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概要と使いどころ
Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.
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。
Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.
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。
Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension. Use when user requests structural review or editorial review of structure
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Manage SVG icons as framework components using Denji CLI. Use when the user needs to add, remove, list, or manage SVG icons in React, Preact, Solid, Qwik, Vue, or Svelte projects. Triggers include requests to "add an icon", "set up icons", "manage SVG icons", "remove an icon", "list icons", or any task involving Iconify icons as framework components.
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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API 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。
Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).
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。
Write what the work found into the project's knowledge record, so the next session inherits it instead of re-deriving it. Use the moment something worth keeping exists: a fact taken from a source, a rule worked out, a decision or conclusion, a measurement, a correction to a recorded value, or a question left open - and check for unrecorded findings before implementation, handoff (including a PR), or finishing work. Record within the user's write authorization. The first write creates GROUNDING.yaml. Covers one-report update, add, set, review, same and distinct, judgments with a falsifier, and background capture.
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。
Pushing GitHub Actions changes, resolving push rejection, requesting upload exceptions.
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。
Universal desktop application controller for Windows. Automate any desktop app — send messages, click buttons, type text, take screenshots, and interact with native Windows applications. Use this skill when the user asks to: "control my computer", "automate desktop", "send a message on [any chat app]", "click on [something]", "type into [app]", "take a screenshot", "操控电脑", "自动化桌面", "给某某发消息", "打开某某软件", "截个屏", or any task involving interacting with Windows desktop applications. WeChat triggers: "send a WeChat message", "message someone on WeChat", "打开微信发消息", "给某某发微信", "微信发送", "微信发消息", "用微信发", "帮我发微信". Email/Web triggers: "打开邮箱", "发邮件", "send email", "open browser", "操控浏览器", "网页操作". Supports: WeChat, WeCom, DingTalk, Feishu/Lark, QQ, Telegram, Slack, Teams, any browser-based web app, and any other Windows desktop application. Inspired by OpenAI's playwright-interactive skill, combining code-based automation with visual screenshot feedback loops. ROUTING: For web/Electron apps (email, Slack, browser), prefer Playwright with CSS selectors for speed and precision. For native desktop apps (WeChat, QQ, DingTalk), use Win32 API automation.
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。
Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.
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。
Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
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 verifying a story or ticket implementation against its spec and acceptance criteria - per-AC verdicts with file:line evidence and proving tests, code reuse and design conformance, dead code, stale docs, comment discipline. Finds the spec from an explicit path, the ticket key in the branch name, or plan files - and asks rather than guessing. For defect-hunting code review use dotnet-techne-code-review instead. Keywords: story QA, acceptance criteria, spec conformance, AC coverage, definition of done, dead code check, reuse check, comment check, verify story, QA this branch.
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。
Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.
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。
Finalize code changes for production readiness by removing duplicate logic, auditing hardcoded values, verifying branch/worktree intent, verifying build integrity, structuring clean commits with Conventional Commits format, and forcing a post-publish branch/worktree cleanup decision.
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 condensing, decondenseing, refactoring, or improving existing skills. Covers the full skill lifecycle from verbose → sharp → maintained.
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 BEFORE any verbose CLI run, large file read, doc conversion, or near-context handoff — decision tree keyed by intent citing the canonical token-saving asset.
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 deciding 'should this be a skill or a rule?', creating/improving/reviewing agent skills, SKILL.md frontmatter, or procedure sections — even without saying 'skill-writing'.
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 processing code review feedback (bot or human) before changing anything — triages, verifies and pushes back with technical reasoning — even when the user just says 'fix the comments'.
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 running verbose CLI commands — wraps them with rtk (Rust Token Killer, third-party Apache-2.0; upstream reports 60-90% token savings). Covers installation, configuration, and usage patterns.
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 creating, rewriting, or significantly improving a README based on the actual repository structure, commands, and intended audience.
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 BEFORE saving any .md under .augment/, dist/agent-src*/, or agents/ — scans umlauts, German function words, and German phrases outside DE:/EN: anchor blocks. Hard gate per language-and-tone.
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 writing, generating, or improving Pest tests for Laravel — clear intent, good coverage, maintainable structure, and alignment with project testing conventions.
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 removing AI-writing tells from deliverable prose — posts, articles, drafts. Triggers on 'make this sound less like AI', 'humanize this draft', 'this reads like ChatGPT wrote it'.
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。