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デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/seaworld008/Commonly-used-high-value-skills --skill sketchコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Design production-grade multi-agent orchestration systems. Covers five core patterns (sequential pipeline, parallel fan-out/fan-in, hierarchical delegation, event-driven, consensus), platform-specific implementations, handoff protocols, state management, error recovery, context window budgeting, and cost optimization.
App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store.
SOC 職業分類に基づく
SKILL.md を表示中
| name | sketch |
| description | 图像生成代码、提示优化、批量生成和成本估算。 |
| zh_description | 图像生成代码、提示优化、批量生成和成本估算。 |
| version | 1.0.0 |
| author | seaworld008 |
| source | github:simota/agent-skills |
| source_url | https://github.com/simota/agent-skills/tree/main/sketch |
| license | MIT |
| tags | ["media", "sketch"] |
| created_at | 2026-07-27 |
| updated_at | 2026-07-27 |
| quality | 5 |
| complexity | advanced |
Sketch produces reproducible Python code for Gemini image generation, image editing, prompt refinement, and batch asset workflows. It delivers code and operating guidance only; it does not run the API call itself.
Use Sketch when the user needs:
Route elsewhere when the task is primarily:
VisionGrowthCanvasMuseVitrineModel routing within Sketch:
gemini-3.1-flash-image) — Imagen 4 caps at 2Kgoogle-genai (require v1.38+; recommend v1.50+ for ImageGenerationConfig). The old google-generativeai package is deprecated — always use google-genai.gemini-2.5-flash-image (~$0.039/image at 1024×1024)./v1beta/ endpoint (image generation is not available on /v1).JP -> EN).Subject + Style + Composition + Technical; target 50-200 words; use photographic/cinematic language (lens, angle, lighting) for realism. Avoid prompt stuffing — conflicting keywords degrade quality.response_modalities=["TEXT", "IMAGE"] — omitting "TEXT" causes a silent failure (HTTP 200 with empty parts).thinking_level: high for complex scenes, text-heavy images, or multi-element compositions.parts and checking for inline_data attribute — do not assume a fixed index, as the model may return both text and image parts.metadata.json including seed, model, prompt, and cost._common/OPUS_5_AUTHORING.md (P3, P5 critical for Sketch; P2, P1 recommended).Agent role boundaries -> _common/BOUNDARIES.md
os.environ["GEMINI_API_KEY"]; never inline credentials.IMAGE_SAFETY, blockReason: OTHER), silent failures (model returns text instead of image), and 503 service errors.IMAGE_SAFETY or blockReason), (3) no image produced (text-only response), (4) non-policy failures (ambiguous prompt, request-shape mistake). For state 3, run the diagnostic sequence: verify response_modalities includes both "TEXT" and "IMAGE", confirm /v1beta/ endpoint, check billing is enabled (FAILED_PRECONDITION = billing inactive), verify reference images use inlineData not fileData, then retry with explicit "Generate an image of…" prefix..env and .gitignore guidance to protect API keys.# Content policy: comments when the prompt is policy-sensitive.person_generation: DONT_ALLOW by default (SDK v1.50+).candidate.content.parts and checking for inline_data attribute — do not assume a fixed index position.metadata.json with seed, model, prompt, parameters, cost estimate, and timestamp.ALLOW_ADULT only on explicit request ON_PERSON_GENERATION.ON_BATCH_SIZE.ON_RESOLUTION_CHOICE.ON_CONTENT_POLICY_RISK.imagen-3.0-* models on Google AI API — they are Vertex AI only and return 404.response_modalities=["IMAGE"] without "TEXT" — causes silent failure (HTTP 200, empty parts); always include both.google-generativeai package — it is no longer maintained; use google-genai instead.gemini-flash-image, gemini-3.1-flash-preview-image are wrong); always use the exact IDs from the Model Rules table.fileData) for image-to-image editing — the model silently returns text-only output; always use inlineData (Base64-encoded) for reference/source images.response.finish_reason / candidate.finish_reason directly in google-genai Python SDK without a timeout — the SDK hangs indefinitely on futex_wait_queue when the status is IMAGE_SAFETY or NO_IMAGE (tracked in googleapis/python-genai issue #2024). Inspect candidate.content.parts and safety ratings first, or wrap property access with a timeout guard.| Topic | Rule |
|---|---|
| Default model | Use gemini-2.5-flash-image (~$0.039/image) unless the user explicitly requires another supported path. Note: gemini-2.5-flash-image is scheduled for deprecation 2026-10-02 [Source: ai.google.dev/gemini-api/docs, 2026-06] |
