[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-cloud-bigtable":3,"mdc--r9yxp0-key":34,"related-repo-google-cloud-bigtable":545,"related-org-google-cloud-bigtable":554},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":24,"repoUrl":25,"updatedAt":26,"license":27,"forks":28,"topics":29,"repo":30,"sourceUrl":32,"mdContent":33},"bigtable","manage and query Google Bigtable instances","Manage Google Bigtable instances\u002Ftables, design schemas, and query data using SQL or client libraries. Use for provisioning, schema updates, or code generation.",{"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",[13,17,20,23],{"name":14,"slug":15,"type":16},"Database","database","tag",{"name":18,"slug":19,"type":16},"SQL","sql",{"name":21,"slug":22,"type":16},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":16},20,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-bigtable-ecosystem","2026-07-12T07:41:44.159466",null,14,[],{"repoUrl":25,"stars":24,"forks":28,"topics":31,"description":27},[],"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-bigtable-ecosystem\u002Ftree\u002FHEAD\u002Fskills\u002Fbigtable","---\nname: bigtable\ndescription: Manage Google Bigtable instances\u002Ftables, design schemas, and query data using SQL or client libraries. Use for provisioning, schema updates, or code generation.\n---\n\n# Bigtable Skill\n\nThis skill provides core workflows and guidance for administering and developing with Google Bigtable. \n\n## Core Principles\n\n- **Control Plane vs. Data Plane:**\n  - Use **`gcloud`** for Control Plane operations: Manage Instances, Clusters, App Profiles, Backups and IAM. Create Tables, Logical Views, Materialized Views and Authorized Views.\n  - Use **`cbt`** for Data Plane operations: Update Tables, Column Families, and reading\u002Fwriting data.\n- **Performance First:** Bigtable is a NoSQL database. Efficiency is tied to Row Key design. Always warn about Full Table Scans.\n- **Client Selection:** For production use cases, **Java** or **Go** are preferred for their superior performance and feature coverage compared to other languages.\n- **Observability:** When diagnosing performance or hotspotting, **ALWAYS** mention **Key Visualizer** (via Cloud Console) as the primary diagnostic tool, followed by hot-tablets tool and table stats in gcloud CLI and `include-stats=full` option under `cbt read` to diagnose slow queries.\n\n> [!IMPORTANT]\n> **Safety Rule:** Always obtain explicit user confirmation before making non-emulator database changes.\n\n## Quick Recipes\n\n### 1. Querying Data\nUse SQL for complex transforms or aggregations and key-value APIs for simpler query patterns.\n*Note: Use exact match, prefix (_key LIKE 'myprefix%') or range predicates on `_key` to avoid expensive unbounded scans.*\n\nIf expensive scans (either unbounded or prefix or range queries scanning a large range) are unavoidable due to multiple access patterns that can’t all be accommodated in a single schema, consider one of these two options:\n- If the query  will be used in user facing and\u002For latency sensitive applications, use continuous materialized views with keys optimized for the additional access patterns.\n- If secondary access patterns are infrequent, batch patterns like ETL, ML model training or analytical read-only tasks, use Bigtable Data Boost instead. \n\n### 2. Manipulating Data\nUse key-value APIs for insert, update, increment and delete operations. SQL API is read-only.\n\n### 3. Data Model Definition (DDL)\nSQL API doesn't support DDL operations. Table creation, deletion, updates should be made using gcloud CLI. Logical Views and Continuous Materialized Views are defined as SQL queries but they must be created using gcloud CLI. \n\n## Reference Guides\n\n- **CLI Operations**:\n  - [infrastructure_management.md](references\u002Finfrastructure_management.md) - Provisioning instances, clusters, and table schemas.