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概要と使いどころ
Use when a human directly requests review and publication of an existing diff or delegated work. Not for merging pull requests, force pushes, history rewrites, or deployment.
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 a workflow, plan, diff, or completed work cycle must be checked for practices that punish the project later. Not for source, remote, credential, publish, deploy, or irreversible changes.
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 says "simplify this diff" or asks for a compression pass over a change-set. Not for dead-code sweeps: use deslop.
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。
Independently review one Proto UI autonomous-maintenance remediation in a fresh read-only context. Use after pui-remediate produces a packet and actual diff. Classify technical-completion eligibility without making product or integration decisions.
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。
Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).
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 "test what I just changed", "/devtest", "smoke test my change", "verify my latest commit", "测一下我刚改的", "看看刚改的功能能不能跑", "跑一下登录看看", or similar requests to verify a recent code change against a running Android or iOS device. The skill reads the git diff, infers the affected UI surface, generates a focused test plan, drives the app via ui-mcp + mobile-mcp, captures logs via log-mcp, and produces a Markdown report via report-mcp.
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。
Fast headless browser for QA testing and site dogfooding. Navigate any URL, interact with elements, verify page state, diff before/after actions, take annotated screenshots, check responsive layouts, test forms and uploads, handle dialogs, and assert element states. ~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a user flow, or file a bug with evidence. Use when asked to "open in browser", "test the site", "take a screenshot", or "dogfood this".
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。
Ship workflow: detect + merge base branch, run tests, review diff, bump VERSION, update CHANGELOG, commit, push, create PR. Use when asked to "ship", "deploy", "push to main", "create a PR", or "merge and push".
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。
Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.
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。
Post-ship documentation update. Reads all project docs, cross-references the diff, updates README/ARCHITECTURE/CONTRIBUTING/CLAUDE.md to match what shipped, polishes CHANGELOG voice, cleans up TODOS, and optionally bumps VERSION. Use when asked to "update the docs", "sync documentation", or "post-ship docs".
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。
Clean up a diff for reuse, simplicity and efficiency, then apply the fixes.
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。
Director-level AI art prompt creation for Eastern beauty series, including Eastern fantasy / gu feng Chinese beauty character art, classical Eastern beauty, realistic and modern Eastern beauty, Cosplay展会摄影 / official convention cosplay photography with AAA game character campaign and Vogue editorial quality, 古风闺蜜 / AncientFemaleCompanionship two-person ancient Chinese female companionship photography, 东方美学图鉴 / 小红书图鉴 cover and content systems for 四大美人、四大才女、十二花神、东方神女、敦煌飞天、朝代服饰、东方器物、东方神话, 甜系纯欲生活写真 / SweetHomeGirl lifestyle portraits built around realism, feminine charm, romantic feeling, story moment, and natural attraction, new-Chinese-style fashion editorials, magazine portraits, worldbuilding, mythic role design, costume/styling systems, cinematic composition, lighting, negative prompts, and model-specific prompt variants. Use when the user asks for 东方美人, 东方审美, 东方美学图鉴, 小红书图鉴, 图鉴封面, 图鉴正文, 四大美人, 四大才女, 十二花神, 东方神女, 敦煌飞天, 朝代服饰, 东方器物, 东方神话, Cosplay展会摄影, coser, Coser, 漫展摄影, 展会摄影, ChinaJoy, Bilibili World, Tokyo Game Show, 游戏展台, 官方展会摄影, AAA游戏角色Cosplay, 古风闺蜜, AncientFemaleCompanionship, Ancient Female Companionship, 闺阁夜话, 姐妹古风写真, 古代闺阁生活, 灯下梳发, 共读, 夏夜纳凉, 守夜, 江南同行, 湖亭共伞, 山寺同行, 灯会夜游, 柔美, 妩媚, 成熟, 优雅, 高贵, 清冷, 温婉, 英气, 神性, 慵懒, 东方幻想, 古风美女, 古典东方美人, 宋韵, 唐宫, 江南, 洛神, 青瓷, 昆曲, 国风人物, 汉服女子, 写实东方美人, 现代东方审美, 新中式, 东方高级感, SweetHomeGirl, 甜系纯欲生活写真, 甜系纯欲, 甜美女友感, 真实女生, 自然抓拍, 故事感, 女友视角, lifestyle girlfriend portrait, 仙侠/武侠/宫廷/江南/敦煌风 AI 绘画提示词, image prompt optimization, style word expansion, character series concepts, or prompt packs for Midjourney, Stable Diffusion, ComfyUI, DALL-E, Gemini, Seedream, or other image generation tools.
