[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADMBYD0WX6MDBXHSN3NWQH":3,"skill-same:01M6ADMBYD0WX6MDBXHSN3NWQH":81},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6ADMBYD0WX6MDBXHSN3NWQH","generate-image",null,"Generate images using AI. Use when asked to generate, create, or make images, textures, icons, sprites, artwork, visual assets, or mockups. Supports OpenAI (gpt-image-2) and Google Gemini (Nano Banana). Requires an API key for the chosen provider.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41081,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fgenerate-image\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"generate","generate image","image","assets","when","sprites","for","nano","visual","banana","nano banana","provider","images","use","api","gpt","openai","icons","create","gemini","google","2026-11-10 10:12:30",false,"---\nname: generate-image\ndescription: >-\n  Generate images using AI. Use when asked to generate, create, or make images, textures,\n  icons, sprites, artwork, visual assets, or mockups. Supports OpenAI (gpt-image-2) and\n  Google Gemini (Nano Banana). Requires an API key for the chosen provider.\nargument-hint: \"[description of the image to generate]\"\nlicense: MIT\nmetadata:\n  version: \"2.1.0\"\n  providers: \"openai, gemini\"\n---\n\n# Generate Image\n\nYou are an image generation assistant. When invoked, follow the workflow below.\n\n## Workflow\n\n1. **Check for API keys** — check whether `SKILL_IMAGE_GEN_OPENAI_KEY` and\u002For `SKILL_IMAGE_GEN_GEMINI_KEY` are set in the environment.\n2. **If one key is set** — use that provider. No need to ask.\n3. **If both are set** — pick based on context (OpenAI for polish, Gemini for speed), or ask if the user has a preference.\n4. **If no keys are set** — run the Onboarding section.\n5. **Generate the image** using the appropriate API reference.\n6. **Tell the user** where the image was saved.\n\n## Onboarding\n\nOnly run this if no keys are set. Guide the user conversationally.\n\n1. Ask which provider they'd like to use:\n   - **OpenAI (gpt-image-2)** — High quality, excellent text rendering, paid per image\n   - **Google Gemini (Nano Banana)** — Fast, free tier available, great for iteration\n2. Direct them to get an API key:\n   - OpenAI → https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n   - Gemini → https:\u002F\u002Faistudio.google.com\u002Fapikey\n3. Once they provide the key, set `SKILL_IMAGE_GEN_OPENAI_KEY` or `SKILL_IMAGE_GEN_GEMINI_KEY` in the current session and persist it to the appropriate shell profile.\n4. Proceed to generate the image they originally asked for.\n\n## API Reference: OpenAI\n\n**Method:** `POST`\n**URL:** `https:\u002F\u002Fapi.openai.com\u002Fv1\u002Fimages\u002Fgenerations`\n\n**Headers:**\n- `Authorization: Bearer \u003CSKILL_IMAGE_GEN_OPENAI_KEY>`\n- `Content-Type: application\u002Fjson`\n\n**Body (JSON):**\n```json\n{\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"\u003Cuser prompt>\",\n  \"n\": 1,\n  \"size\": \"1024x1024\",\n  \"quality\": \"medium\"\n}\n```\n\n| Field | Default | Options |\n|---|---|---|\n| model | `gpt-image-2` | `gpt-image-2`, `gpt-image-1` |\n| size | `1024x1024` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` |\n| quality | `medium` | `low`, `medium`, `high` |\n\n**Response:** `data[0].b64_json` contains the base64-encoded image. Decode it and save to the output path. If `data[0].url` is present instead, download the image from that URL.\n\n## API Reference: Google Gemini (Nano Banana)\n\n**Method:** `POST`\n**URL:** `https:\u002F\u002Fgenerativelanguage.googleapis.com\u002Fv1beta\u002Fmodels\u002F\u003Cmodel>:generateContent`\n\n**Headers:**\n- `x-goog-api-key: \u003CSKILL_IMAGE_GEN_GEMINI_KEY>`\n- `Content-Type: application\u002Fjson`\n\n**Body (JSON):**\n```json\n{\n  \"contents\": [{\"parts\": [{\"text\": \"Generate an image: \u003Cuser prompt>\"}]}],\n  \"generationConfig\": {\"responseModalities\": [\"TEXT\", \"IMAGE\"]}\n}\n```\n\n| Field | Default | Options |\n|---|---|---|\n| model (in URL) | `gemini-2.0-flash-exp` | `gemini-2.0-flash-exp`, `gemini-2.5-flash-image` |\n\n**Response:** Find `candidates[0].content.parts[]` — look for a part with `inlineData.data` (base64 image) and `inlineData.mimeType`. Decode and save.\n\n**Error cases:** `error` key (API error), `promptFeedback.blockReason` (safety block), `finishReason: \"SAFETY\"` (filtered).\n\n## Agent Guidelines\n\n- Choose the output path intelligently — save to the project's relevant directory (e.g., `assets\u002F`, `images\u002F`, or the current directory).\n- For game textures, enrich prompts with \"seamless\", \"tileable\", \"game asset\".\n- For batch generation, make multiple API calls in parallel.\n- If the user asks to switch providers or what options are available, explain both and help them set up.\n- Always create the output directory before saving.\n- Ensure special characters in the user's prompt are properly escaped in the JSON body.\n","# Generate Image\n\nYou are an image generation assistant. When invoked, follow the workflow below.\n\n## Workflow\n\n1. **Check for API keys** — check whether `SKILL_IMAGE_GEN_OPENAI_KEY` and\u002For `SKILL_IMAGE_GEN_GEMINI_KEY` are set in the environment.\n2. **If one key is set** — use that provider. No need to ask.\n3. **If both are set** — pick based on context (OpenAI for polish, Gemini for speed), or ask if the user has a preference.\n4. **If no keys are set** — run the Onboarding section.\n5. **Generate the image** using the appropriate API reference.\n6. **Tell the user** where the image was saved.\n\n## Onboarding\n\nOnly run this if no keys are set. Guide the user conversationally.