\n  - [cli_data_access.md](references\u002Fcli_data_access.md) - Reading and writing data via the `cbt` CLI.\n- **Design & Discovery**:\n  - [schema_design.md](references\u002Fschema_design.md) - Best practices for row keys and performance with tables and continuous materialized views.\n  - [dataplex.md](references\u002Fdataplex.md) - Data catalog search for Bigtable assets.\n- **Querying & Code**:\n  - [sql_guide.md](references\u002Fsql_guide.md) - Querying structured row keys via SQL and CLI.\n  - [client_libraries.md](references\u002Fclient_libraries.md) - Patterns for high-performance Go\u002FJava\u002FPython code.\n\n## Common Workflows\n\n### Schema Evolution (DevOps)\n1. **Prefer Terraform** for production schema changes to prevent accidental data loss.\n2. For manual `cbt` changes, verify column family GC policies:\n   ```bash\n   cbt createfamily [TABLE] [FAMILY]\n   cbt setgcpolicy [TABLE] [FAMILY] \"maxversions=5 AND maxage=30d\"\n   ```\n3. Reference [infrastructure_management.md](references\u002Finfrastructure_management.md) for full syntax.\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,48,54,61,184,204,210,217,236,241,254,260,265,271,276,282,396,402,408,539],{"type":40,"tag":41,"props":42,"children":44},"element","h1",{"id":43},"bigtable-skill",[45],{"type":46,"value":47},"text","Bigtable Skill",{"type":40,"tag":49,"props":50,"children":51},"p",{},[52],{"type":46,"value":53},"This skill provides core workflows and guidance for administering and developing with Google Bigtable.",{"type":40,"tag":55,"props":56,"children":58},"h2",{"id":57},"core-principles",[59],{"type":46,"value":60},"Core Principles",{"type":40,"tag":62,"props":63,"children":64},"ul",{},[65,110,120,144],{"type":40,"tag":66,"props":67,"children":68},"li",{},[69,75],{"type":40,"tag":70,"props":71,"children":72},"strong",{},[73],{"type":46,"value":74},"Control Plane vs. Data Plane:",{"type":40,"tag":62,"props":76,"children":77},{},[78,95],{"type":40,"tag":66,"props":79,"children":80},{},[81,83,93],{"type":46,"value":82},"Use ",{"type":40,"tag":70,"props":84,"children":85},{},[86],{"type":40,"tag":87,"props":88,"children":90},"code",{"className":89},[],[91],{"type":46,"value":92},"gcloud",{"type":46,"value":94}," for Control Plane operations: Manage Instances, Clusters, App Profiles, Backups and IAM. Create Tables, Logical Views, Materialized Views and Authorized Views.",{"type":40,"tag":66,"props":96,"children":97},{},[98,99,108],{"type":46,"value":82},{"type":40,"tag":70,"props":100,"children":101},{},[102],{"type":40,"tag":87,"props":103,"children":105},{"className":104},[],[106],{"type":46,"value":107},"cbt",{"type":46,"value":109}," for Data Plane operations: Update Tables, Column Families, and reading\u002Fwriting data.",{"type":40,"tag":66,"props":111,"children":112},{},[113,118],{"type":40,"tag":70,"props":114,"children":115},{},[116],{"type":46,"value":117},"Performance First:",{"type":46,"value":119}," Bigtable is a NoSQL database. Efficiency is tied to Row Key design. Always warn about Full Table Scans.",{"type":40,"tag":66,"props":121,"children":122},{},[123,128,130,135,137,142],{"type":40,"tag":70,"props":124,"children":125},{},[126],{"type":46,"value":127},"Client Selection:",{"type":46,"value":129}," For production use cases, ",{"type":40,"tag":70,"props":131,"children":132},{},[133],{"type":46,"value":134},"Java",{"type":46,"value":136}," or ",{"type":40,"tag":70,"props":138,"children":139},{},[140],{"type":46,"value":141},"Go",{"type":46,"value":143}," are preferred for their superior performance and feature coverage compared to other languages.",{"type":40,"tag":66,"props":145,"children":146},{},[147,152,154,159,161,166,168,174,176,182],{"type":40,"tag":70,"props":148,"children":149},{},[150],{"type":46,"value":151},"Observability:",{"type":46,"value":153}," When