[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CPS9A6DDQESAFPWF55QH3C":3,"skill-same:01M6CPS9A6DDQESAFPWF55QH3C":80},{"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":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":33,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6CPS9A6DDQESAFPWF55QH3C","openai-imagegen",null,"Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit\u002Finpaint\u002Fmask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts\u002Fimage_gen.py`) and require `OPENAI_API_KEY` for live calls. (Imported from openai upstream skills)","khalidsaidi\u002Fskillrunner","khalidsaidi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F4368721?v=4","01M6CPK1G2TKAWA8BSNSANBNWJ","Khalid Saidi","01M6CPK1M7RGKV85142PWBEJ54",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-imagegen\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],"openai imagegen","openai","imagegen","api","imported","upstream","main","Art","images","example","use","knowledge","image","generate image","batch","product","run","generate","github","cli","2026-11-10 10:12:30",false,"---\nname: openai-imagegen\ndescription: \"Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit\u002Finpaint\u002Fmask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts\u002Fimage_gen.py`) and require `OPENAI_API_KEY` for live calls. (Imported from openai upstream skills)\"\nversion: \"1.0.0\"\ntags: [\"openai\", \"upstream\", \"imported\"]\nkind: knowledge\nrisk: low\navailability: advanced\ndocs:\n  source: \"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fimagegen\"\n---\n\n# openai-imagegen\n\nImported upstream skill.\n\n- Provider source: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fimagegen\n- Original skill name: imagegen\n\n## Upstream Content\n\n# Image Generation Skill\n\nGenerates or edits images for the current project (e.g., website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, infographics). Defaults to `gpt-image-1.5` and the OpenAI Image API, and prefers the bundled CLI for deterministic, reproducible runs.\n\n## When to use\n- Generate a new image (concept art, product shot, cover, website hero)\n- Edit an existing image (inpainting, masked edits, lighting or weather transformations, background replacement, object removal, compositing, transparent background)\n- Batch runs (many prompts, or many variants across prompts)\n\n## Decision tree (generate vs edit vs batch)\n- If the user provides an input image (or says “edit\u002Fretouch\u002Finpaint\u002Fmask\u002Ftranslate\u002Flocalize\u002Fchange only X”) → **edit**\n- Else if the user needs many different prompts\u002Fassets → **generate-batch**\n- Else → **generate**\n\n## Workflow\n1. Decide intent: generate vs edit vs batch (see decision tree above).\n2. Collect inputs up front: prompt(s), exact text (verbatim), constraints\u002Favoid list, and any input image(s)\u002Fmask(s). For multi-image edits, label each input by index and role; for edits, list invariants explicitly.\n3. If batch: write a temporary JSONL under tmp\u002F (one job per line), run once, then delete the JSONL.\n4. Augment prompt into a short labeled spec (structure + constraints) without inventing new creative requirements.\n5. Run the bundled CLI (`scripts\u002Fimage_gen.py`) with sensible defaults (see references\u002Fcli.md).\n6. For complex edits\u002Fgenerations, inspect outputs (open\u002Fview images) and validate: subject, style, composition, text accuracy, and invariants\u002Favoid items.\n7. Iterate: make a single targeted change (prompt or mask), re-run, re-check.\n8. Save\u002Freturn final outputs and note the final prompt + flags used.\n\n## Temp and output conventions\n- Use `tmp\u002Fimagegen\u002F` for intermediate files (for example JSONL batches); delete when done.\n- Write final artifacts under `output\u002Fimagegen\u002F` when working in this repo.\n- Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.\n\n## Dependencies (install if missing)\nPrefer `uv` for dependency management.\n\nPython packages:\n```\nuv pip install openai pillow\n```\nIf `uv` is unavailable:\n```\npython3 -m pip install openai pillow\n```\n\n## Environment\n- `OPENAI_API_KEY` must be set for live API calls.