「apply」の検索結果

Use when an agent is asked to draft, rewrite, edit, review, polish, copyedit, simplify, humanize, or create written prose, including creative writing, essays, posts, scripts, speeches, emails, documentation, product copy, and other style-sensitive text. Apply George Orwell's six rules and ASD-STE100 Simplified Technical English as a plain-English discipline while preserving the user's intended meaning, audience, tone, and explicit constraints.

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Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).

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。

Conduct a structured literature review on a given topic by defining a search strategy, applying inclusion and exclusion criteria, extracting key findings, and synthesizing results into a coherent academic review. Use when the user requests literature review or provides relevant inputs for this workflow.

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。

Inspect, extract, OCR, create, merge, split, reorder, rotate, annotate, fill, redact, compress, secure, and verify PDF documents while preserving source files and visual fidelity. Use when working with one or more .pdf files; converting documents to or from PDF; extracting text, tables, images, metadata, forms, or page ranges; applying true redactions or signatures; diagnosing malformed, encrypted, scanned, or inaccessible PDFs; or validating that a PDF transformation preserved the intended content and layout.

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 clean code principles when generating or modifying implementation code. Enforces function focus, naming clarity, complexity management, error handling, and self-documenting style. Use when the user mentions 'clean code', 'code quality', 'coding guidelines', or 'implementation quality'. Loaded automatically by the code-generating molecules (code-forge, refactor-safely, bug-fix). This skill governs the craft of writing individual code units -- not architecture (see architecture), not security posture (see secure-coding), not test structure (see test-quality), and not refactoring workflows (see refactor-safely).

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 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).

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 security-conscious thinking when generating or modifying code. Enforces trust boundary awareness, input validation, injection prevention, secrets management, and defense-in-depth authorization. Use when generating code that handles user input, authentication, authorization, database queries, external APIs, or file operations, or when the user mentions 'security review', 'secure this', 'check for vulnerabilities', 'trust boundary', 'input validation', or 'OWASP'. Loaded automatically by the code-generating molecules (code-forge, refactor-safely, bug-fix). This skill governs the security posture of generated code -- not architecture (see architecture) and not code craft (see clean-code).

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 test quality principles when generating or reviewing test code. Enforces Arrange-Act-Assert structure, one behavior per test, assertion quality, test isolation, meaningful naming, and test data management. Use when writing tests, reviewing test code, or when the user mentions 'write tests', 'test this', 'test quality', 'test review', 'improve tests', or 'test structure'. Loaded automatically by the code-generating molecules (code-forge, refactor-safely, bug-fix). This skill governs the craft of writing individual test cases -- not what to test (that is driven by the code being implemented) but how to write tests that are reliable, readable, and maintainable.

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 DDD tactical patterns when working with domain code, and validate proposed domain models before approval (design mode). Enforces aggregate design, value objects over primitives, entity identity rules, and bounded context boundaries. Use when creating or modifying domain models, designing aggregates, working in the domain layer, or when the user mentions 'domain', 'aggregate', 'value object', 'entity', 'bounded context', or 'DDD'.

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。

Produce short-form video with Pika (pika.art) — its effect-driven tools (Pikaffects, Pikadditions, Pikaswaps, Pikaframes, Pikascenes, Pikatwists) and audio-driven lip sync (Pikaformance), plus text-to-video and image-to-video. Use when a task calls for playful, stylized, meme-able social clips, or for applying generative effects and object swaps to real uploaded footage, and when deciding whether Pika is the right engine versus a higher-fidelity model. Covers model versions, credit/plan economics, the fal.ai API route, prompt construction, iteration/repair, capability limits, and consent/rights rules for uploaded real-person media. Not a general "best video model" chooser.

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 Resemble AI Chatterbox for text-to-speech and voice-cloning workflows, including local open-weight Chatterbox, Chatterbox Multilingual, Chatterbox Turbo, and Resemble-hosted Chatterbox API routes. Apply when an agent must choose a Chatterbox variant, prepare reference-voice inputs, control emotion/paralinguistic delivery, plan multilingual TTS, handle PerTh watermarking, or make production decisions about consent, privacy, licensing, latency, and audio QA.

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。

Produce 3D assets with Meshy (meshy.ai) through its REST API and web platform — text-to-3D, image-to-3D, multi-image-to-3D, AI texturing/PBR, remesh/topology control, UV, and auto-rigging/animation. Use this skill when an agent must generate a mesh from a prompt or reference image, control polygon count and topology, apply or restyle textures, rig a humanoid for animation, export to GLB/FBX/OBJ/USDZ/STL for Unity/Unreal/Blender, drive Meshy's asynchronous job API (auth, polling, streaming, webhooks, credits, rate limits), review generated meshes for game/film/print readiness, or decide whether Meshy is the right tool at all. Not for editing an existing hand-modeled asset's geometry, hard-surface CAD with exact dimensions, or guaranteed-watertight manufacturing parts.

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 Adobe Firefly Services to generate, edit, expand, fill, match, composite, and upscale still images through the current REST APIs. Apply when selecting Firefly Image 5 versus Image 3/4 or custom models, implementing authenticated asynchronous image workflows, using style or structure references, building product composites, handling provenance and enterprise rights, or diagnosing Firefly image jobs in production. Exclude Firefly video, audio, and unrelated Creative Cloud APIs.

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, edit, composite, stream, and production-review still images with OpenAI GPT Image models. Use when an agent must choose between GPT Image 2 and legacy GPT Image/DALL-E integrations, select the Image API or Responses API, build prompts and reference-image workflows, use masks or multiple inputs, preserve identity or brand details, render in-image text, handle output files and partial images, estimate cost and rate limits, migrate deprecated image code, or apply OpenAI image safety, consent, privacy, provenance, and rights requirements. Do not use for OpenAI video or audio 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。

Apply vaex in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses vaex or its strengths fit the task.

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 validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.

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 umap-learn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses umap-learn or its strengths fit the task.

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 statsmodels in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statsmodels or its strengths fit the task.

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 single-cell rna qc in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses single-cell rna qc or its strengths fit the task.

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 spark optimization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses spark optimization or its strengths fit the task.

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 pytorch geometric in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses pytorch geometric or its strengths fit the task.

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 risk metrics calculation in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses risk metrics calculation or its strengths fit the task.

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 sql optimization patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql optimization patterns or its strengths fit the task.

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 sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.

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。