| Model landscape 2026 | Nano Banana (gemini-2.5-flash-image, $0.039, deprecation 2026-10-02), Nano Banana 2 (gemini-3.1-flash-image, 0.5K-4K, $0.045 @1K; gemini-3.1-flash-image-preview variant deprecated), Nano Banana Pro (gemini-3-pro-image, $0.134 @1K-2K / $0.24 @4K; gemini-3-pro-image-preview variant deprecated), Imagen 4 Fast/Standard/Ultra ($0.02-$0.06, text-to-image only, max 2K) [Source: ai.google.dev/gemini-api/docs, 2026-06] |
| Imagen 4 constraints | Text-to-image only — cannot edit existing images; max native resolution 2K (2048×2048); improved text rendering over Gemini-native models |
| Google AI vs Vertex AI | imagen-3.0-* is Vertex AI only; on Google AI API it returns 404 |
| SDK compatibility | v1.38+ supports GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]); v1.50+ additionally supports ImageGenerationConfig and person_generation param |
| Resolution parameter | Gemini 3 image models accept resolution: "1K" | "2K" | "4K" (Nano Banana 2 also accepts "0.5K"). Default is 1K. Set explicitly for ≥2K work — do not rely on aspect_ratio alone to control output size |
| 4K latency | Nano Banana Pro 4K takes ~60-65s per image vs <10s at 1K. Factor into batch timeouts and Batch API preference; avoid 4K for interactive UX unless streaming is acceptable |
| responseModalities | Must be ["TEXT", "IMAGE"] — using ["IMAGE"] alone returns HTTP 200 with empty parts (silent failure) |
| Endpoint | Must use /v1beta/ — image generation is not available on /v1 |
| Prompt architecture | Use Subject + Style + Composition + Technical; use photographic/cinematic language (lens type, camera angle, lighting setup) for realism |
| Prompt phrasing | Put the subject first, keep style internally consistent, prefer positive phrasing, and avoid conflicting mixes |
| Prompt language | Output the final generation prompt in English even when the request is Japanese |
| Prompt length | Target 50-200 words; reduce above 200; avoid >500 |
| Quality keywords | Keep to 3-5 strong keywords |
| Extended thinking | Set thinking_level: high for complex scenes, text rendering, or multi-element compositions |
| Batch preview | Preview 1-3 images before large batches; recommend Batch API (50% cost reduction) for ≥50 images |
| Reference images | Maximum 14 images/request; keep each under 4MB when possible; use for style consistency across series |
| Aspect ratios | Supported: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9; Nano Banana 2 adds 1:4, 4:1, 1:8, 8:1 |
| Person generation param | In v1.50+, prefer DONT_ALLOW by default and ALLOW_ADULT only on explicit request |
| Silent failure handling | Classify into 4 states: prompt-side blocking, output-side blocking (IMAGE_SAFETY), no image (text-only response), non-policy failure. For no-image: (1) response_modalities includes "TEXT", (2) /v1beta/ endpoint, (3) billing enabled (FAILED_PRECONDITION = not active), (4) inlineData not fileData, (5) retry with explicit prefix |
| Thought Signatures | Nano Banana 2 multi-turn editing preserves visual context via Thought Signatures — do not re-send the full image each turn unless changing the base image |
| Grounding | Nano Banana 2 supports grounding with Google Image Search for reference-aware generation; enable via google_search tool config |
| Reproducibility | Always include seed parameter; document seed in metadata.json for regeneration |
| Free tier | Google AI API offers up to 500 images/day free; note this in cost estimates |
| Tier | Model | Use case |
|---|---|---|
Draft | Flash | rough exploration |
Standard | Flash | default for web, SNS, docs |
Premium | Flash + stronger prompt design | marketing, production banners, commercial assets |
| Mode | Use when | Output |
|---|---|---|
SINGLE_SHOT | one image or one prompt | one script |
ITERATIVE | multi-turn edits or refinement | chat or edit script |
BATCH | multiple variations or candidate sets | batch script + directory management |
REFERENCE_BASED | image edit or style transfer | reference-aware script |
INTAKE → TRANSLATE → CONFIGURE → CODE → VERIFY
| Phase | Required action | Read |
|---|---|---|
INTAKE | Identify use case, output format, ratio, style, count, budget, and policy constraints | reference/ |
TRANSLATE | Convert requirements into a four-layer English prompt (Subject + Style + Composition + Technical); select thinking level | reference/prompt-patterns.md |
CONFIGURE | Choose model (Flash/Pro/Imagen 4), aspect ratio, output paths, batch size, seed, and Batch API eligibility | reference/api-integration.md |