\n\nIf the key is missing, give the user these steps:\n1. Create an API key in the OpenAI platform UI: https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n2. Set `OPENAI_API_KEY` as an environment variable in their system.\n3. Offer to guide them through setting the environment variable for their OS\u002Fshell if needed.\n- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.\n\nIf installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.\n\n## Defaults & rules\n- Use `gpt-image-1.5` unless the user explicitly asks for `gpt-image-1-mini` or explicitly prefers a cheaper\u002Ffaster model.\n- Assume the user wants a new image unless they explicitly ask for an edit.\n- Require `OPENAI_API_KEY` before any live API call.\n- Use the OpenAI Python SDK (`openai` package) for all API calls; do not use raw HTTP.\n- If the user requests edits, use `client.images.edit(...)` and include input images (and mask if provided).\n- Prefer the bundled CLI (`scripts\u002Fimage_gen.py`) over writing new one-off scripts.\n- Never modify `scripts\u002Fimage_gen.py`. If something is missing, ask the user before doing anything else.\n- If the result isn’t clearly relevant or doesn’t satisfy constraints, iterate with small targeted prompt changes; only ask a question if a missing detail blocks success.\n\n## Prompt augmentation\nReformat user prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new requirements.\n\n## Use-case taxonomy (exact slugs)\nClassify each request into one of these buckets and keep the slug consistent across prompts and references.\n\nGenerate:\n- photorealistic-natural — candid\u002Feditorial lifestyle scenes with real texture and natural lighting.\n- product-mockup — product\u002Fpackaging shots, catalog imagery, merch concepts.\n- ui-mockup — app\u002Fweb interface mockups that look shippable.\n- infographic-diagram — diagrams\u002Finfographics with structured layout and text.\n- logo-brand — logo\u002Fmark exploration, vector-friendly.\n- illustration-story — comics, children’s book art, narrative scenes.\n- stylized-concept — style-driven concept art, 3D\u002Fstylized renders.\n- historical-scene — period-accurate\u002Fworld-knowledge scenes.\n\nEdit:\n- text-localization — translate\u002Freplace in-image text, preserve layout.\n- identity-preserve — try-on, person-in-scene; lock face\u002Fbody\u002Fpose.\n- precise-object-edit — remove\u002Freplace a specific element (incl. interior swaps).\n- lighting-weather — time-of-day\u002Fseason\u002Fatmosphere changes only.\n- background-extraction — transparent background \u002F clean cutout.\n- style-transfer — apply reference style while changing subject\u002Fscene.\n- compositing — multi-image insert\u002Fmerge with matched lighting\u002Fperspective.\n- sketch-to-render — drawing\u002Fline art to photoreal render.\n\nQuick clarification (augmentation vs invention):\n- If the user says “a hero image for a landing page”, you may add *layout\u002Fcomposition constraints* that are implied by that use (e.g., “generous negative space on the right for headline text”).\n- Do not introduce new creative elements the user didn’t ask for (e.g., adding a mascot, changing the subject, inventing brand names\u002Flogos).\n\nTemplate (include only relevant lines):\n```\nUse case: \u003Ctaxonomy slug>\nAsset type: \u003Cwhere the asset will be used>\nPrimary request: \u003Cuser's main prompt>\nScene\u002Fbackground: \u003Cenvironment>\nSubject: \u003Cmain subject>\nStyle\u002Fmedium: \u003Cphoto\u002Fillustration\u002F3D\u002Fetc>\nComposition\u002Fframing: \u003Cwide\u002Fclose\u002Ftop-down; placement>\nLighting\u002Fmood: \u003Clighting + mood>\nColor palette: \u003Cpalette notes>\nMaterials\u002Ftextures: \u003Csurface details>\nQuality: \u003Clow\u002Fmedium\u002Fhigh\u002Fauto>\nInput fidelity (edits): \u003Clow\u002Fhigh>\nText (verbatim): \"\u003Cexact text>\"\nConstraints: \u003Cmust keep\u002Fmust avoid>\nAvoid: \u003Cnegative constraints>\n```\n\nAugmentation rules:\n- Keep it short; add only details the user already implied or provided elsewhere.\n- Always classify the request into a taxonomy slug above and tailor constraints\u002Fcomposition\u002Fquality to that bucket. Use the slug to find the matching example in `references\u002Fsample-prompts.md`.