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Use when the user wants to write text in a specific 16-minds (MBTI) personality's voice — articles, blog posts, essays, SNS投稿, replies, or rewriting existing AI-generated text to feel less AI-like. Triggers on Japanese phrases like 「ENFPで書いて」「INTJ風の記事」「人間っぽく書き直して」「AI臭抜いて」, English equivalents like "write as INFJ" / "humanize", or any 16-minds command with a `--write` / `--rewrite` flag. Personality (one of intj/intp/entj/entp/infj/infp/enfj/enfp/istj/isfj/estj/esfj/istp/isfp/estp/esfp) must be specified or inferable.

Design and verify audio finishing for Japanese or English short-form social video. Use for 短尺SNS, BGM/効果音, テンポ調整, 意味ベース配置, dialogue-first mixing, or existing rough cut audio finishing with pinned local assets and internal-review QA.

Write or edit Japanese articles, reports, guides, and public-facing drafts so the argument is clear and the prose reads naturally. Preserve the author's meaning, evidence, and voice when revising. Use for requests to write from a topic or notes, remove AI臭 / AIっぽさ, improve awkward wording or structure, or format notes as 箇条書き, チーム共有, 社内共有, a decision note, handoff, Slack post, or Teams post. For existing prose, ask Gemini through Antigravity CLI to rewrite naturally by default, then audit against the source; use the current model if unavailable. Do NOT use for ordinary Japanese answers, progress updates, technical explanations, code review, English-only prose that is not a bullet-formatting request, or an explicitly incompatible format.

Unified pull request workflow: prepare a safe branch, run read-only review, fix Required findings, create/update a GitHub draft PR, and verify CI plus automated review comments. Subcommands: `create` (simplify + PR, skip code review), `review` (read-only findings only), `fix` (Required fixes + commit, no push), `simplify` (simplification only), `verify` (existing PR checks/reviews only). Use when creating or reviewing PRs, self-reviewing changes, simplifying PR changes, or requesting "PR作成", "レビュー", "簡素化". Do NOT use for responding to review comments or a lightweight review of the current working diff without the PR workflow (use the built-in /code-review instead).

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

日本語の概要は準備中です。原文の説明を表示しています。

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

日本語の概要は準備中です。原文の説明を表示しています。

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

日本語の概要は準備中です。原文の説明を表示しています。

Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep. TRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update. DO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).

日本語の概要は準備中です。原文の説明を表示しています。

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

日本語の概要は準備中です。原文の説明を表示しています。

Facilitate Kimball-style dimensional (data warehouse / BI) modeling sessions through conversation, producing DimML (Dimensional Modeling Language, YAML) as the primary artifact plus a bus-matrix + star-schema HTML. Use whenever the user wants to design fact tables, dimensions, a star schema, or a bus matrix — declaring grain, choosing SCD types, identifying conformed dimensions and measures (additivity) — by grilling one step at a time. Also invoke for refining an existing DimML, or when the user says "ディメンショナル・モデリング", "ディメンションモデリング", "スタースキーマ", "バスマトリクス", "ファクト表を設計したい", "グレインを決めたい", "DimML を育てたい", "Kimball で整理したい".

Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

日本語の概要は準備中です。原文の説明を表示しています。

Skill for analyzing a project's existing UI in Vue. Activates when the user uses /pwdev-uiux:scan or asks to "analyze the project UI", "map existing components", "understand project patterns", or "create project skill". Generates .planning/ui/project-ui-skill.md with project-specific patterns and tokens.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants tests planned for existing code or a test audit — unit, integration, E2E scenarios exportable to Playwright, playwright-cli UI checks, a coverage matrix — 'criar testes para o UserService', 'plano de testes E2E do login', 'auditar a cobertura'. Writes .planning/feat/features/{slug}/plan.md. Do NOT use for code review (feat-review), for implementing features (feat-feature) or to run an existing plan (feat-exec).

