[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-cloud-custom-golden-image-discovery":3,"mdc--q6y0yk-key":31,"related-repo-google-cloud-custom-golden-image-discovery":491,"related-org-google-cloud-custom-golden-image-discovery":585},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":21,"repoUrl":22,"updatedAt":23,"license":24,"forks":25,"topics":26,"repo":27,"sourceUrl":29,"mdContent":30},"custom-golden-image-discovery","discover golden base images for GKE nodes","Expert at discovering golden base images for GKE custom nodes using technical specs or context clues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"google-cloud","Google Cloud","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fgoogle-cloud.png","GoogleCloudPlatform",[13,17,20],{"name":14,"slug":15,"type":16},"Deployment","deployment","tag",{"name":18,"slug":19,"type":16},"Kubernetes","kubernetes",{"name":9,"slug":8,"type":16},161,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgke-mcp","2026-07-12T07:39:30.888879",null,78,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":24},[],"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgke-mcp\u002Ftree\u002FHEAD\u002Fskills\u002Fcustom-golden-image-discovery","---\nname: custom-golden-image-discovery\ndescription: Expert at discovering golden base images for GKE custom nodes using technical specs or context clues.\n---\n\n# GKE Golden Base Image Discovery Expert\n\nYou are an expert at helping users find the correct \"golden\" base image for creating custom GKE images. You can bridge the gap between a user's high-level description and the technical JSON requirements.\n\n## Information Gathering & Inference\n\nIf a user doesn't know their exact configuration, use the following **Context Clues** and **Sensible Defaults** to infer the values:\n\n| Field                      | Context Clues                                                                                                         | Default Value      |\n| :------------------------- | :-------------------------------------------------------------------------------------------------------------------- | :----------------- |\n| **GKE Version**            | (Required) Must be 1.34.1-gke.2909000 or later.                                                                       | N\u002FA                |\n| **Operating System**       | \"I like Google's OS\" -> COS; \"I need Ubuntu\u002Fstandard Linux\" -> Ubuntu.                                                | **COS**            |\n| **Architecture**           | \"Using ARM\u002FAmpere\" -> ARM64; \"Standard\u002FIntel\u002FAMD\" -> X86_64.                                                          | **X86_64**         |\n| **gVisor Enabled**         | \"Need a sandbox\" or \"gVisor\" mentioned -> true.                                                                       | **false**          |\n| **Has Accelerators**       | Mention of \"GPU\", \"accelerator\", \"Nvidia\", \"TPU\", or any specific hardware models (e.g., T4, A100, H100, L4) -> true. | **false**          |\n| **Enforce Signed Modules** | \"Hardened nodes\" or \"Signed modules\" mentioned -> true.                                                               | **false**          |\n| **Cgroup Mode**            | Almost all GKE 1.26+ clusters use V2. Only V1 if explicitly legacy.                                                   | **CGROUP_MODE_V2** |\n\n## Discovery Workflow\n\n1. **Extract Info**: Parse the user's request for the GKE Version and any context clues for the fields above. Be proactive: if a user mentions _any_ specialized hardware or security requirements, map them to the corresponding technical flags.\n2. **Determine Minor Version**: Extract the major\u002Fminor version (e.g., `1.34`).\n3. **Fetch Data**: `curl` the mapping: `https:\u002F\u002Fwww.gstatic.com\u002Fgke-image-maps\u002Fbase-images\u002Fnode-config-to-base-images-\u003CMINOR_VERSION>.json`\n4. **Filter Logic**:\n   - Match `version` exactly.\n   - Match `node_info` using the inferred or provided values:\n     - `image_family`: `COS_CONTAINERD` (COS) or `UBUNTU_CONTAINERD` (Ubuntu).\n     - Other fields match exactly.\n5. **Refine Search**: If no exact match is found with defaults, try toggling `cgroup_mode` to `CGROUP_MODE_V1` or `gvisor_enabled` to `false` and inform the user.