diagnosing performance or hotspotting, ",{"type":40,"tag":70,"props":155,"children":156},{},[157],{"type":46,"value":158},"ALWAYS",{"type":46,"value":160}," mention ",{"type":40,"tag":70,"props":162,"children":163},{},[164],{"type":46,"value":165},"Key Visualizer",{"type":46,"value":167}," (via Cloud Console) as the primary diagnostic tool, followed by hot-tablets tool and table stats in gcloud CLI and ",{"type":40,"tag":87,"props":169,"children":171},{"className":170},[],[172],{"type":46,"value":173},"include-stats=full",{"type":46,"value":175}," option under ",{"type":40,"tag":87,"props":177,"children":179},{"className":178},[],[180],{"type":46,"value":181},"cbt read",{"type":46,"value":183}," to diagnose slow queries.",{"type":40,"tag":185,"props":186,"children":187},"blockquote",{},[188],{"type":40,"tag":49,"props":189,"children":190},{},[191,197,202],{"type":40,"tag":192,"props":193,"children":194},"span",{},[195],{"type":46,"value":196},"!IMPORTANT",{"type":40,"tag":70,"props":198,"children":199},{},[200],{"type":46,"value":201},"Safety Rule:",{"type":46,"value":203}," Always obtain explicit user confirmation before making non-emulator database changes.",{"type":40,"tag":55,"props":205,"children":207},{"id":206},"quick-recipes",[208],{"type":46,"value":209},"Quick Recipes",{"type":40,"tag":211,"props":212,"children":214},"h3",{"id":213},"_1-querying-data",[215],{"type":46,"value":216},"1. Querying Data",{"type":40,"tag":49,"props":218,"children":219},{},[220,222],{"type":46,"value":221},"Use SQL for complex transforms or aggregations and key-value APIs for simpler query patterns.\n",{"type":40,"tag":223,"props":224,"children":225},"em",{},[226,228,234],{"type":46,"value":227},"Note: Use exact match, prefix (_key LIKE 'myprefix%') or range predicates on ",{"type":40,"tag":87,"props":229,"children":231},{"className":230},[],[232],{"type":46,"value":233},"_key",{"type":46,"value":235}," to avoid expensive unbounded scans.",{"type":40,"tag":49,"props":237,"children":238},{},[239],{"type":46,"value":240},"If expensive scans (either unbounded or prefix or range queries scanning a large range) are unavoidable due to multiple access patterns that can’t all be accommodated in a single schema, consider one of these two options:",{"type":40,"tag":62,"props":242,"children":243},{},[244,249],{"type":40,"tag":66,"props":245,"children":246},{},[247],{"type":46,"value":248},"If the query  will be used in user facing and\u002For latency sensitive applications, use continuous materialized views with keys optimized for the additional access patterns.",{"type":40,"tag":66,"props":250,"children":251},{},[252],{"type":46,"value":253},"If secondary access patterns are infrequent, batch patterns like ETL, ML model training or analytical read-only tasks, use Bigtable Data Boost instead.",{"type":40,"tag":211,"props":255,"children":257},{"id":256},"_2-manipulating-data",[258],{"type":46,"value":259},"2. Manipulating Data",{"type":40,"tag":49,"props":261,"children":262},{},[263],{"type":46,"value":264},"Use key-value APIs for insert, update, increment and delete operations. SQL API is read-only.",{"type":40,"tag":211,"props":266,"children":268},{"id":267},"_3-data-model-definition-ddl",[269],{"type":46,"value":270},"3. Data Model Definition (DDL)",{"type":40,"tag":49,"props":272,"children":273},{},[274],{"type":46,"value":275},"SQL API doesn't support DDL operations. Table creation, deletion, updates should be made using gcloud CLI. Logical Views and Continuous Materialized Views are defined as SQL queries but they must be created using gcloud CLI.",{"type":40,"tag":55,"props":277,"children":279},{"id":278},"reference-guides",[280],{"type":46,"value":281},"Reference