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。
为 verl-omni 接入或修复 MSProbe 训推一致性采集，生成打点代码、样本关联日志和诊断启动脚本。 用于 diffusion/omni 的 rollout 与 actor 数据采集、dump 缺失或配对失败；不负责精度根因分析。
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。
在实现适配器前对候选模型做分析。确定模型实现来源（transformers / 模型目录 / diffusers）、结构特征、内存约束下的逐层量化建议（可选）及 MoE 融合权重风险。适用于用户询问模型适配可行性或做适配前分析时使用。识别为 DiT 扩散模型时，索取推理仓路径后回传 `next_step: model-adapt`，由 orchestrator 委派 `msmodelslim-model-adapt`（DiT 扩展节承接）。
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 NowStack Mobile app icons and logos. Use for `/ns icon`, app icon, iOS logo, launcher icon, favicon, or logo refresh. By default ALWAYS generate exactly 8 different iOS-quality icon candidates first, then finalize one icon and derive app/web assets.
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。
Uses OpenSEO as the measured SEO data plane for keyword volume and difficulty, SERPs, ranked keywords, backlinks, local SEO, rank tracking, Search Console, GA4, and site audits. Use for measured SEO metrics, indexing evidence, organic analytics, local rankings, backlink profiles, or rank trackers. Prefer OpenSEO over guessed metrics when connected; otherwise continue with qualitative web research and mark numeric metrics unknown.
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。
On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".
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。
kbo-cli 의 작업 브랜치에서 develop 으로 향하는 새 PR 을 만든다. 단순 push+gh pr create 가 아니라, /code-review 로 diff 검토 → biome check (format+lint) → tsc typecheck → bun build → bun test 5단계 검증을 통과시킨 뒤에 PR 을 올린다. PR 제목·본문은 모두 한국어 Angular 컨벤션 (CLAUDE.md 참조). 사용자가 "PR 올려줘", "pull request 만들어줘", "PR 생성해줘", "이거 PR 로 정리해줘", "리뷰 받게 올려줘" 같은 PR 생성 의도를 보이거나, 기능 작업이 끝나서 머지 흐름을 시작하려는 맥락이면 반드시 이 스킬을 쓴다. push 만 하고 끝나는 단순 push 요청과 다르다 — push 직전에 검증을 강제하는 게 핵심이다.
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。
Reads task files from tasks/<branch>/ and the diff against the detected base ref, then generates a polished PR description in markdown for the user to copy-paste.
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。
当用户需要找出产品/服务的独特竞争优势来写定位文案或广告主轴时调用。 适用于：产品同质化严重不知怎么差异化、新产品定位、重新审视现有产品的竞争优势。 核心信号：用户在问"我的产品和别人有什么不同""怎么让客户选我而不是对手"。 不适用于：已有明确USP只需要执行、不需要竞争分析的产品（如垄断市场）。
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。
Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.
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 accommodation plans for learning differences. TRIGGERS - Use when user needs help with accommodation-plan related tasks.
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。
GitHub/GitLab 贡献真实性核验：逐个查看 PR 的实际 diff、按领域与深度分类统计、核对贡献量级与"Contributor/核心贡献者/Maintainer"类表述是否相符，识别刷绿、刷 PR、刷 star 与领域错配（如自称 AI Infra 实则全是前端提交）。凡是要评估某人 GitHub 的含金量就使用本技能——包括用户说"他这个 GitHub 怎么样""这个 contributor 有多少水分""开源经历值多少""帮我看看这个账号"时。也在 /resume-audit 需要核验代码托管材料时调用。
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 this skill when read_file is not enough to read a PDF — it failed, timed out, or returned garbled or empty text, or the question is about figures, charts, layout, embedded images, attachments, or form-field values. Covers content inventory, text extraction, page rasterization for visual inspection, embedded image/attachment/table/form-field extraction, and choosing the right reading strategy for different document types (text-heavy, scanned, slide-decks, forms, data-heavy). Do NOT use this skill for PDF creation, form filling, merging, splitting, watermarking, or encryption — use the pdf skill instead.
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。