\n\n1. Ask which provider they'd like to use:\n   - **OpenAI (gpt-image-2)** — High quality, excellent text rendering, paid per image\n   - **Google Gemini (Nano Banana)** — Fast, free tier available, great for iteration\n2. Direct them to get an API key:\n   - OpenAI → https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n   - Gemini → https:\u002F\u002Faistudio.google.com\u002Fapikey\n3. Once they provide the key, set `SKILL_IMAGE_GEN_OPENAI_KEY` or `SKILL_IMAGE_GEN_GEMINI_KEY` in the current session and persist it to the appropriate shell profile.\n4. Proceed to generate the image they originally asked for.\n\n## API Reference: OpenAI\n\n**Method:** `POST`\n**URL:** `https:\u002F\u002Fapi.openai.com\u002Fv1\u002Fimages\u002Fgenerations`\n\n**Headers:**\n- `Authorization: Bearer \u003CSKILL_IMAGE_GEN_OPENAI_KEY>`\n- `Content-Type: application\u002Fjson`\n\n**Body (JSON):**\n```json\n{\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"\u003Cuser prompt>\",\n  \"n\": 1,\n  \"size\": \"1024x1024\",\n  \"quality\": \"medium\"\n}\n```\n\n| Field | Default | Options |\n|---|---|---|\n| model | `gpt-image-2` | `gpt-image-2`, `gpt-image-1` |\n| size | `1024x1024` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` |\n| quality | `medium` | `low`, `medium`, `high` |\n\n**Response:** `data[0].b64_json` contains the base64-encoded image. Decode it and save to the output path. If `data[0].url` is present instead, download the image from that URL.\n\n## API Reference: Google Gemini (Nano Banana)\n\n**Method:** `POST`\n**URL:** `https:\u002F\u002Fgenerativelanguage.googleapis.com\u002Fv1beta\u002Fmodels\u002F\u003Cmodel>:generateContent`\n\n**Headers:**\n- `x-goog-api-key: \u003CSKILL_IMAGE_GEN_GEMINI_KEY>`\n- `Content-Type: application\u002Fjson`\n\n**Body (JSON):**\n```json\n{\n  \"contents\": [{\"parts\": [{\"text\": \"Generate an image: \u003Cuser prompt>\"}]}],\n  \"generationConfig\": {\"responseModalities\": [\"TEXT\", \"IMAGE\"]}\n}\n```\n\n| Field | Default | Options |\n|---|---|---|\n| model (in URL) | `gemini-2.0-flash-exp` | `gemini-2.0-flash-exp`, `gemini-2.5-flash-image` |\n\n**Response:** Find `candidates[0].content.parts[]` — look for a part with `inlineData.data` (base64 image) and `inlineData.mimeType`. Decode and save.\n\n**Error cases:** `error` key (API error), `promptFeedback.blockReason` (safety block), `finishReason: \"SAFETY\"` (filtered).\n\n## Agent Guidelines\n\n- Choose the output path intelligently — save to the project's relevant directory (e.g., `assets\u002F`, `images\u002F`, or the current directory).\n- For game textures, enrich prompts with \"seamless\", \"tileable\", \"game asset\".\n- For batch generation, make multiple API calls in parallel.\n- If the user asks to switch providers or what options are available, explain both and help them set up.\n- Always create the output directory before saving.\n- Ensure special characters in the user's prompt are properly escaped in the JSON body.\n",{"name":5,"license":15,"metadata":53,"description":7,"argument-hint":56},{"version":54,"providers":55},"2.1.0","openai, gemini","[description of the image to generate]","main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fgenerate-image",5344,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-13 02:05:36",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add github\u002Fawesome-copilot --skill generate-image",{"tool":73,"command":74},"bunx","bunx skills add github\u002Fawesome-copilot --skill generate-image",{"tool":76,"command":77},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill generate-image","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fgenerate-image\u002FSKILL.md",true,[],[82,111,138,168,201,216],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":92,"publishedAt":48},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[88,91],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[93,94,95,96,97,31,98,99,100,101,102,103,104,105,106,107,40,108,109,110],"supabase","rls","supabase rls","supabase rls policies","project","review","change","read","write","security","isolation","tables","as","tests","prove","users","functions","with",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":118,"publishedAt":48},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[117],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[119,120,121,122,123,124,33,125,126,127,128,129,130,131,132,133,134,135,136,137],"distribution","skill distribution audit","audit","SET","deterministic","installer","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":142,"sourceHtmlUrl":143,"categories":144,"keywords":150,"publishedAt":48},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[145],{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[151,152,153,154,155,156,33,97,157,158,159,160,161,162,163,40,164,165,166,167],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":172,"sourceHtmlUrl":173,"categories":174,"keywords":179,"publishedAt":48},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[175,178],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},[180,181,41,182,183,184,185,186,187,188,189,190,191,192,193,31,194,195,196,197,198,33,199,200],"winmd api search","search","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":205,"licenseFileUrl":206,"sourceHtmlUrl":207,"categories":208,"keywords":212,"publishedAt":48},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[209,210,211],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[213,122,188,214,215],"resemble detect","https","identity",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":205,"licenseFileUrl":220,"sourceHtmlUrl":221,"categories":222,"keywords":227,"publishedAt":48},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[223,226],{"categoryNo":224,"categoryName":225,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[228,229,230,231,152,98,9,121,232,233,214,234,235,236,237,238,239,240,241,242,243,244,106,245,246,247,248],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]