[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-cloud-target-evidence-dossier":3,"mdc-a0jcfm-key":33,"related-org-google-cloud-target-evidence-dossier":436,"related-repo-google-cloud-target-evidence-dossier":625},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":31,"mdContent":32},"target-evidence-dossier","build target validation dossiers","Build a target-validation dossier for a gene \u002F protein \u002F pathway — biology, disease association, druggability, existing programs, key publications, trial pipeline, safety signals. Use for early-stage discovery target review, portfolio decisions, or when the user asks \"what do we know about target X\".",{"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},"Research","research","tag",{"name":18,"slug":19,"type":16},"Life Sciences","life-sciences",{"name":21,"slug":22,"type":16},"Bioinformatics","bioinformatics",14,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FLifeSciences","2026-07-12T07:40:53.048652",null,10,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":26},[],"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FLifeSciences\u002Ftree\u002FHEAD\u002Fapplications\u002Fpharma-on-gemini-enterprise\u002Fbiocompass-on-gemini-enterprise\u002Fapp\u002Fskills\u002Ftarget-evidence-dossier","---\nname: target-evidence-dossier\ndescription: Build a target-validation dossier for a gene \u002F protein \u002F pathway — biology, disease association, druggability, existing programs, key publications, trial pipeline, safety signals. Use for early-stage discovery target review, portfolio decisions, or when the user asks \"what do we know about target X\".\n---\n\n# Target Evidence Dossier\n\nYou are building the evidence package a pharma R&D team uses to decide\nwhether to advance, deprioritize, or further-validate a target. Audience\nis a biology or computational-bio team lead — they want compactness and\ncitations, not narrative fluff.\n\n## Workflow\n\n### 1. Identify the target canonically\n\nCall `lookup_entity_id` with `concept=\"gene\"` to get the canonical\nPubTator3 ID (e.g. `@GENE_BRCA1`). Note any synonyms \u002F aliases \u002F paralogs\nthe user should be aware of (PubTator3 returns these; surface them\nprominently because alias drift causes evidence to be missed).\n\n### 2. Biology section\n\nUse `search_pubmed` with `publication_types=[\"review\"]` on the gene name\nto surface the canonical reviews. Distill:\n\n- Protein family + domain architecture.\n- Cellular localization and expression pattern (which tissues highly\n  express it; which cell types).\n- Known biological function and pathway membership.\n- Knockout \u002F loss-of-function phenotype (mouse and, where available,\n  human LoF).\n\n### 3. Disease association\n\nThree angles, in order:\n\n1. **Genetic association** — call `find_related_entities` with the gene\n   ID, `relation_type=\"associate\"`, `target_type=\"disease\"`. Cross-check\n   against `search_pubmed` for `\"\u003Cgene> AND GWAS\"` and\n   `\"\u003Cgene> AND mutation AND \u003Cdisease>\"`.\n2. **Functional \u002F mechanistic association** — `relation_type=\"cause\"` and\n   `relation_type=\"positive_correlate\"` \u002F `\"negative_correlate\"`.\n3. **Expression-based association** — note if the literature flags\n   over- \u002F under-expression in disease tissue.\n\nTag each association with strength of evidence (genetic > mechanistic >\ncorrelation).\n\n### 4. Druggability + existing programs\n\n- Existing drugs \u002F probes: `find_related_entities` with the gene ID,\n  `relation_type=\"inhibit\"` and `relation_type=\"stimulate\"`,\n  `target_type=\"chemical\"`.\n- Trials targeting it: `search_clinical_trials` with `intervention=`\n  the gene name and \u002F or `condition=` the leading associated indication.\n  Group results by sponsor and phase.\n- Modality landscape: small molecule vs. biologic vs. PROTAC vs. genetic\n  medicine. The trial table usually answers this implicitly.\n\n### 5. Translatability + safety signals\n\n- Animal-model evidence: include reviews that cite KO\u002FCKO mouse phenotypes.\n- Human genetic evidence: surface known LoF tolerance — if humans with\n  natural LoF are healthy, that's a positive translatability signal; if\n  LoF is associated with severe disease, flag the on-target safety risk.\n- Literature on pathway-level toxicity (e.g. inhibiting target X disrupts\n  pathway Y which controls Z).\n\n### 6. Output\n\nFinal structure:\n\n```\n# Target dossier — \u003CGENE_SYMBOL>\n\n## Snapshot\n- Family \u002F domain \u002F localization\n- Strongest disease association (1 sentence + PMID)\n- Druggability verdict (Tractable \u002F Challenging \u002F Undruggable + 1 sentence)\n- Pipeline status (count of trials by phase, lead sponsors)\n\n## Biology\n... cited bullets ...