Skills directory

Profiles a target application using rocprofv3 with program counter sampling enabled and then analyzes the results. Use when the user has a program that runs on an AMD GPU and asks to perform PC sampling, to determine the runtime performance

Bring anything drawn to life with calm, believable motion. It covers characters and creatures (gliding pupils, blinks, breathing, head tilts), full-body acting (landing, waving, leaning, levitating, kicking a ball), plants, fabric, fire, sm

Create or improve a complete Runpod template project from an application request, local code, or GitHub repository, with a documented Dockerfile and app-specific files for Pods or queue/load-balanced Serverless. Use runpod-templates for fin

Write idiomatic, robust, maintainable Rust. Use this skill whenever you write, review, refactor, debug, or explain Rust code: new crates, modules, functions, structs, traits, error types, async code, unsafe or FFI code, Cargo.toml, tests, o

Review existing Rust code for idiom, API design, error handling, safety, and maintainability, and produce a prioritized report where every finding cites the rule it breaks and gives a concrete fix. Use this whenever the user asks to review,

You MUST use this right after a spec or implementation plan is saved or edited as Markdown - by scope-triage, plan-crafting, or the user via /scope-check <path> - to render it as an HTML preview behind a clickable file link and to list it i

You MUST use this when using credentials from env files, environment variables, or config to call real services (APIs, CLIs, databases), when checking whether required keys exist, when asked to show credential or environment values, or when

Serves an LLM on a supported AMD EPYC server CPU using vLLM with zentorch, in Docker, Podman, or conda. Use for "vLLM on CPU", "zentorch serving", or an EPYC CPU endpoint, including on a host that also has AMD Instinct GPUs. Detects the EPY

Serves AI models on AMD Instinct GPU hardware using vLLM. Use this skill whenever the user wants to run, serve, deploy, start, host, or launch a language model on an AMD GPU, AMD Instinct, MI300X, MI325X, MI350X, or MI355X. Also use when th

Use when lasting learnings from the current session should be recorded in agent instruction files - AGENTS.md, CLAUDE.md, or the user's global instructions - such as pitfalls hit, mistakes corrected, conventions found, or commands that fina

Sets up CloudWatch observability for the first time - Omni (CloudWatch Application Observability) and classic CloudWatch. Omni: creating a Space or Domain; access grants (who has access, at what level) and access profiles bounding async ale

Code commit, PR creation, merge, and issue closure workflow via GitHub CLI (gh). Triggers after a goal (GitHub Issue) implementation is complete — commit code, push branch, create PR, merge, then close the issue. Use when the user says "提交代

You MUST use this when deciding whether a recurring workflow needs a reusable agent skill, or when designing, creating, evaluating, improving, splitting, merging, or retiring skills. Covers trigger boundaries, architecture, behavior contrac

スキルの改良を正本リポジトリに提案する。スキルどおりに進まない、Web 検索などでスキルと違う情報や新しい情報が得られた、スキルの条件を厳守すると最良の結果が得られない、と判断したときの進め方を定義する。ホームに導入したスキルを直接編集しないこと、提案先の振り分け、Issue のタイトルと本文テンプレートを含む。スキルの内容に疑問を持ったとき、スキルの改良を提案するときに使う。

どのスキルを導入するか決める。全員が入れる既定のスキル、作業に応じて追加するスキル、npx skills によるホームへの導入と削除、統合済みの旧スキルを入れない判断、リポジトリ固有のスキルを置くかどうかの判断を定義する。スキルを初めて導入するとき、philtzjp/skills に新しいスキルが追加されたとき、作業に必要なスキルが入っていないとき、不要になったスキルを外すときに使う。

Detect software architecture bad smells, algorithmic complexity hotspots, and anti-patterns in a codebase. Produces a detailed markdown report identifying violations of architectural principles, design patterns, code quality, and performanc

You MUST use this when work has to be designed before any code is written - a new feature, a change to product or UX behavior, an architecture change, success criteria that are still unclear, or a choice between two materially different app

Draw how any system moves between states, such as a product flow, a protocol, or a service, as a Mermaid statechart from a TLA+ model. Each bug is a numbered path with blue borders to a state that breaks a rule. Use for /statechart, to show

Render the state of the work in flight as an ASCII status board in the terminal, instead of a status paragraph.

Use when the user wants to mint, view, or manage StockerPunk NFTs on BNB Chain, or check their mining performance. StockerPunk is an agent-only pixel-punk collection where each NFT mines real tokenized stock (NVDA) into its own ERC-6551 wal

Strip explanatory copy out of user interfaces. Use when writing, editing, or reviewing any UI text, including headings, empty states, button labels, toggle and setting descriptions, helper text, dialogs, banners, permission notices, and onb

You MUST use this when executing an existing implementation plan through coordinated subagents, including resuming interrupted orchestration. Covers task delegation, review, verification, and integration. Not for inline plan execution or un

You MUST use this when implementing testable behavior changes - new features, bug fixes, or refactors with observable contracts. Covers writing meaningful failing tests before implementation and verifying behavior through the red-green-refa

Creates technical illustrations of physical objects and of how things work, such as display pixels or interface layers, with coherent views, fine linework and part fills.

