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Inspect and diagnose minimal-zine posters, photo restyles, editorial images, and social-card series for subject preservation, factual and text integrity, composition, paper realism, print-process coherence, color signal, anti-AI craft, platform fit, and series continuity. Use when the user asks whether an image has高级感, feels AI-generated, matches Paper Signal or gc-minimal-zine quality, needs improvement, or should be regenerated. Returns evidence-based findings and a targeted correction prompt; regenerates only when the user also asks for a fix.

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Refresh HitKeep's checked-in bot/AI-agent, IP metadata, and spam-filter datasets through their canonical generators. Use for routine generated-data maintenance; do not use for hand-editing generated assets or changing detector policy.

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。

AI-driven image generation and editing tool for creating high-quality product images. Triggered when users ask to generate images, create product pictures, edit photos, text-to-image, image-to-image, replace backgrounds, transform styles, composite products into scenes, swap models, or create any type of AI-generated visual content. Even if the user does not explicitly say "AI image," this skill should be triggered whenever the request involves creating, modifying, or transforming 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。

Pick among candidate outputs (code, configs, plans) by running them on diverse inputs and clustering by behavioural fingerprint, rather than by textual aggregation or log-probability. Activates when an executor returns multiple plausible candidates that need disambiguation, when output-majority voting would be the default choice, or when reviewing generated code that has not yet been validated. The 2026 evidence (Semantic Voting, arxiv 2605.08680v1) is that any execution-based selector dominates output-majority voting by 19-52pp; sketch-generated inputs beat random fuzz by 11.3pp. Triggers: "pick the best candidate", "majority vote on code", "select from N samples", "validate the generated output", "behavioural verification".

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Generate MaaFramework Pipeline nodes and recognition snippets from screenshots or observed UI state. Use for OCR node generation, ROI sweep, choosing TemplateMatch/OCR/ColorMatch/CustomRecognition, preserving target-file schema style, and designing `next`/`[JumpBack]` links before merging generated nodes into pipeline JSON.

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Generate hierarchical AGENTS.md structures for codebases. Use when user asks to create AGENTS.md files, analyze codebase for AI agent documentation, set up AI-friendly project documentation, or generate context files for AI coding assistants. Triggers on "create AGENTS.md", "generate agents", "analyze codebase for AI", "AI documentation setup", "hierarchical 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。

Generate product pitch videos for the Erode project from screenshots. Use this skill whenever the user wants to create, update, or regenerate the product demo video, landing page animation, or pitch clip for erode.dev or the GitHub repo. Also trigger when the user mentions "product video", "demo video", "pitch video", "update the video", "regenerate video", or "landing page animation" in the context of Erode.

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Generate images and video from text prompts via the mediagen CLI, across Gemini, OpenAI and Kie AI. Use when asked to create, generate, render, or edit an image or a video — a logo, an illustration, a mockup, a photo, a banner, a clip — or to mark existing media as AI-generated. Also covers choosing a provider or model for a generation 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。

Generate jigsaw puzzles via generate_puzzle() or geom_puzzle_*() with parameter validation against inst/config.yml. Supports rectangular, hexagonal, concentric, voronoi, and snic puzzle types with configurable grid, size, seed, offset, and layout parameters. Use when creating puzzle SVG files for a specific type and configuration, testing generation with different parameters, generating sample output for documentation or demos, or creating ggplot2 puzzle visualizations.

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Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.

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 visual asset briefs, ad copy variants, AI image prompts, video scripts, and storyboards. Full creative production system with 5 specialized modes: product photos, product video, social graphics, talking heads, and ad creative. Make sure to use this skill whenever the user mentions any visual or creative marketing need — ad creative, image prompts, video scripts, thumbnails, banners, social graphics, product photography, storyboards, or marketing visuals of any kind. Even if they just say 'I need images for my campaign' or 'make something visual', this is the skill. Owns thumbnail and banner briefs plus the creative direction. Includes platform-specific dimensions, AI anti-slop techniques, and Remotion composition templates. For rendering the actual image file, see /image-gen; for AI-generated marketing/brand videos, see /higgsfield-generate; for stitching slides into an MP4, see /video-content.