\n\n## Disease association\n| Disease | Evidence type | Strength | Key refs |\n\n## Existing programs\n| Asset \u002F probe | Modality | Sponsor | Phase | NCT |\n\n## Translatability + safety\n... cited bullets ...\n\n## Open questions \u002F next experiments\n... 3-5 bullets framed as testable hypotheses ...\n\n## References\nPMIDs grouped by section.\n```\n\nOptionally render a one-panel target-context diagram via\n`visualize_concept` (`figure_type=\"diagram\"`) — protein in its pathway,\ndisease tissue overlay, existing drugs as inhibitor arrows. Useful for\nslide use.\n\n## Guardrails\n\n- Distinguish \"X is associated with disease Y\" from \"X causes disease Y\" —\n  use the strength-of-evidence tag.\n- Do not invent KO phenotypes or LoF data — if the literature does not\n  cover it, write \"no published mouse KO data found\" rather than\n  speculating.\n- Aliases matter: if PubTator3 returns multiple canonical IDs for the\n  query, run the dossier on each and note the alias mapping.\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,46,52,59,66,96,102,122,147,153,158,262,267,273,344,350,368,374,379,391,412,418],{"type":39,"tag":40,"props":41,"children":42},"element","h1",{"id":4},[43],{"type":44,"value":45},"text","Target Evidence Dossier",{"type":39,"tag":47,"props":48,"children":49},"p",{},[50],{"type":44,"value":51},"You are building the evidence package a pharma R&D team uses to decide\nwhether to advance, deprioritize, or further-validate a target. Audience\nis a biology or computational-bio team lead — they want compactness and\ncitations, not narrative fluff.",{"type":39,"tag":53,"props":54,"children":56},"h2",{"id":55},"workflow",[57],{"type":44,"value":58},"Workflow",{"type":39,"tag":60,"props":61,"children":63},"h3",{"id":62},"_1-identify-the-target-canonically",[64],{"type":44,"value":65},"1. Identify the target canonically",{"type":39,"tag":47,"props":67,"children":68},{},[69,71,78,80,86,88,94],{"type":44,"value":70},"Call ",{"type":39,"tag":72,"props":73,"children":75},"code",{"className":74},[],[76],{"type":44,"value":77},"lookup_entity_id",{"type":44,"value":79}," with ",{"type":39,"tag":72,"props":81,"children":83},{"className":82},[],[84],{"type":44,"value":85},"concept=\"gene\"",{"type":44,"value":87}," to get the canonical\nPubTator3 ID (e.g. ",{"type":39,"tag":72,"props":89,"children":91},{"className":90},[],[92],{"type":44,"value":93},"@GENE_BRCA1",{"type":44,"value":95},"). Note any synonyms \u002F aliases \u002F paralogs\nthe user should be aware of (PubTator3 returns these; surface them\nprominently because alias drift causes evidence to be missed).",{"type":39,"tag":60,"props":97,"children":99},{"id":98},"_2-biology-section",[100],{"type":44,"value":101},"2. Biology section",{"type":39,"tag":47,"props":103,"children":104},{},[105,107,113,114,120],{"type":44,"value":106},"Use ",{"type":39,"tag":72,"props":108,"children":110},{"className":109},[],[111],{"type":44,"value":112},"search_pubmed",{"type":44,"value":79},{"type":39,"tag":72,"props":115,"children":117},{"className":116},[],[118],{"type":44,"value":119},"publication_types=[\"review\"]",{"type":44,"value":121}," on the gene name\nto surface the canonical reviews. Distill:",{"type":39,"tag":123,"props":124,"children":125},"ul",{},[126,132,137,142],{"type":39,"tag":127,"props":128,"children":129},"li",{},[130],{"type":44,"value":131},"Protein family + domain architecture.",{"type":39,"tag":127,"props":133,"children":134},{},[135],{"type":44,"value":136},"Cellular localization and expression pattern (which tissues highly\nexpress it; which cell types).",{"type":39,"tag":127,"props":138,"children":139},{},[140],{"type":44,"value":141},"Known biological function and pathway membership.",{"type":39,"tag":127,"props":143,"children":144},{},[145],{"type":44,"value":146},"Knockout \u002F loss-of-function phenotype (mouse and, where available,\nhuman LoF).",{"type":39,"tag":60,"props":148,"children":150},{"id":149},"_3-disease-association",[151],{"type":44,"value":152},"3. Disease association",{"type":39,"tag":47,"props":154,"children":155},{},[156],{"type":44,"value":157},"Three angles, in order:",{"type":39,"tag":159,"props":160,"children":161},"ol",{},[162,220,252],{"type":39,"tag":127,"props":163,"children":164},{},[165,171,173,179,181,187,189,195,197,202,204,210,212,218],{"type":39,"tag":166,"props":167,"children":168},"strong",{},[169],{"type":44,"value":170},"Genetic association",{"type":44,"value":172}," — call ",{"type":39,"tag":72,"props":174,"children":176},{"className":175},[],[177],{"type":44,"value":178},"find_related_entities",{"type":44,"value":180}," with the