\n- If the user gives a broad request (e.g., \"Generate images for this website\"), use judgment to propose tasteful, context-appropriate assets and map each to a taxonomy slug.\n- For edits, explicitly list invariants (\"change only X; keep Y unchanged\").\n- If any critical detail is missing and blocks success, ask a question; otherwise proceed.\n\n## Examples\n\n### Generation example (hero image)\n```\nUse case: stylized-concept\nAsset type: landing page hero\nPrimary request: a minimal hero image of a ceramic coffee mug\nStyle\u002Fmedium: clean product photography\nComposition\u002Fframing: centered product, generous negative space on the right\nLighting\u002Fmood: soft studio lighting\nConstraints: no logos, no text, no watermark\n```\n\n### Edit example (invariants)\n```\nUse case: precise-object-edit\nAsset type: product photo background replacement\nPrimary request: replace the background with a warm sunset gradient\nConstraints: change only the background; keep the product and its edges unchanged; no text; no watermark\n```\n\n## Prompting best practices (short list)\n- Structure prompt as scene -> subject -> details -> constraints.\n- Include intended use (ad, UI mock, infographic) to set the mode and polish level.\n- Use camera\u002Fcomposition language for photorealism.\n- Quote exact text and specify typography + placement.\n- For tricky words, spell them letter-by-letter and require verbatim rendering.\n- For multi-image inputs, reference images by index and describe how to combine them.\n- For edits, repeat invariants every iteration to reduce drift.\n- Iterate with single-change follow-ups.\n- For latency-sensitive runs, start with quality=low; use quality=high for text-heavy or detail-critical outputs.\n- For strict edits (identity\u002Flayout lock), consider input_fidelity=high.\n- If results feel “tacky”, add a brief “Avoid:” line (stock-photo vibe; cheesy lens flare; oversaturated neon; harsh bloom; oversharpening; clutter) and specify restraint (“editorial”, “premium”, “subtle”).\n\nMore principles: `references\u002Fprompting.md`. Copy\u002Fpaste specs: `references\u002Fsample-prompts.md`.\n\n## Guidance by asset type\nAsset-type templates (website assets, game assets, wireframes, logo) are consolidated in `references\u002Fsample-prompts.md`.\n\n## CLI + environment notes\n- CLI commands + examples: `references\u002Fcli.md`\n- API parameter quick reference: `references\u002Fimage-api.md`\n- If network approvals \u002F sandbox settings are getting in the way: `references\u002Fcodex-network.md`\n\n## Reference map\n- **`references\u002Fcli.md`**: how to *run* image generation\u002Fedits\u002Fbatches via `scripts\u002Fimage_gen.py` (commands, flags, recipes).\n- **`references\u002Fimage-api.md`**: what knobs exist at the API level (parameters, sizes, quality, background, edit-only fields).\n- **`references\u002Fprompting.md`**: prompting principles (structure, constraints\u002Finvariants, iteration patterns).\n- **`references\u002Fsample-prompts.md`**: copy\u002Fpaste prompt recipes (generate + edit workflows; examples only).\n- **`references\u002Fcodex-network.md`**: environment\u002Fsandbox\u002Fnetwork-approval troubleshooting.\n","# openai-imagegen\n\nImported upstream skill.\n\n- Provider source: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fimagegen\n- Original skill name: imagegen\n\n## Upstream Content\n\n# Image Generation Skill\n\nGenerates or edits images for the current project (e.g., website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, infographics). Defaults to `gpt-image-1.5` and the OpenAI Image API, and prefers the bundled CLI for deterministic, reproducible runs.\n\n## When to use\n- Generate a new image (concept art, product shot, cover, website hero)\n- Edit an existing image (inpainting, masked edits, lighting or weather transformations, background replacement, object removal, compositing, transparent background)\n- Batch runs (many prompts, or many variants across prompts)\n\n## Decision tree (generate vs edit vs batch)\n- If the user provides an input image (or says “edit\u002Fretouch\u002Finpaint\u002Fmask\u002Ftranslate\u002Flocalize\u002Fchange only X”) → **edit**\n- Else if the user needs many different prompts\u002Fassets → **generate-batch**\n- Else → **generate**\n\n## Workflow\n1. Decide intent: generate vs edit vs batch (see decision tree above).