Implement a piece of work based on a spec or set of tickets.
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。
3,606 件 ・ 関連度順
概要と使いどころ
Implement a piece of work based on a spec or set of tickets.
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。
Splits large markdown documents into smaller, organized files based on level 2 (default) sections. Use if the user says perform shard document
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 a custom checklist for the current feature based on user 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。
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
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 an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
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 a custom checklist for the current feature based on user 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。
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
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 an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
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。
Plan, write, and maintain automated tests in any language, with worked examples in Python and JavaScript/TypeScript. Use this skill whenever the user wants to write or plan tests, apply TDD (red-green-refactor), structure unit tests with arrange-act-assert and given-when-then naming, choose between fakes, stubs, and mocks, set up dependency injection for testability, build integration tests with testcontainers, database seeding, or transaction rollback, write Playwright or Cypress end-to-end tests, use property-based testing with Hypothesis or fast-check, create pytest fixtures or factory_boy factories, review coverage or mutation testing, or triage flaky tests in CI. Also trigger for "test pyramid", "test trophy", "test isolation", "mocking", "snapshot tests", "golden files", "parametrized tests", "make the tests pass".
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。
TypeScript-only skill for generating and debugging high-resolution AI images through sub2api OpenAI-compatible APIs. Use this whenever the user wants a TS/Node sub2api image generator, 4K image generation, stream=true /v1/images/generations or /v1/responses image generation, Cloudflare 524 timeout avoidance, partial image capture, or SUB2API_BASE_URL/SUB2API_API_KEY based image generation.
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 architecture and design documents for implemented code changes with risk-based depth selection. Automatically evaluates risk signals, layer spread, and change magnitude to choose documentation level (A/B/C). Use after completing a feature, before merging a PR that touches multiple layers or sensitive areas (auth, infra, migrations, payments), or when an implemented change needs a design record or ADR.
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。
Diagnose and fix CJK (Korean, Japanese, Chinese) text-wrapping issues in web UIs. Systematically traces mid-syllable breaks, orphaned glyphs, and awkward line splits through a layered fix strategy covering global CSS cascade, component-library overrides, headline balancing, and translation-level phrase binding. Produces an evidence-based diagnosis and verification report before any code change is applied.
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 when a bug has multiple plausible causes across layers — competing hypotheses, validation loops, evidence-based conclusions — even when the user just says 'why is this happening?'.
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 debugging PHP with Xdebug — breakpoints, step-through, dual-container setup, IDE configuration, header-based routing — even when the user just says 'why does this blow up on request X'.
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 working with feature flags — Laravel Pennant, gradual rollouts, A/B testing, scope-based flags — even when the user just says 'hide this behind a flag' without naming Pennant.
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。
Two agents co-create an artifact through turn-based iteration until both signal DONE.
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。
Role-based collaboration pattern (Drafter, Reviewer, Breaker). Assigns roles to available agents via prompt parameters. Use when starting multi-agent dev work.
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。
Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai
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。
Framework de orquestração multi-agent para colaboração autônoma de IA. Use ao construir equipes de agentes especializados trabalhando juntos em tarefas complexas, quando você precisa de colaboração de agentes baseada em papéis com memória, ou para workflows em produção exigindo execução sequencial/hierárquica. Construído sem dependências de LangChain para execução enxuta e rápida.
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。
Load this skill whenever the project contains audio or video content, media players, podcasts, video embeds, or any <audio>/<video> elements. Under no circumstances publish audio or video without captions, transcripts, and audio descriptions where required. Absolutely always apply WCAG 1.2 criteria for time-based media.
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。
Load this skill whenever the project contains static or interactive maps, embedded map widgets (Google Maps, Leaflet, Mapbox, OpenStreetMap), or any geographic visualizations. Under no circumstances embed a map without a text-based alternative conveying the same information. Absolutely always ensure map controls are keyboard-accessible and that all meaningful map content is available without the visual map.
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。
Load this skill whenever the project contains SVG graphics — inline SVGs, external SVG files, SVG icons, SVG illustrations, or SVG-based data visualizations. Under no circumstances use SVG without proper accessible titles, descriptions, and ARIA roles where required. Absolutely always add <title> and <desc> to meaningful SVGs and set role="img" with aria-labelledby pointing to those elements. Also apply this skill whenever the project accepts untrusted or user-uploaded SVG content, which requires sanitization before rendering.
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。