「generating」の検索結果

Design and review APIs with suggestions for endpoints, parameters, return types, and best practices. Use when designing new APIs from requirements, reviewing existing API designs, generating API documentation, or getting implementation guidance. Supports REST APIs with focus on endpoint structure, request/response schemas, authentication, pagination, filtering, versioning, and OpenAPI specifications. Triggers when users ask to design, review, document, or improve APIs.

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Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.

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 self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling. Use when creating dashboards, building interactive reports, or generating shareable HTML files with charts and filters that work without a server.

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。

Review contracts against your organization's negotiation playbook, flagging deviations and generating redline suggestions. Use when reviewing vendor contracts, customer agreements, or any commercial agreement where you need clause-by-clause analysis against standard positions.

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 applies brand guidelines to content creation. It should be used when the user asks to "write an email", "draft a proposal", "create a pitch deck", "write a LinkedIn post", "draft a presentation", "write a Slack message", "draft sales content", or any content creation request where brand voice should be applied. Also triggers on "on-brand", "brand voice", "enforce voice", "apply brand guidelines", "brand-aligned content", "write in our voice", "use our brand tone", "make this sound like us", "rewrite this in our tone", or "this doesn't sound on-brand". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).

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 AI coding agents and exploration mode to generate TestDriver 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。

Generate tests by exploring your app with the AI vision agent

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。

JeecgBoot UniApp3 移动端 CRUD 代码生成器。Use when user asks to generate UniApp3/mobile/APP CRUD pages, create mobile list/form pages, or says '生成移动端代码', '生成APP页面', 'uniapp代码生成', '移动端CRUD', '生成uniapp页面', '手机端页面', '移动端模块', 'generate uniapp code', 'mobile CRUD', 'uniapp page', 'APP端代码', '生成小程序页面', '生成H5页面'. Also triggers when user mentions generating mobile pages for an existing backend module, or wants to add mobile support to JeecgBoot modules. Even if user just says '给XX模块加个移动端页面' or '做个手机端的XX功能', this skill should trigger.

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Use when testing a bundle-plugin locally before release — generating dev-marketplace environments, verifying component discovery, running hook smoke tests, and validating cross-platform readiness

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Use when generating project structure for new bundle-plugins, adding or removing platform support (Claude Code, Cursor, Codex, OpenCode, Gemini CLI, OpenClaw), updating platform manifests, or migrating hooks and configuration between platforms

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TypeScript-first schema validation and type inference. Use for validating API requests/responses, form data, env vars, configs, defining type-safe schemas with runtime validation, transforming data, generating JSON Schema for OpenAPI/AI, or encountering missing validation errors, type inference issues, validation error handling problems. Zero dependencies, compact core (~5kb gzipped; zod/mini ~1.9kb).

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Fill a Stage5 final remarks template using condensed discussion markdown from all reviewers and optional rating changes. Use when generating final remarks after Stage3/Stage4 discussion.

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Manages software projects end-to-end — decomposing work into tasks, tracking progress across sprints, generating status reports, and integrating with tools like Jira, Linear, GitHub Issues, and Trello. Use when the user requests project management or provides relevant inputs for this workflow.

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Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.

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Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. Use when the user requests ticket triage or provides relevant inputs for this workflow.

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Think and work like an expert Mathematical Analyst. Use when a task calls for Mathematical Analyst judgment. Reasons from function-space topology, convergence modes, and constant-dependent inequalities (Hölder, Sobolev, Gronwall) through compactness theorems (Rellich-Kondrachov, Banach-Alaoglu), Calderon-Zygmund and Schauder estimates, and Lax-Milgram while treating limit-integral swaps without dominated convergence, boundary-degenerating constants, weak-versus-classical regularity gaps, and concentration-compactness loss as first-class failure modes.

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Think and work like an expert AI Researcher. Use when a task calls for AI Researcher judgment. Reasons from data generating processes, inductive biases, and compute-data-algorithm trade-offs through train/val/test discipline, seed sweeps, ablation ladders, and standards like NeurIPS reproducibility checklists, model cards, and lm-eval-harness, while treating data leakage (Kapoor & Narayanan taxonomy), benchmark contamination, spurious correlations, and reward-model overoptimization as first-class failure modes.

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Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.

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。

Testing workflow skill for finding high-value test candidates, writing focused tests, generating realistic workloads, reviewing test value, and diagnosing test-suite health. Use for prompts about what to test next, adding or improving tests, reviewing whether a test is worth keeping, low-signal or flaky tests, workload scenarios, or test-suite trust. Do not use for general QA discussion unless the user wants a concrete testing workflow or artifact.

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Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).

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Plan and scope production work with Odyssey's real-time interactive video world models (odyssey.ml — Odyssey-1/Odyssey-2/Starchild-1/Agora-1). Use when a request involves generating video that a user steers live with keyboard, text, speech, or controller input (streamed frames that respond in the moment), or when someone must decide between Odyssey-style interactive generation and offline clip generators (Sora/Veo/Kling) or downloadable 3D / game engines. Covers what is verified versus announced, current access routes, latency and coherence limits, feasible-now versus speculative use cases, pricing/access terms, and content/rights considerations. This is a fast-moving research-stage product; the skill teaches honest scoping, not a promise of production reliability.

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Operate AudioShake's cloud source-separation service (developer.audioshake.ai) to split a recording into stems. Use when a task involves isolating vocals, drums, bass, guitar, piano, keys, strings, or winds from music; separating dialogue / music / effects (DME) for film-TV post-production, localization, and dubbing; splitting a mixed recording into one stem per speaker; lyric transcription or word-level alignment; music detection/identification; or speech denoise/dereverb. Covers the Tasks API job lifecycle (assets, targets, formats, polling vs webhooks, credits, limits), choosing which stem targets a production actually needs, reviewing separated stems for bleed / artifacts / transient smearing / phase problems and repairing them, the rights and consent questions raised by separating copyrighted or third-party recordings, and the decision boundary for when a local open model such as Demucs is the better route than the API. Not for generating or synthesizing new audio, mixing, or mastering.

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Use for Stability AI Stable Audio production work: selecting Stable Audio hosted API or open-weight models, generating or editing music, loops, sound effects, foley, beds, stingers, and sonic-branding audio from text or source audio, planning rights-safe uploads, setting model parameters, polling asynchronous jobs, and reviewing generated audio for media projects.

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。

Provider-independent production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation assets. Use when planning, scripting, storyboarding, generating, reviewing, localizing, or QAing animation whose primary purpose is learning.

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。