\n2. Collect inputs up front: prompt(s), exact text (verbatim), constraints\u002Favoid list, and any input image(s)\u002Fmask(s). For multi-image edits, label each input by index and role; for edits, list invariants explicitly.\n3. If batch: write a temporary JSONL under tmp\u002F (one job per line), run once, then delete the JSONL.\n4. Augment prompt into a short labeled spec (structure + constraints) without inventing new creative requirements.\n5. Run the bundled CLI (`scripts\u002Fimage_gen.py`) with sensible defaults (see references\u002Fcli.md).\n6. For complex edits\u002Fgenerations, inspect outputs (open\u002Fview images) and validate: subject, style, composition, text accuracy, and invariants\u002Favoid items.\n7. Iterate: make a single targeted change (prompt or mask), re-run, re-check.\n8. Save\u002Freturn final outputs and note the final prompt + flags used.\n\n## Temp and output conventions\n- Use `tmp\u002Fimagegen\u002F` for intermediate files (for example JSONL batches); delete when done.\n- Write final artifacts under `output\u002Fimagegen\u002F` when working in this repo.\n- Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.\n\n## Dependencies (install if missing)\nPrefer `uv` for dependency management.\n\nPython packages:\n```\nuv pip install openai pillow\n```\nIf `uv` is unavailable:\n```\npython3 -m pip install openai pillow\n```\n\n## Environment\n- `OPENAI_API_KEY` must be set for live API calls.\n\nIf the key is missing, give the user these steps:\n1. Create an API key in the OpenAI platform UI: https:\u002F\u002Fplatform.openai.com\u002Fapi-keys\n2. Set `OPENAI_API_KEY` as an environment variable in their system.\n3. Offer to guide them through setting the environment variable for their OS\u002Fshell if needed.\n- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.\n\nIf installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.\n\n## Defaults & rules\n- Use `gpt-image-1.5` unless the user explicitly asks for `gpt-image-1-mini` or explicitly prefers a cheaper\u002Ffaster model.\n- Assume the user wants a new image unless they explicitly ask for an edit.\n- Require `OPENAI_API_KEY` before any live API call.\n- Use the OpenAI Python SDK (`openai` package) for all API calls; do not use raw HTTP.\n- If the user requests edits, use `client.images.edit(...)` and include input images (and mask if provided).\n- Prefer the bundled CLI (`scripts\u002Fimage_gen.py`) over writing new one-off scripts.\n- Never modify `scripts\u002Fimage_gen.py`. If something is missing, ask the user before doing anything else.\n- If the result isn’t clearly relevant or doesn’t satisfy constraints, iterate with small targeted prompt changes; only ask a question if a missing detail blocks success.\n\n## Prompt augmentation\nReformat user prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new requirements.\n\n## Use-case taxonomy (exact slugs)\nClassify each request into one of these buckets and keep the slug consistent across prompts and references.\n\nGenerate:\n- photorealistic-natural — candid\u002Feditorial lifestyle scenes with real texture and natural lighting.\n- product-mockup — product\u002Fpackaging shots, catalog imagery, merch concepts.\n- ui-mockup — app\u002Fweb interface mockups that look shippable.\n- infographic-diagram — diagrams\u002Finfographics with structured layout and text.\n- logo-brand — logo\u002Fmark exploration, vector-friendly.\n- illustration-story — comics, children’s book art, narrative scenes.\n- stylized-concept — style-driven concept art, 3D\u002Fstylized renders.\n- historical-scene — period-accurate\u002Fworld-knowledge scenes.\n\nEdit:\n- text-localization — translate\u002Freplace in-image text, preserve layout.\n- identity-preserve — try-on, person-in-scene; lock face\u002Fbody\u002Fpose.\n- precise-object-edit — remove\u002Freplace a specific element (incl. interior swaps).\n- lighting-weather — time-of-day\u002Fseason\u002Fatmosphere changes only.\n- background-extraction — transparent background \u002F clean cutout.\n- style-transfer — apply reference style while changing subject\u002Fscene.\n- compositing — multi-image insert\u002Fmerge with matched lighting\u002Fperspective.