596 件 ・ 関連度順
概要と使いどころ
Generate and operate Tencent Hunyuan video through the managed TokenHub HY-Video-1.5 API or official local HunyuanVideo repositories. Use for text-to-video, image-to-video, hosted job lifecycle, pricing and region planning, local checkpoint selection, hardware and acceleration, prompt design, licensing, safety, provenance, and delivery 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。
Use Google Lyria music generation models through Google Cloud / Vertex AI / Gemini Enterprise Agent Platform for production music beds, songs, vocal tracks, image-conditioned music, short-form social audio, ad music, and video scoring. Covers Lyria 2, Lyria 3 Clip, and Lyria 3 Pro model selection, prompt construction, rights/privacy checks, SynthID/C2PA provenance, artifact custody, and 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。
Provider-independent production workflow for agents assembling, auditing, executing, and handing off ComfyUI node-graph workflows for image, video, upscale, inpaint, conditioning, and batch media generation; use when a task involves ComfyUI workflow JSON/API graphs, model and custom-node inventories, reproducibility, provenance, safety/rights review, local or cloud execution hygiene, or production 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。
Provider-independent production workflow for AI agents assembling generated or source media into HyperFrames HTML/CSS/JS videos. Use for HyperFrames composition planning, scene architecture, media custody, animation/timing, captions/audio, deterministic preview/render QA, accessibility/flashing checks, provenance ledgers, and delivery handoff.
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。
Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.
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。
Provider-independent production workflow for creating Manim-based explainer animations. Use when an agent must plan, code, render, QA, or hand off precise math, science, data, diagram, algorithm, or concept animations with Manim, including storyboard, scene architecture, formulas, coordinate systems, voiceover timing, accessibility, reproducibility, provenance, and delivery decisions.
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 assembling generated or sourced media into React/Remotion videos. Use when an agent must plan, build, render, review, variant-render, or hand off Remotion compositions with media custody, captions, audio, animation, accessibility, provenance, QA, and delivery 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。
Plan, generate, edit, and quality-check still images with Google's Gemini native image models (Nano Banana), including Gemini 3.1 Flash Image, Gemini 3.1 Flash-Lite Image, Gemini 3 Pro Image, and legacy Gemini 2.5 Flash Image. Use for model and API-route selection, conversational editing, multi-reference composition, thinking and Google Search grounding, resolution/aspect-ratio control, exact-text and brand workflows, SynthID/provenance, safety, privacy, rights, and migration from legacy Gemini or Imagen image endpoints. Do not use for Veo/video generation or Gemini Live/audio 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。
Produce and review production image-generation and image-editing workflows with Amazon Nova Canvas on Amazon Bedrock, including native InvokeModel payloads, safe authentication, validation, retries, cost controls, provenance, and lifecycle migration checks. Use when a task names Nova Canvas, amazon.nova-canvas-v1:0, Bedrock image generation, Canvas inpainting/outpainting/background removal, image conditioning, color guidance, image variation, virtual try-on, or Canvas fine-tuning.
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 media quality control and delivery direction for generated images, video, audio, ads, product content, social clips, broadcast and streaming assets, localization packages, and mixed-source edits. Use when an agent must define final export specs, inspect technical and perceptual quality, check captions/audio/color/accessibility/provenance/rights, create delivery sheets, version filenames, checksums, manifests, package deliverables, set acceptance or rejection criteria, and perform final delivery QA before handoff to a platform, broadcaster, streamer, client, or ad trafficker.
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 reference media analysis for generated-media production. Use when an agent must analyze reference images, videos, audio, style boards, product shots, brand assets, mood boards, storyboards, performances, prior cuts, or client examples and translate them into safe, non-copying direction, prompts, QA criteria, provenance records, and handoff notes for image, video, audio, avatar, or post-production agents.
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 asset continuity and version management for generated-media production. Use when an agent must track generated or reference assets, prompts, seeds, model/tool versions, manifests, approvals, edit decisions, derivatives, localization variants, metadata/provenance, and continuity QA across image, video, audio, avatar, product-ad, social, explainer, or film-style pipelines.
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 this skill to turn generated, captured, scanned, or modeled 3D output into production-ready standalone assets for DCC, real-time engine, web, or interchange delivery. It covers requirements, topology repair, scale and axes, UVs, PBR baking, LODs, static and skeletal readiness, rig handoff, optimization, glTF/USD/FBX delivery, validation, QA, and rights/provenance. Do not use it for provider-specific generation prompts, full scene/world building, mocap operation, or exact CAD/manufacturing 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。
Provider-independent workflow for producing terminal, IDE, documentation, browser, desktop, and application demonstration videos. Use when choosing authentic capture, browser automation, synthetic UI, or hybrid treatment; scripting actions and narration; resetting demo state; protecting secrets and personal data; directing cursor/callouts; creating crop variants; and reviewing workflow truth, readability, accessibility, and provenance.
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 quality assurance for AI-generated and AI-assisted media. Use when reviewing, accepting, revising, or reporting on images, video, audio, avatars, ads, product content, social clips, explainers, localization, mixed-source edits, captions, accessibility, provenance, model metadata, safety/policy, and delivery readiness.
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 governance workflow for verifying and correcting human- or model-generated video descriptions. Use for pre-caption critique and post-caption revision, aspect-by-aspect factual review, critique precision/recall/constructiveness, second-stage peer review, calibration, appeals, versioned triplets, provenance, and acceptance reporting; not for pixel/audio QA, accessibility captioning, model training, or creative shot direction.
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 game trailers and promos, including announcement, gameplay, launch, DLC/update, store-page videos, social cutdowns, and generated/engine-capture hybrid trailers. Use when planning, scripting, capturing, generating, editing, localizing, reviewing, or QAing game marketing videos where gameplay truthfulness, platform/store constraints, ratings, accessibility, claims, CTA metadata, and rights provenance matter.
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。
Workflow for orchestrating reproducible omics pipelines with workflow engines and clear execution provenance.
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。
Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-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。
Mine AI coding-agent session transcripts into structured, gate-validated GPU-kernel optimization records for the wiki. Use when asked to turn vibe-coding sessions, Codex rollout logs, or Claude Code project transcripts into wiki records; to summarise what a kernel-optimization session achieved; to build or extend a session-trace store; or to re-run and validate one. Also use when asked how a session-derived record's number, snippet, or provenance was established.
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。
Mine a per-kernel optimization trace — a git repository capturing successive versions of one kernel being optimized — into structured, gate-validated optimization-experience records for the GPU kernel wiki. Use when asked to distill an optimization run, a kernel_opt trace directory or a version ladder into wiki records; to report what such a run actually achieved; to build, extend, re-run or validate the staging store behind those records; or to explain how a trace-derived record's number, snippet or provenance was established.
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。
Performs deployment with pre-flight checks, atomic replacement, post-deploy verification, provenance (proving the environment serves the exact commit you pushed), and automatic rollback. Use before any deployment to staging, preview, or production.
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。