「sla」の検索結果

This skill orchestrates autonomous discovery of brand materials across enterprise platforms (Notion, Confluence, Google Drive, Box, SharePoint, Figma, Gong, Granola, Slack). It should be used when the user asks to "discover brand materials", "find brand documents", "search for brand guidelines", "audit brand content", "what brand materials do we have", "find our style guide", "where are our brand docs", "do we have a style guide", "discover brand voice", "brand content audit", or "find brand assets".

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Translate SKILL.md and README.md files into multiple languages for sharing skills internationally

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 Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

Bidirectional Figma skill — read designs and write back to Figma. READ: extract tokens, map Vue/Reka UI components, translate Auto Layout to Tailwind. WRITE: push components, screens, and design tokens from code to Figma. Requires /figma:figma-use skill before every use_figma call.

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Translate a vague request or GitHub issue into a concrete, testable spec using five structured elements (problem, scope, acceptance criteria, edge cases, success criterion) before writing any code. Prevents scope creep, PR churn, and mid-implementation pivots.

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。

A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

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 for creating, reviewing, or debugging GitHub Copilot reusable prompt files and slash commands in Visual Studio Code, including .github/prompts/*.prompt.md, inputs, context links, agent selection, and tool selection. Do not use for always-on rules, multi-step Agent Skills, custom personas, or VS Code extension commands.

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Native agent-to-agent language for compact multi-agent messaging. A shared tongue agents speak directly, not a translation layer. 340+ atoms across 7 domains; 3x smaller than natural 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。

Implements multi-language support using i18next, gettext, or Intl API with translation workflows and RTL support. Use when building multilingual applications, handling date/currency formatting, or supporting right-to-left languages.

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Think and work like an expert Comparative Medicine Researcher. Use when a task calls for Comparative Medicine Researcher judgment. Reasons from species biology, translational validity, and the 3Rs through model-validity frameworks, IACUC protocols, ARRIVE 2.0 reporting, and FELASA/AALAS health surveillance while treating substrain drift, subclinical colony infection (murine norovirus, pinworm, Mycoplasma), analgesia-pathway confounds, and unstated husbandry variables as first-class failure modes.

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Macro-driven trade idea generation, translating macroeconomic views into concrete stock ideas, sector selection from macro regime, international trade ideas via ADRs, cross-border idea sourcing, macro catalyst mapping to equity positions

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Systematic trade idea generation framework, fundamental quantitative and qualitative screening pipeline, catalyst-driven trade structuring, macro-to-micro idea translation, 20-60 day long-short portfolio management process

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。

Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Use Microsoft Azure Speech in Foundry Tools for media-production speech workflows: speech-to-text, fast and batch transcription, diarization, captions/subtitles, real-time transcription, text-to-speech neural and HD voices, SSML, custom/personal voices, text-to-speech avatars, speech/video translation, localization, quotas, regions, privacy, consent, rights, and 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。

Use this skill when an agent needs AssemblyAI for speech-to-text or speech-understanding work in media production, including pre-recorded, synchronous short-file, and real-time streaming transcription; speaker diarization or speaker identification; captions and subtitles; timestamps; language detection, code-switching, or transcript translation; audio intelligence such as summaries, chapters, topics, entities, key phrases, sentiment, content moderation, profanity filtering, and PII redaction; webhooks, scaling, rate limits, retention, security, consent, and QA for podcasts, interviews, captions, call recordings, and edit workflows.

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Produce and understand audio with OpenAI request-based audio APIs and audio-capable chat models, including text-to-speech, transcription, translation, multimodal audio input/output, model routing, prompt and performance direction, artifact custody, approval gates, and safety/rights review. Use for non-realtime OpenAI audio production; route continuous live voice agents to the separate realtime voice skill.

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Use NVIDIA Speech NIM microservices for speech production and voice workflows, including self-hosted ASR/STT, TTS, text translation, speech-to-speech pipeline design, Riva Python client integration, deployment planning, GPU/runtime sizing, licensing/privacy review, and production QA for transcription, captioning, voice agents, localization, and synthetic voice output.

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Provider-independent production guidance for translating measured audio features into deterministic video timing and motion. Use for beat-, onset-, phrase-, energy-, silence-, or spectrum-reactive visualizers, edits, typography, and generated compositions; not for music-video concept direction, audio generation, or a runtime-specific recipe.

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 AI lip sync and visual dubbing with Sync Labs (sync.so) — choose the right model (sync-3, lipsync-2, lipsync-2-pro, lipsync-1.9.0-beta, react-1), build the POST /v2/generate request, host inputs, handle async jobs via polling or webhooks, run batch dubbing/localization, and review output for sync accuracy and identity preservation. Use when an agent must re-voice, dub, translate, personalize, or re-time the mouth of a talking-head video (or animate a still face from audio), or must debug a failed/rejected Sync generation. Also covers consent, likeness, and rights obligations for editing a real person's face.

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。

Translate a creative brief, brand system, or reference set into an original, portable visual-direction system for image and video production. Use for art direction, reference analysis without imitation, design tokens, palette and contrast, typography, composition, texture and material, lighting, lens and camera language, motion language, cross-shot continuity, accessibility, production handoff, visual review, and repair; do not use for provider-specific API syntax or model-specific prompt recipes.

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 design direction for AI-generated media. Use when translating narrative, brand, advertising, product, social, film, or worldbuilding intent into sets, locations, props, color and material palettes, era research, graphic and environmental design, continuity bibles, image/video prompt planning, reference strategy, production constraints, iteration notes, handoff specs, and QA.

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Direct cinematic shots and coverage for AI-assisted content production. Use when an agent must translate story intent into framing, shot size, lens and perspective, camera position, movement, blocking, screen direction, lighting-aware coverage, continuity, shot specifications, or diagnose why generated or filmed shots do not cut together.

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Provider-independent production guidance for AI agents creating or retouching appetizing food and beverage images, recipe videos, restaurant/menu visuals, CPG ads, ecommerce product images, beverage pours, packaging/lifestyle shots, and social food clips, including truthfulness, labeling/claims discipline, styling, prompt translation, platform variants, accessibility, rights, alcohol, and QA.

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Provider-independent cinematic trailer production for AI agents creating movie-style trailers, teaser trailers, launch trailers, game trailers, documentary trailers, series promos, and high-impact generated-media previews. Use for trailer strategy, structure, shot planning, voiceover/title-card copy, music and sound-design direction, generated-video prompt translation, edit pacing, platform/runtime choices, delivery handoff, rights/claims caveats, and trailer 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。