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概要と使いどころ
Defines where UI components belong in Operately (TurboUI-first). Use when creating, changing, reviewing, or migrating UI components, adding features that need UI, or deciding whether to refactor legacy app UI in app/assets/js/components or app/assets/js/features. Covers pure TurboUI components, component reuse, the app bridge pattern, and legacy migration scenarios.
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。
Turns approved AC/SRS into an executable test plan (scenarios, seed, selector gaps) for the test-loop workflow. Use for independent test-plan generation from stable requirements.
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。
Resolve iOS and Android differences using Platform API and native modules in React Native. Use when platform branching or native-module integration is the primary task; defer cross-platform feature implementation such as push notifications.
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。
Turn acceptance criteria into an executable test plan (scenarios, seed, selector gaps) before generating E2E code. Use when ACs exist but no runnable test plan does yet.
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。
Drives iOS/Android mobile devices via Appium MCP. Runs a preflight, picks local emulator/simulator/USB or a device cloud, captures screenshots/page source into a named evidence dir. Use for verifying mobile bugs, E2E tests, and navigating real device clouds (LambdaTest/BrowserStack).
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。
Standardizes mobile UI audits, performance/scroll checks, RTL verification, and state-specific testing on iOS/Android.
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。
Apply motion design principles for mobile apps covering timing curves, transitions, gestures, and performance-conscious animations. Use when implementing screen transitions, gesture-driven interactions, shared-element animations, or optimizing animation frame rates on iOS, Android, or Flutter.
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。
Probe for hardcoded secrets, injection surfaces, unguarded routes, business logic flaws, and platform-specific weaknesses across backend (Node, Go, Java, Python, Rust), frontend (React, Angular, Vue), and mobile (iOS, Android, Flutter) codebases. Use when performing security audits, vulnerability scans, secrets detection, or penetration testing.
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。
Plans risk-based test portfolios and acceptance evidence; does not write or execute tests.
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。
Reviews lyrics and prose for AI-written patterns (abstract noun stacking, over-explained metaphors, cliche escalation, missing idiosyncrasy, prose AI tells). Advisory Warning/Info severity — flags issues, does not block or rewrite. Use when reviewing lyrics for authenticity or before generation to catch AI-sounding language.
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。
Produces human-sounding text that avoids detectable AI writing patterns. Activates on any writing task — tweets, emails, articles, bios, captions, reports, copy, messages, LinkedIn posts, cover letters, README files, or any content where the output must not read as AI-generated. Enforces banned vocabulary, structural variety, punctuation discipline, accuracy rules, and voice calibration. Use when the user says "write," "draft," "rewrite," "make this sound human," "anti-slop," "not AI," or any variation of wanting authentic-sounding output.
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。
Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.
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。
Find similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.
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。
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
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。
Turn one confirmed security finding into a disclosure-ready GitHub advisory with root cause, proof of concept, impact, and source evidence. Use for reporting an established vulnerability, not discovering or validating one.
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。
Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training 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。
Run Hugging Face models in JavaScript or TypeScript with Transformers.js, WebGPU, or WASM across browser, Node.js, Bun, and Deno. Use for client-side or JS-runtime inference, not Python training.
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 (SKILL.md files), modify and improve existing skills, and design skill descriptions for accurate triggering. Use when the user wants to create a new skill from scratch, edit an existing skill, optimize a skill's description, or convert a workflow they just demonstrated into a reusable 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。
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision 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。
Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.
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 a repository-grounded AppSec threat model covering assets, trust boundaries, attackers, abuse paths, and mitigations. Use only for explicit threat-modeling requests, not general architecture or code review.
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。
Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.
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。
Systematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.
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 the `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.
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。