「vision」の検索結果

This skill should be used when the user asks to "write a sequel", "write a prequel", "start book two", "continue the series", "companion novel", "spin-off", "link books in a series", "carry characters into the next book", "series continuity", "series bible", "pitch my series", "series pitch", "multi-book pitch", or needs to plan, pitch, or keep canon consistent across multiple Story Skills projects. NOT for a standalone book (use story-init), continuity within one book (use revision-continuity), or a query, pitch, or synopsis for a single book (use submission).

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This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project. NOT for planning a scene's structure, subtext, or outcome (use scene-craft), drafting without an outline (use discovery-drafting), or revising existing chapters (use revision-continuity).

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Master product strategy, market analysis, competitive positioning, and long-term product vision. Define business models and craft go-to-market strategies that drive success.

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。

Applies the reasoning style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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Fix false-positive Cloud Run deploys where `gcloud builds submit --tag` silently fails but the follow-up `gcloud run services update --image :latest` succeeds against a stale image, producing a confident "Deploying... Done" message with pre-fix code still live. Use when: (1) gcloud builds submit prints "ERROR: (gcloud.builds.submit) Invalid value for [source]: Dockerfile required when specifying --tag" but the shell scrolls past it, (2) code changes verified locally aren't visible in production after a deploy that printed "revision ... has been deployed and is serving 100 percent of traffic", (3) Chaining `gcloud builds submit` and `gcloud run services update` in a single copy-paste block, (4) Using the mutable `:latest` tag for Cloud Run deploys.

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 consistent characters, environments and props in Guaardvark's Cast Library and train LoRAs for them locally (reference photos → vision bible → sample plan → approved samples → training). Use when the user wants the same face or object across images, videos, a music video, or a Film Crew production, or asks to "train a LoRA".

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Build and refine production Blender geometry through semantic vertex/edge/face editing, BMesh, subdivision control cages, Boolean construction, Displace relief, remesh, sculpt preparation, retopology, and deliberate surface connections. Use after blockout for hard-surface, product, organic, architectural openings, terrain/relief, or reference-driven assets when shape quality, topology, continuity, or realistic part integration matters.

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Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.

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Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.

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。

Game designer for creative vision, GDD creation, and narrative design. Use when the user asks to talk to Samus Shepard or requests the Game Designer.

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ACTIVATE when developing long-term strategy, setting OKRs, creating vision statements, building SWOT analyses, designing strategic frameworks, defining company direction, planning multi-year initiatives, or aligning organizational strategy with business goals. Executive must think through market positioning, competitive advantage, and measurable strategic objectives.

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。

Launch browsers, desktop apps, and extensions in your sandbox

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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。

Set up bundle IDs, capabilities, signing certificates, provisioning profiles, and encrypted signing sync with the asc cli. Use when onboarding a new app, rotating signing assets, or sharing them across a team.

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。

Chinese Word document formatting toolkit for .docx/.doc/.wps workflows. Use when Codex needs to diagnose document formatting, fix mixed Chinese/English punctuation and spacing, apply official/academic/legal/custom presets, normalize tables and page numbers, preserve or output Word revision marks, convert plain text or Markdown into formatted DOCX, or batch/script document cleanup for Chinese official documents.

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。

Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.

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Use when user asks to analyze an image, describe image contents, or answer questions about a picture. iFlytek Image Understanding (图片理解) — analyze and answer questions about images using Spark Vision model. WebSocket API, pure Python stdlib, no pip dependencies.

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。

Revenue decomposition, segment breakdown, geographic revenue split, product-line waterfall, revenue mix analysis, business segment performance, divisional revenue, revenue concentration, customer revenue dependency, channel revenue analysis

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。

Earnings sentiment analysis, analyst estimates vs guidance, earnings surprise history, consensus sentiment, earnings revision trends, analyst rating changes, earnings beat miss track record, guidance accuracy, whisper numbers, pre-announcement sentiment

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Select, integrate, and operate multi-model image-generation gateways with model-specific schema discovery, version policy, asynchronous jobs, webhooks, spend approval, safe inputs and artifacts, data-governance review, and billing observability. Use for comparing or building against fal.ai, Replicate, or Together AI image APIs, including controlled failover; do not use for direct model-provider APIs, local inference, training, dedicated endpoint provisioning, video generation, or general image editing.

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 plan, direct, finish, and QA provider-independent mono or stereo 180-degree, 360-degree, spherical, spatial, wide-FOV, and immersive video. It covers format choice, capture rigs, stitching, nadir and seam management, stereoscopic comfort, attention direction, stabilization, ambisonic and spatial audio, titles, captions, accessibility, edit, color, VFX, projection metadata, Vision Pro, YouTube, headset delivery, device QA, and privacy, location, and likeness rights. Do not use it for interactive XR apps, volumetric/world-model generation, game-engine experiences, or provider-specific video generation prompts.

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 editing and montage direction for AI agents producing generated videos, ads, trailers, social clips, explainers, product videos, documentary-style pieces, and music- or beat-driven cuts. Use when planning, revising, QAing, or handing off an edit: story structure, shot selection, continuity, montage theory, rhythm and pacing, J/L cuts, match cuts, cutaways, transitions, beat sync, platform timing, source/generated asset management, revision workflow, and composition 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。