\n- sketch-to-render — drawing\u002Fline art to photoreal render.\n\nQuick clarification (augmentation vs invention):\n- If the user says “a hero image for a landing page”, you may add *layout\u002Fcomposition constraints* that are implied by that use (e.g., “generous negative space on the right for headline text”).\n- Do not introduce new creative elements the user didn’t ask for (e.g., adding a mascot, changing the subject, inventing brand names\u002Flogos).\n\nTemplate (include only relevant lines):\n```\nUse case: \u003Ctaxonomy slug>\nAsset type: \u003Cwhere the asset will be used>\nPrimary request: \u003Cuser's main prompt>\nScene\u002Fbackground: \u003Cenvironment>\nSubject: \u003Cmain subject>\nStyle\u002Fmedium: \u003Cphoto\u002Fillustration\u002F3D\u002Fetc>\nComposition\u002Fframing: \u003Cwide\u002Fclose\u002Ftop-down; placement>\nLighting\u002Fmood: \u003Clighting + mood>\nColor palette: \u003Cpalette notes>\nMaterials\u002Ftextures: \u003Csurface details>\nQuality: \u003Clow\u002Fmedium\u002Fhigh\u002Fauto>\nInput fidelity (edits): \u003Clow\u002Fhigh>\nText (verbatim): \"\u003Cexact text>\"\nConstraints: \u003Cmust keep\u002Fmust avoid>\nAvoid: \u003Cnegative constraints>\n```\n\nAugmentation rules:\n- Keep it short; add only details the user already implied or provided elsewhere.\n- Always classify the request into a taxonomy slug above and tailor constraints\u002Fcomposition\u002Fquality to that bucket. Use the slug to find the matching example in `references\u002Fsample-prompts.md`.\n- If the user gives a broad request (e.g., \"Generate images for this website\"), use judgment to propose tasteful, context-appropriate assets and map each to a taxonomy slug.\n- For edits, explicitly list invariants (\"change only X; keep Y unchanged\").\n- If any critical detail is missing and blocks success, ask a question; otherwise proceed.\n\n## Examples\n\n### Generation example (hero image)\n```\nUse case: stylized-concept\nAsset type: landing page hero\nPrimary request: a minimal hero image of a ceramic coffee mug\nStyle\u002Fmedium: clean product photography\nComposition\u002Fframing: centered product, generous negative space on the right\nLighting\u002Fmood: soft studio lighting\nConstraints: no logos, no text, no watermark\n```\n\n### Edit example (invariants)\n```\nUse case: precise-object-edit\nAsset type: product photo background replacement\nPrimary request: replace the background with a warm sunset gradient\nConstraints: change only the background; keep the product and its edges unchanged; no text; no watermark\n```\n\n## Prompting best practices (short list)\n- Structure prompt as scene -> subject -> details -> constraints.\n- Include intended use (ad, UI mock, infographic) to set the mode and polish level.\n- Use camera\u002Fcomposition language for photorealism.\n- Quote exact text and specify typography + placement.\n- For tricky words, spell them letter-by-letter and require verbatim rendering.\n- For multi-image inputs, reference images by index and describe how to combine them.\n- For edits, repeat invariants every iteration to reduce drift.\n- Iterate with single-change follow-ups.\n- For latency-sensitive runs, start with quality=low; use quality=high for text-heavy or detail-critical outputs.\n- For strict edits (identity\u002Flayout lock), consider input_fidelity=high.\n- If results feel “tacky”, add a brief “Avoid:” line (stock-photo vibe; cheesy lens flare; oversaturated neon; harsh bloom; oversharpening; clutter) and specify restraint (“editorial”, “premium”, “subtle”).\n\nMore principles: `references\u002Fprompting.md`. Copy\u002Fpaste specs: `references\u002Fsample-prompts.md`.\n\n## Guidance by asset type\nAsset-type templates (website assets, game assets, wireframes, logo) are consolidated in `references\u002Fsample-prompts.md`.\n\n## CLI + environment notes\n- CLI commands + examples: `references\u002Fcli.md`\n- API parameter quick reference: `references\u002Fimage-api.md`\n- If network approvals \u002F sandbox settings are getting in the way: `references\u002Fcodex-network.md`\n\n## Reference map\n- **`references\u002Fcli.md`**: how to *run* image generation\u002Fedits\u002Fbatches via `scripts\u002Fimage_gen.py` (commands, flags, recipes).