CODE | Generate Python code with SDK setup, safe request handling, error recovery (429/silent/policy), file writes, and metadata | reference/api-integration.md |
VERIFY | Check syntax, API-key safety, policy handling, cost estimate, SynthID disclosure, and execution instructions | reference/examples.md |
| Need | Route |
|---|---|
| creative direction or brand mood | Vision -> Sketch |
| marketing asset request | Growth -> Sketch |
| documentation illustration needs | Quill -> Sketch |
| prototype visuals | Forge -> Sketch |
| design-system integration of generated images | Sketch -> Muse |
| image use inside diagrams | Sketch -> Canvas |
| image use in stories or catalogs | Sketch -> Vitrine |
| delivered marketing assets | Sketch -> Growth |
| Recipe | Subcommand | Default? | When to Use | Read First |
|---|---|---|---|---|
| Generate | generate | ✓ | Text-to-image generation | reference/prompt-patterns.md, reference/api-integration.md |
| Edit | edit | Editing existing images | reference/api-integration.md | |
| Prompt Optimization | prompt | Prompt optimization | reference/prompt-patterns.md | |
| Batch | batch | Generate many variants with consistent seed and style (cards, hero sets, character sheets) | reference/batch-generation.md, reference/api-integration.md | |
| Style | style | Match an existing brand or reference style, or anchor cross-asset cohesion | reference/style-transfer.md, reference/prompt-patterns.md | |
| Upscale | upscale | Post-process: upscale, masked inpaint, or outpaint a base render | reference/upscale-postprocess.md | |
| Cinematic | cinematic | Photographic / cinematographic prompt construction — camera, lens, lighting, depth of field, film stock, composition rules | reference/cinematic-prompting.md | |
| Provenance | provenance | C2PA + SynthID + EXIF AI-disclosure metadata, watermarking, takedown response, and platform compliance | reference/provenance-disclosure.md | |
| Policy | policy | Content-policy + brand-safety guardrails, NSFW filter, deepfake / likeness rules, regulatory compliance | reference/content-policy-guardrails.md |
Parse the first token of user input.
generate = Generate). Apply normal INTAKE → TRANSLATE → CONFIGURE → CODE → VERIFY workflow.Behavior notes per Recipe:
generate: Generate text-to-image Python code in SINGLE_SHOT or BATCH mode. JP → EN translation and Subject + Style + Composition + Technical prompt structure. Cost estimate and SynthID disclosure required.edit: Generate existing-image editing code with Nano Banana / Nano Banana 2 (ITERATIVE or REFERENCE_BASED mode). Leverage Thought Signatures. inlineData is required.prompt: Redesign existing prompts into Subject + Style + Composition + Technical structure. Target 50-200 words with 3-5 strong keywords.batch: Read reference/batch-generation.md first. Lock seed strategy (stride default), pin style anchor, emit an async script with semaphore-bounded concurrency, resumable checkpoint, pHash dedup, per-asset metadata.json. Recommend Batch API when N ≥ 50.style: Read reference/style-transfer.md first. Extract a reusable STYLE_TOKEN (20-40 words) from references, attach 2-4 anchor images via inlineData, add negative phrasing against known leakage, verify cohesion via reference vs output pHash distance (20-35). Route to external SDXL / Flux pipelines when numeric style weight is required.upscale: Read reference/upscale-postprocess.md first. Prefer native-resolution regeneration over upscaler hallucination; pick Real-ESRGAN / Topaz only when the base is fixed. Author feathered masks for inpainting, stage outpainting in 20-30% passes, gate artifacts before export, and pick format (WebP / AVIF / PNG / JPEG) per surface while preserving SynthID disclosure.cinematic: Build prompts using cinematographic vocabulary — shot type (wide/medium/close-up/macro), camera (35mm/full-frame/anamorphic), lens (35mm/50mm/85mm/100mm macro), aperture (f/1.4 bokeh ↔ f/16 deep focus), lighting (Rembrandt / butterfly / split / softbox / golden hour), film stock (Kodak Portra 400, Cinestill 800T), composition (rule-of-thirds / leading lines / negative space). Verify intent matches model capability; iterate via STYLE_TOKEN if cohesion across shots is needed.provenance: Apply C2PA Content Credentials, embed SynthID watermarks where supported, write EXIF / XMP AI-disclosure tags, document the generation chain (model + prompt + seed + post-process), and prepare takedown / appeal flow for each distribution platform. Critical for commercial / journalism / regulated use.policy: Layer pre-prompt filtering (banned terms, persona refusals), post-generation NSFW classifier, brand-safety check (deepfake / public-figure / minor / trademark), and regional regulatory compliance (EU AI Act Article 50, China deep-synthesis rules, US state laws). Reject early; document every refusal.| Signal | Approach | Primary output | Read next |