Guides",{"type":40,"tag":62,"props":283,"children":284},{},[285,328,362],{"type":40,"tag":66,"props":286,"children":287},{},[288,293,295],{"type":40,"tag":70,"props":289,"children":290},{},[291],{"type":46,"value":292},"CLI Operations",{"type":46,"value":294},":\n",{"type":40,"tag":62,"props":296,"children":297},{},[298,310],{"type":40,"tag":66,"props":299,"children":300},{},[301,308],{"type":40,"tag":302,"props":303,"children":305},"a",{"href":304},"references\u002Finfrastructure_management.md",[306],{"type":46,"value":307},"infrastructure_management.md",{"type":46,"value":309}," - Provisioning instances, clusters, and table schemas.",{"type":40,"tag":66,"props":311,"children":312},{},[313,319,321,326],{"type":40,"tag":302,"props":314,"children":316},{"href":315},"references\u002Fcli_data_access.md",[317],{"type":46,"value":318},"cli_data_access.md",{"type":46,"value":320}," - Reading and writing data via the ",{"type":40,"tag":87,"props":322,"children":324},{"className":323},[],[325],{"type":46,"value":107},{"type":46,"value":327}," CLI.",{"type":40,"tag":66,"props":329,"children":330},{},[331,336,337],{"type":40,"tag":70,"props":332,"children":333},{},[334],{"type":46,"value":335},"Design & Discovery",{"type":46,"value":294},{"type":40,"tag":62,"props":338,"children":339},{},[340,351],{"type":40,"tag":66,"props":341,"children":342},{},[343,349],{"type":40,"tag":302,"props":344,"children":346},{"href":345},"references\u002Fschema_design.md",[347],{"type":46,"value":348},"schema_design.md",{"type":46,"value":350}," - Best practices for row keys and performance with tables and continuous materialized views.",{"type":40,"tag":66,"props":352,"children":353},{},[354,360],{"type":40,"tag":302,"props":355,"children":357},{"href":356},"references\u002Fdataplex.md",[358],{"type":46,"value":359},"dataplex.md",{"type":46,"value":361}," - Data catalog search for Bigtable assets.",{"type":40,"tag":66,"props":363,"children":364},{},[365,370,371],{"type":40,"tag":70,"props":366,"children":367},{},[368],{"type":46,"value":369},"Querying & Code",{"type":46,"value":294},{"type":40,"tag":62,"props":372,"children":373},{},[374,385],{"type":40,"tag":66,"props":375,"children":376},{},[377,383],{"type":40,"tag":302,"props":378,"children":380},{"href":379},"references\u002Fsql_guide.md",[381],{"type":46,"value":382},"sql_guide.md",{"type":46,"value":384}," - Querying structured row keys via SQL and CLI.",{"type":40,"tag":66,"props":386,"children":387},{},[388,394],{"type":40,"tag":302,"props":389,"children":391},{"href":390},"references\u002Fclient_libraries.md",[392],{"type":46,"value":393},"client_libraries.md",{"type":46,"value":395}," - Patterns for high-performance Go\u002FJava\u002FPython code.",{"type":40,"tag":55,"props":397,"children":399},{"id":398},"common-workflows",[400],{"type":46,"value":401},"Common Workflows",{"type":40,"tag":211,"props":403,"children":405},{"id":404},"schema-evolution-devops",[406],{"type":46,"value":407},"Schema Evolution (DevOps)",{"type":40,"tag":409,"props":410,"children":411},"ol",{},[412,422,528],{"type":40,"tag":66,"props":413,"children":414},{},[415,420],{"type":40,"tag":70,"props":416,"children":417},{},[418],{"type":46,"value":419},"Prefer Terraform",{"type":46,"value":421}," for production schema changes to prevent accidental data loss.",{"type":40,"tag":66,"props":423,"children":424},{},[425,427,432,434],{"type":46,"value":426},"For manual ",{"type":40,"tag":87,"props":428,"children":430},{"className":429},[],[431],{"type":46,"value":107},{"type":46,"value":433}," changes, verify column family GC policies:\n",{"type":40,"tag":435,"props":436,"children":441},"pre",{"className":437,"code":438,"language":439,"meta":440,"style":440},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","cbt createfamily [TABLE] [FAMILY]\ncbt setgcpolicy [TABLE] [FAMILY] \"maxversions=5 AND