\n\n## Example Output\n\n\"Based on your setup (GKE 1.34.1-gke.2909000, COS, and using the new H100 GPUs), I've inferred you need the **X86_64** image with **Accelerators** enabled. The golden base image is: `gke-1341-gke2909000-cos-125-19216-0-115-c-pre`\"\n",{"data":32,"body":33},{"name":4,"description":6},{"type":34,"children":35},"root",[36,45,51,58,78,274,280,459,465],{"type":37,"tag":38,"props":39,"children":41},"element","h1",{"id":40},"gke-golden-base-image-discovery-expert",[42],{"type":43,"value":44},"text","GKE Golden Base Image Discovery Expert",{"type":37,"tag":46,"props":47,"children":48},"p",{},[49],{"type":43,"value":50},"You are an expert at helping users find the correct \"golden\" base image for creating custom GKE images. 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Only V1 if explicitly legacy.",{"type":37,"tag":114,"props":267,"children":268},{"align":93},[269],{"type":37,"tag":64,"props":270,"children":271},{},[272],{"type":43,"value":273},"CGROUP_MODE_V2",{"type":37,"tag":52,"props":275,"children":277},{"id":276},"discovery-workflow",[278],{"type":43,"value":279},"Discovery Workflow",{"type":37,"tag":281,"props":282,"children":283},"ol",{},[284,303,322,346,419],{"type":37,"tag":285,"props":286,"children":287},"li",{},[288,293,295,301],{"type":37,"tag":64,"props":289,"children":290},{},[291],{"type":43,"value":292},"Extract Info",{"type":43,"value":294},": Parse the user's request for the GKE Version and any context clues for the fields above. Be proactive: if a user mentions ",{"type":37,"tag":296,"props":297,"children":298},"em",{},[299],{"type":43,"value":300},"any",{"type":43,"value":302}," specialized hardware or security requirements, map them to the corresponding technical flags.",{"type":37,"tag":285,"props":304,"children":305},{},[306,311,313,320],{"type":37,"tag":64,"props":307,"children":308},{},[309],{"type":43,"value":310},"Determine Minor Version",{"type":43,"value":312},": Extract the major\u002Fminor version (e.g., ",{"type":37,"tag":314,"props":315,"children":317},"code",{"className":316},[],[318],{"type":43,"value":319},"1.34",{"type":43,"value":321},").",{"type":37,"tag":285,"props":323,"children":324},{},[325,330,332,338,340],{"type":37,"tag":64,"props":326,"children":327},{},[328],{"type":43,"value":329},"Fetch Data",{"type":43,"value":331},": ",{"type":37,"tag":314,"props":333,"children":335},{"className":334},[],[336],{"type":43,"value":337},"curl",{"type":43,"value":339}," the mapping: ",{"type":37,"tag":314,"props":341,"children":343},{"className":342},[],[344],{"type":43,"value":345},"https:\u002F\u002Fwww.gstatic.com\u002Fgke-image-maps\u002Fbase-images\u002Fnode-config-to-base-images-\u003CMINOR_VERSION>.json",{"type":37,"tag":285,"props":347,"children":348},{},[349,354,356],{"type":37,"tag":64,"props":350,"children":351},{},[352],{"type":43,"value":353},"Filter Logic",{"type":43,"value":355},":\n",{"type":37,"tag":357,"props":358,"children":359},"ul",{},[360,373],{"type":37,"tag":285,"props":361,"children":362},{},[363,365,371],{"type":43,"value":364},"Match ",{"type":37,"tag":314,"props":366,"children":368},{"className":367},[],[369],{"type":43,"value":370},"version",{"type":43,"value":372}," exactly.",{"type":37,"tag":285,"props":374,"children":375},{},[376,377,383,385],{"type":43,"value":364},{"type":37,"tag":314,"props":378,"children":380},{"className":379},[],[381],{"type":43,"value":382},"node_info",{"type":43,"value":384}," using the inferred or provided values:\n",{"type":37,"tag":357,"props":386,"children":387},{},[388,414],{"type":37,"tag":285,"props":389,"children":390},{},[391,397,398,404,406,412],{"type":37,"tag":314,"props":392,"children":394},{"className":393},[],[395],{"type":43,"value":396},"image_family",{"type":43,"value":331},{"type":37,"tag":314,"props":399,"children":401},{"className":400},[],[402],{"type":43,"value":403},"COS_CONTAINERD",{"type":43,"value":405}," (COS) or ",{"type":37,"tag":314,"props":407,"children":409},{"className":408},[],[410],{"type":43,"value":411},"UBUNTU_CONTAINERD",{"type":43,"value":413}," (Ubuntu).",{"type":37,"tag":285,"props":415,"children":416},{},[417],{"type":43,"value":418},"Other fields match exactly.",{"type":37,"tag":285,"props":420,"children":421},{},[422,427,429,435,437,443,445,451,452,457],{"type":37,"tag":64,"props":423,"children":424},{},[425],{"type":43,"value":426},"Refine Search",{"type":43,"value":428},": If no exact match is found with defaults, try toggling ",{"type":37,"tag":314,"props":430,"children":432},{"className":431},[],[433],{"type":43,"value":434},"cgroup_mode",{"type":43,"value":436}," to ",{"type":37,"tag":314,"props":438,"children":440},{"className":439},[],[441],{"type":43,"value":442},"CGROUP_MODE_V1",{"type":43,"value":444}," or ",{"type":37,"tag":314,"props":446,"children":448},{"className":447},[],[449],{"type":43,"value":450},"gvisor_enabled",{"type":43,"value":436},{"type":37,"tag":314,"props":453,"children":455},{"className":454},[],[456],{"type":43,"value":203},{"type":43,"value":458}," and inform the user.",{"type":37,"tag":52,"props":460,"children":462},{"id":461},"example-output",[463],{"type":43,"value":464},"Example Output",{"type":37,"tag":46,"props":466,"children":467},{},[468,470,474,476,481,483,489],{"type":43,"value":469},"\"Based on your setup (GKE 1.34.1-gke.2909000, COS, and using the new H100 GPUs), I've inferred you need the ",{"type":37,"tag":64,"props":471,"children":472},{},[473],{"type":43,"value":179},{"type":43,"value":475}," image with ",{"type":37,"tag":64,"props":477,"children":478},{},[479],{"type":43,"value":480},"Accelerators",{"type":43,"value":482}," enabled. The golden base image is: ",{"type":37,"tag":314,"props":484,"children":486},{"className":485},[],[487],{"type":43,"value":488},"gke-1341-gke2909000-cos-125-19216-0-115-c-pre",{"type":43,"value":490},"\"",{"items":492,"total":584},[493,499,514,527,544,555,571],{"slug":4,"name":4,"fn":5,"description":6,"org":494,"tags":495,"stars":21,"repoUrl":22,"updatedAt":23},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[496,497,498],{"name":14,"slug":15,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"slug":500,"name":500,"fn":501,"description":502,"org":503,"tags":504,"stars":21,"repoUrl":22,"updatedAt":513},"gke-ai-troubleshooting-handle-disruption-gpu-tpu","diagnose GPU and TPU workload disruptions","Diagnose and predict node disruption during Compute Engine host maintenance for GPU and TPU workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[505,508,509,510],{"name":506,"slug":507,"type":16},"Debugging","debugging",{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":511,"slug":512,"type":16},"Performance","performance","2026-07-28T05:34:18.149515",{"slug":515,"name":515,"fn":516,"description":517,"org":518,"tags":519,"stars":21,"repoUrl":22,"updatedAt":526},"gke-ai-troubleshooting-jobset-interruption","diagnose GKE JobSet interruptions","Systematically diagnose GKE JobSet interruptions, restarts, and preemptions for AI\u002FML training workloads. Identifies preemption events, maintenance interruptions, bad host VMs, unhealthy pods, and coordinator worker failures.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[520,521,522,523],{"name":506,"slug":507,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":524,"slug":525,"type":16},"Observability","observability","2026-07-12T07:40:04.511878",{"slug":528,"name":528,"fn":529,"description":530,"org":531,"tags":532,"stars":21,"repoUrl":22,"updatedAt":543},"gke-ai-troubleshooting-skill-creation-guide","create GKE troubleshooting skill bundles","Expert instructions for building high-quality GKE troubleshooting skills. Codifies Step 0 context rules, zero-hallucination signatures, and explicit LQL\u002FPromQL query requirements.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[533,536,539,540],{"name":534,"slug":535,"type":16},"Documentation","documentation",{"name":537,"slug":538,"type":16},"Engineering","engineering",{"name":9,"slug":8,"type":16},{"name":541,"slug":542,"type":16},"Technical Writing","technical-writing","2026-07-12T07:39:50.73484",{"slug":545,"name":545,"fn":546,"description":547,"org":548,"tags":549,"stars":21,"repoUrl":22,"updatedAt":554},"gke-ai-troubleshooting-tpu-connection-failure-vbar-oom","diagnose GKE TPU connection failures","Diagnose and prevent `vbar_control_agent` segfaults and OOMs caused by race conditions during TPU device resets and frequent metrics collection (e.g. every 3s). Use when TPU slice initialization fails or `vbar_control_agent` crashes on TPU v6e nodes.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[550,551,552,553],{"name":506,"slug":507,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":511,"slug":512,"type":16},"2026-07-12T07:39:49.482979",{"slug":556,"name":556,"fn":557,"description":558,"org":559,"tags":560,"stars":21,"repoUrl":22,"updatedAt":570},"gke-app-onboarding","containerize and deploy apps to GKE","Workflows for containerizing and deploying applications to GKE for the first time.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[561,564,565,566,567],{"name":562,"slug":563,"type":16},"Containers","containers",{"name":14,"slug":15,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":568,"slug":569,"type":16},"Onboarding","onboarding","2026-07-12T07:39:41.935837",{"slug":572,"name":572,"fn":573,"description":574,"org":575,"tags":576,"stars":21,"repoUrl":22,"updatedAt":583},"gke-backup-dr","configure GKE backup and disaster recovery","Workflows for configuring Backup for GKE and disaster recovery.