
Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning rollout/episode/env settings, recording rollout videos, or interpreting results.json / results.csv.
Exports and validates Physical AI Studio policies for Runtime deployment. Use when working on policy.export(...), the physicalai export CLI, the ONNX/OpenVINO/Torch/ExecuTorch backends, export metadata, numerical parity checks, or the Studio side of the export/load contract that Runtime consumes with InferenceModel(...).
Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format. Use when wiring physicalai.data.lerobot.LeRobotDataModule into a training config, choosing a repo_id, converting between the physicalai and lerobot data layouts, defining observation Features/FeatureType, setting normalization, or debugging batch shapes and dataloading.
Use when: a requirement change occurs during a project, the change's impact scope and risks need assessing, or change history needs recording. Do NOT use when: the change is trivial and needs no formal assessment, or a clear decision on the change already exists and no analysis is needed
Use when: allocating R&D resources across multiple products, performing ROI assessments, resolving resource conflicts, or planning teams. Do NOT use when: resources are sufficient and no prioritization is needed, or there is a single product with no multi-project coordination
Simulates a multi-role PRD/prototype review board, giving review conclusions from six perspectives: product, engineering, QA, design, operations, and legal. Triggered when the user says "帮我评审一下这个 PRD", "看看这个需求有没有问题", "模拟评审会", "review 一下这个文档", "这个需求能不能过评审", "帮我查漏补缺". Also applies when: the user uploads a PRD, requirements doc, prototype screenshot, or feature spec and asks for a check; the user mentions "评审", "review", "过会", "需求评审", "方案评审" or similar keywords; the user asks whether a requirement is feasible from an engineering or QA perspective. Not for: writing PRDs (use pm-prd-writer), pure code review (use code-review), or pure design walkthroughs (use design-critique).
[Hyper] Create or update a layered product planning package under `.hyper/prd/[slug]/` in the order PRD → feature specification → user flow → low-fidelity wireframe, with supporting planning diagram, HTML preview, source log, and optional flow tracking. Use when the user wants product requirements and downstream planning artifacts before implementation, not just a standalone PRD.
Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python. Do not use this skill for unrelated requests; route to the nearest named specialist.
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.