maxage=30d\"\n","bash","",[442],{"type":40,"tag":87,"props":443,"children":444},{"__ignoreMap":440},[445,483],{"type":40,"tag":192,"props":446,"children":449},{"class":447,"line":448},"line",1,[450,455,461,467,473,478],{"type":40,"tag":192,"props":451,"children":453},{"style":452},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[454],{"type":46,"value":107},{"type":40,"tag":192,"props":456,"children":458},{"style":457},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[459],{"type":46,"value":460}," createfamily",{"type":40,"tag":192,"props":462,"children":464},{"style":463},"--shiki-light:#90A4AE;--shiki-default:#EEFFFF;--shiki-dark:#BABED8",[465],{"type":46,"value":466}," [TABLE] ",{"type":40,"tag":192,"props":468,"children":470},{"style":469},"--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF",[471],{"type":46,"value":472},"[",{"type":40,"tag":192,"props":474,"children":475},{"style":463},[476],{"type":46,"value":477},"FAMILY",{"type":40,"tag":192,"props":479,"children":480},{"style":469},[481],{"type":46,"value":482},"]\n",{"type":40,"tag":192,"props":484,"children":486},{"class":447,"line":485},2,[487,491,496,500,504,508,513,518,523],{"type":40,"tag":192,"props":488,"children":489},{"style":452},[490],{"type":46,"value":107},{"type":40,"tag":192,"props":492,"children":493},{"style":457},[494],{"type":46,"value":495}," setgcpolicy",{"type":40,"tag":192,"props":497,"children":498},{"style":463},[499],{"type":46,"value":466},{"type":40,"tag":192,"props":501,"children":502},{"style":469},[503],{"type":46,"value":472},{"type":40,"tag":192,"props":505,"children":506},{"style":463},[507],{"type":46,"value":477},{"type":40,"tag":192,"props":509,"children":510},{"style":469},[511],{"type":46,"value":512},"]",{"type":40,"tag":192,"props":514,"children":515},{"style":469},[516],{"type":46,"value":517}," \"",{"type":40,"tag":192,"props":519,"children":520},{"style":457},[521],{"type":46,"value":522},"maxversions=5 AND maxage=30d",{"type":40,"tag":192,"props":524,"children":525},{"style":469},[526],{"type":46,"value":527},"\"\n",{"type":40,"tag":66,"props":529,"children":530},{},[531,533,537],{"type":46,"value":532},"Reference ",{"type":40,"tag":302,"props":534,"children":535},{"href":304},[536],{"type":46,"value":307},{"type":46,"value":538}," for full syntax.",{"type":40,"tag":540,"props":541,"children":542},"style",{},[543],{"type":46,"value":544},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":546,"total":448},[547],{"slug":4,"name":4,"fn":5,"description":6,"org":548,"tags":549,"stars":24,"repoUrl":25,"updatedAt":26},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[550,551,552,553],{"name":14,"slug":15,"type":16},{"name":9,"slug":8,"type":16},{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"items":555,"total":742},[556,574,590,612,626,637,651,668,685,698,714,724],{"slug":557,"name":557,"fn":558,"description":559,"org":560,"tags":561,"stars":571,"repoUrl":572,"updatedAt":573},"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},[562,565,568],{"name":563,"slug":564,"type":16},"Documentation","documentation",{"name":566,"slug":567,"type":16},"Knowledge Base","knowledge-base",{"name":569,"slug":570,"type":16},"Search","search",6749,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fknowledge-catalog","2026-07-12T07:38:52.157375",{"slug":575,"name":576,"fn":577,"description":578,"org":579,"tags":580,"stars":571,"repoUrl":572,"updatedAt":589},"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},[581,584,585,588],{"name":582,"slug":583,"type":16},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":16},{"name":586,"slug":587,"type":16},"Knowledge