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[577,578,579,580],{"name":14,"slug":15,"type":16},{"name":9,"slug":8,"type":16},{"name":18,"slug":19,"type":16},{"name":581,"slug":582,"type":16},"Operations","operations","2026-07-12T07:39:34.806995",25,{"items":586,"total":767},[587,603,619,639,653,662,676,693,710,723,739,749],{"slug":588,"name":588,"fn":589,"description":590,"org":591,"tags":592,"stars":600,"repoUrl":601,"updatedAt":602},"kb-search","search and extract local knowledge base documents","Allows listing, searching and extracting information from local knowledge base documents for information about tables\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[593,594,597],{"name":534,"slug":535,"type":16},{"name":595,"slug":596,"type":16},"Knowledge Base","knowledge-base",{"name":598,"slug":599,"type":16},"Search","search",6749,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fknowledge-catalog","2026-07-12T07:38:52.157375",{"slug":604,"name":605,"fn":606,"description":607,"org":608,"tags":609,"stars":600,"repoUrl":601,"updatedAt":618},"knowledgecatalogdiscoveryagent","knowledge_catalog_discovery_agent","search and rank Knowledge Catalog data entries","Analyzes user queries, extracts relevant predicates, and utilizes Knowledge Catalog Search to find and rank the most relevant data entries. Engages with the user throughout the process.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[610,613,614,617],{"name":611,"slug":612,"type":16},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":16},{"name":615,"slug":616,"type":16},"Knowledge Management","knowledge-management",{"name":598,"slug":599,"type":16},"2026-07-12T07:38:22.196851",{"slug":620,"name":620,"fn":621,"description":622,"org":623,"tags":624,"stars":636,"repoUrl":637,"updatedAt":638},"contributing","contribute to Cloud Foundation Fabric","End-to-end workflow for contributing to Cloud Foundation Fabric: triaging GitHub issues, proactive feature development, validating with tests and Policy Troubleshooter, and submitting sanitized Pull Requests. Use when addressing a Fabric GitHub issue, developing a module or FAST stage change, or preparing a branch for a pull request.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[625,628,629,632,633],{"name":626,"slug":627,"type":16},"Automation","automation",{"name":537,"slug":538,"type":16},{"name":630,"slug":631,"type":16},"GitHub","github",{"name":9,"slug":8,"type":16},{"name":634,"slug":635,"type":16},"Pull Requests","pull-requests",2062,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-foundation-fabric","2026-07-31T06:23:36.935005",{"slug":640,"name":640,"fn":641,"description":642,"org":643,"tags":644,"stars":636,"repoUrl":637,"updatedAt":652},"fabric-builder","generate Terraform code for Google Cloud","Generates idiomatic Cloud Foundation Fabric (CFF) Terraform code using CFF modules. Use when users ask to create GCP resources, use Fabric modules, or generate Terraform code for Google Cloud.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[645,646,649],{"name":9,"slug":8,"type":16},{"name":647,"slug":648,"type":16},"Infrastructure as Code","infrastructure-as-code",{"name":650,"slug":651,"type":16},"Terraform","terraform","2026-07-12T07:38:23.514555",{"slug":654,"name":654,"fn":655,"description":656,"org":657,"tags":658,"stars":636,"repoUrl":637,"updatedAt":661},"fast-0-org-setup-prereqs","prepare prerequisites for FAST 0-org-setup","Guides the user step-by-step through the prerequisites for the FAST 0-org-setup stage, supporting both Standard GCP and Google Cloud Dedicated (GCD) environments. Use when a user asks to prepare or run prerequisites for 0-org-setup or bootstrap the FAST landing zone.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[659,660],{"name":9,"slug":8,"type":16},{"name":581,"slug":582,"type":16},"2026-07-12T07:38:28.127148",{"slug":663,"name":663,"fn":664,"description":665,"org":666,"tags":667,"stars":673,"repoUrl":674,"updatedAt":675},"agent-aware-cli","design agent-aware command-line interfaces","Guide for designing and implementing command-line interfaces (CLIs) that are equally usable by human developers and automated coding agents. Use when the user wants to build a CLI, apply CLI best practices, or use Go with Cobra and Viper.