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 Playwright tests. Use when user says "write tests", "generate tests", "add tests for", "test this component", "e2e test", "create test for", "test this page", or "test this feature".

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。

Generates machine_readable execution logic for Dutch law YAML files: writes the machine_readable sections, validates against the schema, and (when a shell is available) runs the BDD suites until the model is correct. Use this skill proactively when: editing or creating machine_readable sections in law YAML files, working with corpus regulation files, or when user mentions 'generate', 'machine_readable', or wants to make a law executable. Activate automatically when user discusses law YAML files that need executable logic.

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Regenerate and commit Class S site-served artifacts after registry changes, then sync documentation across the main repo and the adjacent wiki. Use this skill when: you've changed registry/nodes/, registry/named/, or named-skills.json and need to regenerate docs/graph/* so the live website reflects the update; CI Guard E (docs-cohesion.yml) is failing because Class S artifacts are out of date; someone says "sync the docs", "regenerate docs", "run gaia dev docs", "update the site graph", "commit the Class S artifacts", "website is stale", or "update the wiki"; or you're finishing a registry PR and need to ensure docs/graph/gaia.json, docs/graph/gaia.gexf, docs/graph/gaia.svg, and docs/graph/named/index.json are committed alongside the source change. Also covers updating README.md, docs/index.html, and mirroring changes to the adjacent wiki repo at ../gaia-wiki.

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Generate a conventional commit message by analyzing staged git changes. Use when the user wants to create, write, or generate a git commit message from their current staged diff. Trigger on phrases like "commit", "commit message", "commit msg", "寫 commit", "提交訊息", "generate commit", or when the user has just finished making code changes and wants to commit them. Also trigger when the user runs /commit-message.

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 publication-ready methods sections, figure legends, supplementary tables, and data availability statements from ENCODE analysis provenance. Implements the scientific documentation standards requiring complete metadata reporting. Use when the user needs to write methods, generate figure legends, create supplementary tables, draft data availability statements, compile tool citations, or auto-generate any publication text from their ENCODE analysis. Trigger on: methods section, figure legend, supplementary table, data availability, tool citations, publication writing, manuscript, write methods, methods draft, write up, write-up, paper writing, reproducible methods.

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。

Generates Stitch screens from authored prompt files using MCP tools. Reads prompt sections, sends each to Stitch for generation, and fetches resulting images and code. Use when user mentions "generate in stitch", "create stitch screens", "run prompts in stitch", "send prompts to stitch", "generate screens from prompts", "fetch stitch images", or has prompt-v*.md files ready for generation. Requires Stitch MCP server.

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, validate, or audit llms.txt files for AI search visibility. Crawls site structure, generates spec-compliant Markdown index for LLMs. Use when user says "llms.txt", "llm txt", "AI crawlers", "generate llms", "LLM file", "AI readability file".

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。

Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.

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。

AI media generation CLI tool using Google's Imagen 4, Veo 3.1, and Gemini TTS. Use when the user wants to (1) generate images from text prompts, (2) edit existing images with AI, (3) explain image contents, (4) generate videos from text or images, (5) create narration/voice audio with character settings. Triggers on requests like "generate an image of...", "create a video...", "make a voice that says...", "edit this image to...", "describe this image".

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 production-ready manga pages, chapter pages, covers, and panel sequences with strict recurring-character consistency. Use this when the user asks for a manga page, manga scene, chapter page, comic page, storyboard page, or character-consistent page generation. Before generating any page, inspect `img-memory`, create missing character sprites/reference sheets, make those images visible to image generation, then generate the final manga page.

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。

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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。

永続化ドキュメント（Steering Documents）を初期生成します。プロダクトのコードを自動解析し、ユーザーへのヒアリングを組み合わせて6種のドキュメントを生成します。併せて generate-working-docs のテンプレートをプロダクト用にカスタマイズします。「永続化ドキュメントを作成して」「steering docs を作って」などで呼び出されます。