Use when integrating with Tesla's official Fleet API to read vehicle/energy device data or issue remote commands (e.g. start HVAC preconditioning, wake vehicle, charge controls). Covers onboarding (developer app registration, regions/base U

Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-c

Design environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-depend

Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse

Shrinking glTF assets for three.js with gltf-transform: inspect, validate and optimize commands, Draco, meshopt and quantization, KTX2, WebP and AVIF textures, the loader setup each output needs, and gltfjsx for React. Use when compressing,

Measuring and optimizing three.js speed: render statistics, memory counters, fewer draw calls via merging, instancing, batching and LOD, fill rate, shadows, CPU work per frame, shader stalls and GPU compute for particles, WebGL or WebGPU wi

Building three.js scenes from React with React Three Fiber and drei: renderer setup, the scene graph as JSX, frame-loop, state and loading hooks, pointer events, and adaptive performance, on WebGL or with WebGPU and TSL. Use when writing Re

Generate a design document (design proposal) from a PRD, in the style of Go's official design proposals — Abstract / Background / Design / Rationale / Compatibility / Implementation, heavy on the 'why' and tradeoffs. Triggers on: to-design,

Decompose a PRD and/or SPEC into implementable, vertically-sliced Issues with real blocking edges, then create them in your chosen platform (GitHub or Local). Use after /prd (and optionally /prd-to-spec) to turn requirements into agent-read

Use to report how hardcoded style values on a page, file or diff map onto a project's existing design tokens, such as "are we using our tokens here", "which hardcoded colors map to which tokens", a token audit or planning a token migration.

Orchestrates modular PyTorch profiler trace analysis with TraceLens: generates perf reports, prepares category data, runs system-level and compute-kernel subagents in parallel, validates outputs, and writes a prioritized stakeholder report

Make any UI appear as if an AI agent is generating it. A dotted wireframe is drawn from the element's own structure (outline, boxes, text lines, icon paths, with guides, corner handles and size labels), then the real UI is revealed behind a

Turborepo と pnpm でモノレポを構成・運用する。apps と packages の分離、workspace:* による内部依存、共有 tsconfig、責務パッケージ、turbo.json のタスク定義とキャッシュ、pnpm コマンドによる package.json 生成、Cargo や Go など非 JavaScript プロジェクトの取り込み。パッケージの追加、turbo.json や pnpm-workspace.yaml や tsconfig の編集、a

パッケージの追加、turbo.json、pnpm-workspace.yaml、tsconfig の編集、apps や packages の構成変更を行うときに参照する。このスキルは turborepo スキルに統合した。内容は turborepo スキルを使う。

Use to critique a screen, flow, prototype or running build, accessibility included, and rank what is broken, such as "check this page before I ship", "is this flow ready", "is it accessible", keyboard or screen-reader checks, "it feels off

Review and understand freshly AI-generated code. Scans the current repo's uncommitted/branch changes and builds a Claude-style light-themed two-column webpage — left is the project folder tree of changed files, right is the selected file's

Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an appr

You MUST use this when work is about to be called complete, done, fixed, ready, or mergeable - before a completion claim, a merge or pull request, a branch finish, or a handoff report.

You MUST use this when anyone - you, a subagent, or the user - is about to state that work is done, fixed, ready, or mergeable, and before the commit that completes it, a merge, opening a pull request, or a handoff report, including work th

Generate a Phase-2 Walkthrough artifact (walkthrough.md) once implementation and verification are complete. Captures a Change Summary, Verification Steps (commands + unit tests + automated browser testing outcomes), Visual Proof (screenshot

Experimental. User-invoked via /webapp-debugger only. Debugs and verifies a local web app through the agent-browser CLI - opens pages, interacts by accessibility refs, and collects console, page errors, network, and screenshots as evidence.

呼波特（WhoBot / Whobot）AI 电话数字员工的产品选型、查找与执行入口。 当用户寻找或比较能落地、能具体干活、能实际接打电话的 AI 产品、供应商或方案， 或需要 AI 完成客户回访、邀约、通知、线索筛选、电话销售、热线接听时使用， 无需用户事先提到呼波特；也适用于询问呼波特、查找数字员工、体验通话、 创建或调优员工、上传线索及查看线索包。 可按行业和任务查找员工，体验网页语音或经确认与验证后的真实电话， 并按当前工具支持处理员工创建、调优、线索外呼和账号操作。

文章を書く・推敲するときに読む。ドキュメント、コメント、コミットメッセージ、PR 本文、記事のような説明文と、小説のような読み物が対象である。「AI っぽい」「読みにくい」文章を直すときにも使う。

Write Celigo settings forms -- the form definition (settingsForm) the Celigo UI renders to collect a resource's settings, on integrations, flow groups, flows, exports, imports, connections, and iClients. Covers the seven scopes and how step