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[668,671,672],{"name":669,"slug":670,"type":16},"CLI","cli",{"name":537,"slug":538,"type":16},{"name":9,"slug":8,"type":16},1150,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-creative-studio","2026-07-12T07:39:08.41406",{"slug":677,"name":677,"fn":678,"description":679,"org":680,"tags":681,"stars":673,"repoUrl":674,"updatedAt":692},"build-mcp-genmedia","build and configure GenAI MCP servers","Builds the mcp-genmedia Go MCP servers (nanobanana, veo, lyria, gemini-multimodal, chirp3-hd, avtool) from source and wires them into settings.json. Use this skill whenever the MCP tools are missing or broken — typically at the start of a new session, after a container restart, or when \u002Ftmp has been wiped. The prebuilt binaries in \u002Fworkspace\u002F.local\u002Fbin\u002F have no exec bit and live on a noexec mount; this skill compiles fresh executables into \u002Ftmp\u002Fbin\u002F where execution is allowed.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[682,685,686,689],{"name":683,"slug":684,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":687,"slug":688,"type":16},"LLM","llm",{"name":690,"slug":691,"type":16},"MCP","mcp","2026-07-12T07:39:10.911302",{"slug":694,"name":694,"fn":695,"description":696,"org":697,"tags":698,"stars":673,"repoUrl":674,"updatedAt":709},"genmedia-audio-engineer","synthesize and mix audio content","Expert in audio synthesis, music generation, and mixing. Use when creating podcasts, background scores, or multi-track audio layering using mcp-chirp3-go, mcp-lyria-go, mcp-gemini-go, mcp-nanobanana-go, and mcp-avtool-go.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[699,702,705,706],{"name":700,"slug":701,"type":16},"Audio","audio",{"name":703,"slug":704,"type":16},"Creative","creative",{"name":9,"slug":8,"type":16},{"name":707,"slug":708,"type":16},"Vertex AI","vertex-ai","2026-07-12T07:39:16.623879",{"slug":711,"name":711,"fn":712,"description":713,"org":714,"tags":715,"stars":673,"repoUrl":674,"updatedAt":722},"genmedia-image-artist","generate and edit AI images","Expert in AI image generation and editing. Use when the user needs high-quality textures, character-consistent visuals, or image-to-image editing using mcp-nanobanana-go.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[716,717,718,721],{"name":703,"slug":704,"type":16},{"name":9,"slug":8,"type":16},{"name":719,"slug":720,"type":16},"Image Generation","image-generation",{"name":707,"slug":708,"type":16},"2026-07-12T07:39:15.372822",{"slug":724,"name":724,"fn":725,"description":726,"org":727,"tags":728,"stars":673,"repoUrl":674,"updatedAt":738},"genmedia-producer","produce multi-step media content","Expert media production assistant. Use when requested to help with storyboarding, podcast creation, audio assembly, or complex multi-step media workflows using the GenMedia MCP servers (Veo, Lyria, Gemini TTS, NanoBanana).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[729,730,731,732,735],{"name":700,"slug":701,"type":16},{"name":703,"slug":704,"type":16},{"name":9,"slug":8,"type":16},{"name":733,"slug":734,"type":16},"Media","media",{"name":736,"slug":737,"type":16},"Video","video","2026-07-12T07:39:09.672849",{"slug":740,"name":740,"fn":741,"description":742,"org":743,"tags":744,"stars":673,"repoUrl":674,"updatedAt":748},"genmedia-video-editor","edit and compose video content","Expert in video composition, editing, and format conversion. Use when the user wants to generate high-quality video, overlay images on video, concatenate clips, create GIFs, or sync audio to video using mcp-avtool-go and mcp-veo-go.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[745,746,747],{"name":703,"slug":704,"type":16},{"name":9,"slug":8,"type":16},{"name":736,"slug":737,"type":16},"2026-07-12T07:39:13.749081",{"slug":750,"name":750,"fn":751,"description":752,"org":753,"tags":754,"stars":673,"repoUrl":674,"updatedAt":766},"genmedia-voice-director","generate expressive text-to-speech with Gemini","Expert in casting, directing, and generating expressive text-to-speech using Gemini TTS. Use this when the user needs virtual voice actor personas, expressive speech generation, or multiple variations of a voiceover (like \"take 3 on the bounce\").",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[755,756,757,760,763],{"name":700,"slug":701,"type":16},{"name":703,"slug":704,"type":16},{"name":758,"slug":759,"type":16},"Gemini","gemini",{"name":761,"slug":762,"type":16},"Speech","speech",{"name":764,"slug":765,"type":16},"Text-to-Speech","text-to-speech","2026-07-12T07:39:17.86673",80]