[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PY3BYRE48JJSAMYPPHGF8W":3,"skill-same:01M6PY3BYRE48JJSAMYPPHGF8W":86},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6PY3BYRE48JJSAMYPPHGF8W","fal-image-gen",null,"Call fal.ai model APIs for image generation (text-to-image and image-to-image). Use when a user asks to integrate fal, construct requests, run jobs, handle auth, or return image URLs from fal model APIs.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"fal image gen","image gen","fal","image","use","for","requests","generation","call","jobs","integrate","user","model","text","Text-to-Image","Image Generation","run","auth","2026-11-10 10:12:30",false,"---\nname: fal-image-gen\ndescription: \"Call fal.ai model APIs for image generation (text-to-image and image-to-image). Use when a user asks to integrate fal, construct requests, run jobs, handle auth, or return image URLs from fal model APIs.\"\n---\n\n# Fal Image Gen\n\n## Overview\nUse this skill to implement text-to-image or image-to-image calls against fal model APIs. Prioritize correctness by checking the current docs for the selected model’s required inputs\u002Foutputs and authentication requirements.\n\n## Quick Start\n1. Identify the target model ID from the fal model API docs.\n2. Collect inputs from the user.\n- Text-to-image: `prompt`, optional `negative_prompt`, size\u002Faspect, steps, seed, safety options.\n- Image-to-image: source image URL, strength\u002Fdenoise, plus prompt\u002Foptions above.\n3. Pick the calling method.\n- If the user prefers SDKs: provide Python and\u002For JavaScript examples.\n- If the user prefers REST: provide a curl\u002FHTTP example.\n4. Execute the request and return image URL(s) from the response.\n\n## Workflow: Text-to-Image\n1. Resolve the model ID and schema.\n- Open the fal model API docs and confirm the exact input fields and output format.\n2. Validate inputs.\n- Ensure prompt is non-empty and size\u002Faspect settings are supported by the model.\n3. Build the request.\n- SDK: call the SDK’s `run`\u002F`submit` method with an `input` object.\n- REST: call the model endpoint with a JSON body that matches the schema.\n4. Execute and parse output.\n- Extract image URL(s) from the response fields defined by the model.\n5. Return URLs.\n- Provide a clean list of URLs and note any metadata the user asked for (seed, size, etc.).\n\n## Workflow: Image-to-Image\n1. Resolve the model ID and schema.\n2. Validate inputs.\n- Ensure the source image is reachable by URL (or converted to the required format).\n- Confirm any strength\u002Fdenoise range constraints from docs.\n3. Build the request.\n- Include source image + prompt + other options as required by the model.\n4. Execute and parse output.\n- Extract image URL(s) from the response fields defined by the model.\n5. Return URLs.\n\n## SDK vs REST Guidance\n- Prefer SDKs for simpler auth and retries.\n- Prefer REST when the user needs raw HTTP examples, or when running in environments without SDK support.\n- Never hardcode API keys. Follow the docs for the required environment variable or header name.\n\n## Minimal Examples (Fill From Docs)\nUse these as templates only. Replace placeholders after checking the docs.\n\n### Python (SDK)\n```python\n# Pseudocode: replace with the exact fal SDK import + call pattern from docs\nimport os\n# from fal import client  # or the current SDK import\n\nMODEL_ID = \"\u003Cmodel-id-from-docs>\"\ninput_data = {\n    \"prompt\": \"a cinematic photo of a red fox\",\n    # \"image_url\": \"https:\u002F\u002F...\"  # for image-to-image\n    # \"negative_prompt\": \"...\",\n    # \"width\": 1024,\n    # \"height\": 1024,\n}\n\n# result = client.run(MODEL_ID, input=input_data)\n# urls = extract_urls(result)\n```\n\n### JavaScript (SDK)\n```javascript\n\u002F\u002F Pseudocode: replace with the exact fal SDK import + call pattern from docs\n\u002F\u002F import { client } from \"@fal-ai\u002Fclient\";\n\nconst MODEL_ID = \"\u003Cmodel-id-from-docs>\";\nconst input = {\n  prompt: \"a cinematic photo of a red fox\",\n  \u002F\u002F image_url: \"https:\u002F\u002F...\" \u002F\u002F for image-to-image\n};\n\n\u002F\u002F const result = await client.run(MODEL_ID, { input });\n\u002F\u002F const urls = extractUrls(result);\n```\n\n### REST (curl)\n```bash\n# Pseudocode: replace endpoint, headers, and payload schema from docs\ncurl -X POST \"https:\u002F\u002F\u003Cfal-api-base>\u002F\u003Cmodel-endpoint>\" \\\n  -H \"Authorization: Bearer \u003CAPI_KEY>\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"prompt\": \"a cinematic photo of a red fox\"\n  }'\n```\n\n## Resources\n- `references\u002Ffal-model-api-checklist.md`: Checklist for gathering inputs and validating responses.\n- `references\u002Ffal-model-examples.md`: Example templates for text-to-image, image-to-image, and REST usage.\n","# Fal Image Gen\n\n## Overview\nUse this skill to implement text-to-image or image-to-image calls against fal model APIs. Prioritize correctness by checking the current docs for the selected model’s required inputs\u002Foutputs and authentication requirements.\n\n## Quick Start\n1. Identify the target model ID from the fal model API docs.\n2. Collect inputs from the user.\n- Text-to-image: `prompt`, optional `negative_prompt`, size\u002Faspect, steps, seed, safety options.