Management","knowledge-management",{"name":569,"slug":570,"type":16},"2026-07-12T07:38:22.196851",{"slug":591,"name":591,"fn":592,"description":593,"org":594,"tags":595,"stars":609,"repoUrl":610,"updatedAt":611},"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},[596,599,602,605,606],{"name":597,"slug":598,"type":16},"Automation","automation",{"name":600,"slug":601,"type":16},"Engineering","engineering",{"name":603,"slug":604,"type":16},"GitHub","github",{"name":9,"slug":8,"type":16},{"name":607,"slug":608,"type":16},"Pull Requests","pull-requests",2062,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-foundation-fabric","2026-07-31T06:23:36.935005",{"slug":613,"name":613,"fn":614,"description":615,"org":616,"tags":617,"stars":609,"repoUrl":610,"updatedAt":625},"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},[618,619,622],{"name":9,"slug":8,"type":16},{"name":620,"slug":621,"type":16},"Infrastructure as Code","infrastructure-as-code",{"name":623,"slug":624,"type":16},"Terraform","terraform","2026-07-12T07:38:23.514555",{"slug":627,"name":627,"fn":628,"description":629,"org":630,"tags":631,"stars":609,"repoUrl":610,"updatedAt":636},"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},[632,633],{"name":9,"slug":8,"type":16},{"name":634,"slug":635,"type":16},"Operations","operations","2026-07-12T07:38:28.127148",{"slug":638,"name":638,"fn":639,"description":640,"org":641,"tags":642,"stars":648,"repoUrl":649,"updatedAt":650},"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},[643,646,647],{"name":644,"slug":645,"type":16},"CLI","cli",{"name":600,"slug":601,"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":652,"name":652,"fn":653,"description":654,"org":655,"tags":656,"stars":648,"repoUrl":649,"updatedAt":667},"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},[657,660,661,664],{"name":658,"slug":659,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":662,"slug":663,"type":16},"LLM","llm",{"name":665,"slug":666,"type":16},"MCP","mcp","2026-07-12T07:39:10.911302",{"slug":669,"name":669,"fn":670,"description":671,"org":672,"tags":673,"stars":648,"repoUrl":649,"updatedAt":684},"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},[674,677,680,681],{"name":675,"slug":676,"type":16},"Audio","audio",{"name":678,"slug":679,"type":16},"Creative","creative",{"name":9,"slug":8,"type":16},{"name":682,"slug":683,"type":16},"Vertex AI","vertex-ai","2026-07-12T07:39:16.623879",{"slug":686,"name":686,"fn":687,"description":688,"org":689,"tags":690,"stars":648,"repoUrl":649,"updatedAt":697},"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},[691,692,693,696],{"name":678,"slug":679,"type":16},{"name":9,"slug":8,"type":16},{"name":694,"slug":695,"type":16},"Image Generation","image-generation",{"name":682,"slug":683,"type":16},"2026-07-12T07:39:15.372822",{"slug":699,"name":699,"fn":700,"description":701,"org":702,"tags":703,"stars":648,"repoUrl":649,"updatedAt":713},"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},[704,705,706,707,710],{"name":675,"slug":676,"type":16},{"name":678,"slug":679,"type":16},{"name":9,"slug":8,"type":16},{"name":708,"slug":709,"type":16},"Media","media",{"name":711,"slug":712,"type":16},"Video","video","2026-07-12T07:39:09.672849",{"slug":715,"name":715,"fn":716,"description":717,"org":718,"tags":719,"stars":648,"repoUrl":649,"updatedAt":723},"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},[720,721,722],{"name":678,"slug":679,"type":16},{"name":9,"slug":8,"type":16},{"name":711,"slug":712,"type":16},"2026-07-12T07:39:13.749081",{"slug":725,"name":725,"fn":726,"description":727,"org":728,"tags":729,"stars":648,"repoUrl":649,"updatedAt":741},"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},[730,731,732,735,738],{"name":675,"slug":676,"type":16},{"name":678,"slug":679,"type":16},{"name":733,"slug":734,"type":16},"Gemini","gemini",{"name":736,"slug":737,"type":16},"Speech","speech",{"name":739,"slug":740,"type":16},"Text-to-Speech","text-to-speech","2026-07-12T07:39:17.86673",80]