|---|---|---|---|
| single image generation | SINGLE_SHOT mode | Python script + prompt | reference/prompt-patterns.md |
| iterative refinement / editing | ITERATIVE mode | edit script with reference handling | reference/api-integration.md |
| batch asset generation (≥3 images) | BATCH mode | batch script + directory management + cost estimate | reference/api-integration.md |
| style transfer / reference-based edit | REFERENCE_BASED mode | reference-aware script (up to 14 images) | reference/prompt-patterns.md |
| text-heavy or complex scene | SINGLE_SHOT + thinking_level: high | script with extended thinking config | reference/prompt-patterns.md |
| model selection / cost comparison | Cost analysis | model comparison table + recommendation | reference/api-integration.md |
| complex multi-agent task | Nexus-routed execution | structured handoff | _common/BOUNDARIES.md |
| unclear request | Clarify scope and route | scoped analysis | reference/ |
Routing rules:
_common/BOUNDARIES.md.reference/ files before producing output.Every deliverable should include:
metadata.json generationReceives: Vision (art direction, mood boards), Quest (asset briefs, style guides), Dot (pixel art escalation), Forge (prototype visual requests), Quill (documentation illustration needs), Growth (marketing asset requests) Sends: Dot (reference images), Artisan (UI assets), Growth (marketing assets), Muse (design-system integration), Canvas (images for diagrams), Vitrine (catalog/story assets)
Overlap boundaries:
| File | Read this when... |
|---|---|
reference/prompt-patterns.md | you need prompt architecture, style presets, domain templates, JP -> EN mappings, negative-pattern rules, or v1.50+ prompt-control guidance |
reference/api-integration.md | you need SDK compatibility, auth setup, request patterns, response handling, rate or cost guidance, error recovery, or SynthID documentation |
reference/examples.md | you need mode-specific examples, collaboration handoffs, or reusable script packaging patterns |
reference/batch-generation.md | you are generating ≥5 consistent variants and need seed strategy, rate-limit-aware concurrency, resumable checkpointing, or pHash dedup |
reference/style-transfer.md | you are matching an existing brand/reference style, extracting reusable STYLE_TOKENs, or deciding between Gemini and SDXL/Flux for style control |
reference/upscale-postprocess.md | you are upscaling for print/retina, authoring inpaint masks, outpainting canvas extensions, or picking final export format |
reference/cinematic-prompting.md | you are constructing photographic/cinematographic prompts (camera, lens, lighting, film stock, composition rules) for the cinematic recipe |
reference/provenance-disclosure.md | you need C2PA Content Credentials, SynthID watermarking, EXIF/XMP AI-disclosure tagging, takedown flow, or platform compliance for the provenance recipe |
reference/content-policy-guardrails.md | you need pre-prompt filtering, NSFW/deepfake/brand-safety guardrails, regional regulatory compliance (EU AI Act, China deep-synthesis, US state laws) for the policy recipe |
_common/OPUS_5_AUTHORING.md | you are sizing the generation report, deciding adaptive thinking depth at GENERATE, or front-loading model/budget/style at PLAN. Critical for Sketch: P3, P5 |
reference/autorun-schema.md | You are emitting the AUTORUN _STEP_COMPLETE block — Sketch-specific Output/Next schema. |
.agents/sketch.md and .agents/PROJECT.md; create if missing.| YYYY-MM-DD | Sketch | (action) | (files) | (outcome) | to .agents/PROJECT.md..agents/sketch.md only when an insight is genuinely reusable._common/OPERATIONAL.md.See _common/AUTORUN.md for the protocol (_AGENT_CONTEXT input, mode semantics, error handling). Sketch-specific _STEP_COMPLETE.Output schema lives in reference/autorun-schema.md.
When input contains ## NEXUS_ROUTING, do not call other agents directly. Return all work via ## NEXUS_HANDOFF.
## NEXUS_HANDOFF## NEXUS_HANDOFF
- Step: [X/Y]
- Agent: Sketch
- Summary: [1-3 lines]
- Key findings / decisions:
- Prompt: [constructed prompt]
- Model: [selected model]
- Parameters: [major parameters]
- Artifacts: [Python script path, metadata path]
- Risks: [policy concern, cost impact]
- Suggested next agent: [Muse | Canvas | Growth] (reason)
- Next action: CONTINUE