日本語の概要は準備中です。原文の説明を表示しています。

Design consultation: understands your product, researches the landscape, proposes a complete design system (aesthetic, typography, color, layout, spacing, motion), and generates font+color preview pages. Creates DESIGN.md as your project's design source of truth. For existing sites, use /plan-design-review to infer the system instead. Use when asked to "design system", "brand guidelines", or "create DESIGN.md". Proactively suggest when starting a new project's UI with no existing design system or DESIGN.md. (gstack)

日本語の概要は準備中です。原文の説明を表示しています。

Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.

日本語の概要は準備中です。原文の説明を表示しています。

Integrate Expo and React Native into an existing native iOS or Android app. Use when the user mentions brownfield, embedding React Native in a native app, AAR/XCFramework, or adding Expo to an existing Kotlin/Swift project. Covers both the isolated approach and the integrated approach.

日本語の概要は準備中です。原文の説明を表示しています。

Reduce an oversized SKILL.md by checking whether an existing skill already covers the excess sub-workflow before creating a new child skill only when none fits. Load when a skill exceeds 200 lines and compress-skill determines excess content is genuinely CORE, when the same sub-workflow appears in multiple skills, or when universal-skill-creator or improve-skills identifies a coherent sub-capability. Also triggers on "split this skill", "extract a sub-skill", "this skill is doing too much", or "make this skill reusable". Always checks for an existing home before creating a new skill.

日本語の概要は準備中です。原文の説明を表示しています。

Research a skill domain before building or improving a skill. Searches academic papers, practitioner blogs, and GitHub skill repos in parallel to find current best practices, domain gotchas, and existing skill patterns. Called by universal-skill-creator and improve-skills before writing any skill. Also load directly when the user asks to research a domain for a skill, find existing skills on a topic, discover best practices for a skill, check what research exists before building an agent skill, or says "what does current research say about", "find best practices for".

日本語の概要は準備中です。原文の説明を表示しています。

Bootstrap full agent infrastructure for an existing, already-coded project without modifying a single line of source code. Surveys the repo, infers conventions from manifests, README, git history, and source samples, then generates the missing AGENTS.md, docs/architecture.md, docs/product-soul.md, ADR backfill, and seeded docs/memory/ files. Asks the user only about gaps the repo cannot reveal. Load when the user asks to "retroactive project setup", "backfill agent infrastructure", "bootstrap agents for this existing repo", "onboard agents to a legacy codebase", "set up agents without touching code", or "fill in missing agent context for this project".

日本語の概要は準備中です。原文の説明を表示しています。

Build a LiveAvatar integration end-to-end — assesses the user's existing stack, recommends the optimal path, and guides implementation. Use when: (1) Building a new LiveAvatar integration, (2) Adding a real-time avatar to an app or site, (3) Connecting LiveAvatar to an existing AI pipeline, (4) User mentions LiveAvatar, real-time avatar, interactive avatar, conversational avatar, or lip-sync avatar, (5) Deciding between Embed, FULL Mode, and LITE Mode, (6) Migrating from HeyGen Interactive Avatar to LiveAvatar.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "既存プロジェクトのタスクを整理して", "技術的負債をIssue化して", "このコードベースを改善するタスクを洗い出して", "GitHub Projectに健全化タスクを登録して", "break down what needs to be fixed in this project", "surface tech debt as issues", or "create a project board for cleanup work". Diagnoses pain points and codebase health of an existing project, then registers improvement tasks as GitHub Issues and a Project Board.

Initialize a new Convex project from scratch or add Convex to an existing app. Use when starting a new project with Convex, scaffolding a Convex app, or integrating Convex into an existing frontend.

日本語の概要は準備中です。原文の説明を表示しています。

When the user requests to add academic papers to a BibTeX bibliography file while maintaining format consistency and sourcing from appropriate repositories. This skill handles 1) Reading existing BibTeX files to understand formatting conventions, 2) Searching for academic papers across multiple sources (OpenReview for conference papers, arXiv for preprints), 3) Extracting proper BibTeX metadata from conference pages or arXiv entries, 4) Determining appropriate citation format (@article vs @inproceedings) based on publication venue, 5) Appending new entries while preserving existing file structure and formatting. Triggers include requests to 'add to ref.bib', 'update bibliography', 'cite papers', or when working with academic reference files.

日本語の概要は準備中です。原文の説明を表示しています。