\n- **`references\u002Fimage-api.md`**: what knobs exist at the API level (parameters, sizes, quality, background, edit-only fields).\n- **`references\u002Fprompting.md`**: prompting principles (structure, constraints\u002Finvariants, iteration patterns).\n- **`references\u002Fsample-prompts.md`**: copy\u002Fpaste prompt recipes (generate + edit workflows; examples only).\n- **`references\u002Fcodex-network.md`**: environment\u002Fsandbox\u002Fnetwork-approval troubleshooting.\n",{"docs":52,"kind":38,"name":5,"risk":54,"tags":55,"version":56,"description":7,"availability":57},{"source":53},"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fimagegen","low",[28,32,31],"1.0.0","advanced","c4da92076cba308457cd72bd1ab06c087e2cdf96","registry\u002Fskills\u002Fopenai-imagegen",0,"https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi","https:\u002F\u002Fkhalidsaidi.com","Software engineer. Maintainer of SourceMint (SRCMNT), an open-source fixed-supply ERC-20 and Foundry tooling on Base.","2026-11-07 02:40:24",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add khalidsaidi\u002Fskillrunner --skill openai-imagegen",{"tool":72,"command":73},"bunx","bunx skills add khalidsaidi\u002Fskillrunner --skill openai-imagegen",{"tool":75,"command":76},"pnpm","pnpm dlx skills add khalidsaidi\u002Fskillrunner --skill openai-imagegen","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-imagegen\u002FSKILL.md",true,[],[81,97,106,125,134,150],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":47},"01M6CPY1N6W58XKWMN4356E8GF","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-yeet\u002Fupstream\u002FSKILL.md",[],[83,88,46,37,89,90,91,92,93,94,95,96,45],"only","user","gh","open","flow","push","pull","pull request","commit",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":47},"01M6CPXZ5RN2ZB5A4Y65RE5Y5J","openai-yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`). (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-yeet\u002FSKILL.md",[],[83,28,104,93,94,95,96,45,46,105,31,32,33,88,37,89,90,38,91,92],"openai yeet","https",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":115,"publishedAt":47},"01M6CPXWGRW59XN1V109G172CV","vercel-deploy","Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\".","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-vercel-deploy\u002Fupstream\u002FSKILL.md",[112],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",[116,117,118,93,37,119,120,121,122,89,123,124],"vercel","vercel deploy","deploy","actions","applications","me","preview deployment","deployment","create",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":47},"01M6CPXSZSZRPQ5TY73K5QWN1B","openai-vercel-deploy","Deploy applications and websites to Vercel. Use when the user requests deployment actions like \"deploy my app\", \"deploy and give me the link\", \"push this live\", or \"create a preview deployment\". (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-vercel-deploy\u002FSKILL.md",[131],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},[118,28,117,116,133,119,31,32,121,33,122,37,89,38,120,105,93,124,123,45],"openai vercel deploy",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"publishedAt":47},"01M6CPXQ4SRTZZZ0VTD7WZP0RN","transcribe","Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio\u002Fvideo, extract text from recordings, or label speakers in interviews or meetings.","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-transcribe\u002Fupstream\u002FSKILL.md",[140],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[136,89,142,143,144,145,146,147,148,37,149],"text","Audio","Video","speech","extract","files","recordings","meetings",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":157,"publishedAt":47},"01M6CPXMG2SCTAA04EB3PDRRXN","openai-transcribe","Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio\u002Fvideo, extract text from recordings, or label speakers in interviews or meetings. (Imported from openai upstream skills)","https:\u002F\u002Fgithub.com\u002Fkhalidsaidi\u002Fskillrunner\u002Fblob\u002Fmain\u002Fregistry\u002Fskills\u002Fopenai-transcribe\u002FSKILL.md",[156],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[28,158,136,45,37,31,32,33,147,148,149,146,89,142,38,143,144,105,145],"openai transcribe"]