\n- Image-to-image: source image URL, strength\u002Fdenoise, plus prompt\u002Foptions above.\n3. Pick the calling method.\n- If the user prefers SDKs: provide Python and\u002For JavaScript examples.\n- If the user prefers REST: provide a curl\u002FHTTP example.\n4. Execute the request and return image URL(s) from the response.\n\n## Workflow: Text-to-Image\n1. Resolve the model ID and schema.\n- Open the fal model API docs and confirm the exact input fields and output format.\n2. Validate inputs.\n- Ensure prompt is non-empty and size\u002Faspect settings are supported by the model.\n3. Build the request.\n- SDK: call the SDK’s `run`\u002F`submit` method with an `input` object.\n- REST: call the model endpoint with a JSON body that matches the schema.\n4. Execute and parse output.\n- Extract image URL(s) from the response fields defined by the model.\n5. Return URLs.\n- Provide a clean list of URLs and note any metadata the user asked for (seed, size, etc.).\n\n## Workflow: Image-to-Image\n1. Resolve the model ID and schema.\n2. Validate inputs.\n- Ensure the source image is reachable by URL (or converted to the required format).\n- Confirm any strength\u002Fdenoise range constraints from docs.\n3. Build the request.\n- Include source image + prompt + other options as required by the model.\n4. Execute and parse output.\n- Extract image URL(s) from the response fields defined by the model.\n5. Return URLs.\n\n## SDK vs REST Guidance\n- Prefer SDKs for simpler auth and retries.\n- Prefer REST when the user needs raw HTTP examples, or when running in environments without SDK support.\n- Never hardcode API keys. Follow the docs for the required environment variable or header name.\n\n## Minimal Examples (Fill From Docs)\nUse these as templates only. Replace placeholders after checking the docs.\n\n### Python (SDK)\n```python\n# Pseudocode: replace with the exact fal SDK import + call pattern from docs\nimport os\n# from fal import client  # or the current SDK import\n\nMODEL_ID = \"\u003Cmodel-id-from-docs>\"\ninput_data = {\n    \"prompt\": \"a cinematic photo of a red fox\",\n    # \"image_url\": \"https:\u002F\u002F...\"  # for image-to-image\n    # \"negative_prompt\": \"...\",\n    # \"width\": 1024,\n    # \"height\": 1024,\n}\n\n# result = client.run(MODEL_ID, input=input_data)\n# urls = extract_urls(result)\n```\n\n### JavaScript (SDK)\n```javascript\n\u002F\u002F Pseudocode: replace with the exact fal SDK import + call pattern from docs\n\u002F\u002F import { client } from \"@fal-ai\u002Fclient\";\n\nconst MODEL_ID = \"\u003Cmodel-id-from-docs>\";\nconst input = {\n  prompt: \"a cinematic photo of a red fox\",\n  \u002F\u002F image_url: \"https:\u002F\u002F...\" \u002F\u002F for image-to-image\n};\n\n\u002F\u002F const result = await client.run(MODEL_ID, { input });\n\u002F\u002F const urls = extractUrls(result);\n```\n\n### REST (curl)\n```bash\n# Pseudocode: replace endpoint, headers, and payload schema from docs\ncurl -X POST \"https:\u002F\u002F\u003Cfal-api-base>\u002F\u003Cmodel-endpoint>\" \\\n  -H \"Authorization: Bearer \u003CAPI_KEY>\" \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"prompt\": \"a cinematic photo of a red fox\"\n  }'\n```\n\n## Resources\n- `references\u002Ffal-model-api-checklist.md`: Checklist for gathering inputs and validating responses.\n- `references\u002Ffal-model-examples.md`: Example templates for text-to-image, image-to-image, and REST usage.\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","安全与密码\u002Ffalimagegen",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill fal-image-gen",{"tool":60,"command":61},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill fal-image-gen",{"tool":63,"command":64},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill fal-image-gen","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002FSKILL.md",true,[68,74,78,82],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"_meta.json","REFERENCE","参考文档",277,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002F_meta.json",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"agents\u002Fopenai.yaml",297,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002Fagents\u002Fopenai.yaml",{"path":79,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Ffal-model-api-checklist.md",972,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002Freferences\u002Ffal-model-api-checklist.md",{"path":83,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Ffal-model-examples.md",2075,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F安全与密码\u002Ffalimagegen\u002Freferences\u002Ffal-model-examples.md",[87,109,143,154,164,193],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":41},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. 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Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":123,"taxonomyTypeDesc":124},"01JBSEEDCAT000000000000040","财务",[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,28],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":200,"publishedAt":41},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[199],{"categoryNo":171,"categoryName":172,"taxonomyType":123,"taxonomyTypeDesc":124},[174,175,177,201,202,203,204,205,206,185,207,208,209,210,211,212,213,214,142,192],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]