gene\nID, ",{"type":39,"tag":72,"props":182,"children":184},{"className":183},[],[185],{"type":44,"value":186},"relation_type=\"associate\"",{"type":44,"value":188},", ",{"type":39,"tag":72,"props":190,"children":192},{"className":191},[],[193],{"type":44,"value":194},"target_type=\"disease\"",{"type":44,"value":196},". Cross-check\nagainst ",{"type":39,"tag":72,"props":198,"children":200},{"className":199},[],[201],{"type":44,"value":112},{"type":44,"value":203}," for ",{"type":39,"tag":72,"props":205,"children":207},{"className":206},[],[208],{"type":44,"value":209},"\"\u003Cgene> AND GWAS\"",{"type":44,"value":211}," and\n",{"type":39,"tag":72,"props":213,"children":215},{"className":214},[],[216],{"type":44,"value":217},"\"\u003Cgene> AND mutation AND \u003Cdisease>\"",{"type":44,"value":219},".",{"type":39,"tag":127,"props":221,"children":222},{},[223,228,230,236,237,243,245,251],{"type":39,"tag":166,"props":224,"children":225},{},[226],{"type":44,"value":227},"Functional \u002F mechanistic association",{"type":44,"value":229}," — ",{"type":39,"tag":72,"props":231,"children":233},{"className":232},[],[234],{"type":44,"value":235},"relation_type=\"cause\"",{"type":44,"value":211},{"type":39,"tag":72,"props":238,"children":240},{"className":239},[],[241],{"type":44,"value":242},"relation_type=\"positive_correlate\"",{"type":44,"value":244}," \u002F ",{"type":39,"tag":72,"props":246,"children":248},{"className":247},[],[249],{"type":44,"value":250},"\"negative_correlate\"",{"type":44,"value":219},{"type":39,"tag":127,"props":253,"children":254},{},[255,260],{"type":39,"tag":166,"props":256,"children":257},{},[258],{"type":44,"value":259},"Expression-based association",{"type":44,"value":261}," — note if the literature flags\nover- \u002F under-expression in disease tissue.",{"type":39,"tag":47,"props":263,"children":264},{},[265],{"type":44,"value":266},"Tag each association with strength of evidence (genetic > mechanistic >\ncorrelation).",{"type":39,"tag":60,"props":268,"children":270},{"id":269},"_4-druggability-existing-programs",[271],{"type":44,"value":272},"4. Druggability + existing programs",{"type":39,"tag":123,"props":274,"children":275},{},[276,311,339],{"type":39,"tag":127,"props":277,"children":278},{},[279,281,286,288,294,296,302,304,310],{"type":44,"value":280},"Existing drugs \u002F probes: ",{"type":39,"tag":72,"props":282,"children":284},{"className":283},[],[285],{"type":44,"value":178},{"type":44,"value":287}," with the gene ID,\n",{"type":39,"tag":72,"props":289,"children":291},{"className":290},[],[292],{"type":44,"value":293},"relation_type=\"inhibit\"",{"type":44,"value":295}," and ",{"type":39,"tag":72,"props":297,"children":299},{"className":298},[],[300],{"type":44,"value":301},"relation_type=\"stimulate\"",{"type":44,"value":303},",\n",{"type":39,"tag":72,"props":305,"children":307},{"className":306},[],[308],{"type":44,"value":309},"target_type=\"chemical\"",{"type":44,"value":219},{"type":39,"tag":127,"props":312,"children":313},{},[314,316,322,323,329,331,337],{"type":44,"value":315},"Trials targeting it: ",{"type":39,"tag":72,"props":317,"children":319},{"className":318},[],[320],{"type":44,"value":321},"search_clinical_trials",{"type":44,"value":79},{"type":39,"tag":72,"props":324,"children":326},{"className":325},[],[327],{"type":44,"value":328},"intervention=",{"type":44,"value":330},"\nthe gene name and \u002F or ",{"type":39,"tag":72,"props":332,"children":334},{"className":333},[],[335],{"type":44,"value":336},"condition=",{"type":44,"value":338}," the leading associated indication.\nGroup results by sponsor and phase.",{"type":39,"tag":127,"props":340,"children":341},{},[342],{"type":44,"value":343},"Modality landscape: small molecule vs. biologic vs. PROTAC vs. genetic\nmedicine. The trial table usually answers this implicitly.",{"type":39,"tag":60,"props":345,"children":347},{"id":346},"_5-translatability-safety-signals",[348],{"type":44,"value":349},"5. Translatability + safety signals",{"type":39,"tag":123,"props":351,"children":352},{},[353,358,363],{"type":39,"tag":127,"props":354,"children":355},{},[356],{"type":44,"value":357},"Animal-model evidence: include reviews that cite KO\u002FCKO mouse phenotypes.",{"type":39,"tag":127,"props":359,"children":360},{},[361],{"type":44,"value":362},"Human genetic evidence: surface known LoF tolerance — if humans with\nnatural LoF are healthy, that's a positive translatability signal; if\nLoF is associated with severe disease, flag the on-target safety risk.",{"type":39,"tag":127,"props":364,"children":365},{},[366],{"type":44,"value":367},"Literature on pathway-level toxicity (e.g. inhibiting target X disrupts\npathway Y which controls Z).",{"type":39,"tag":60,"props":369,"children":371},{"id":370},"_6-output",[372],{"type":44,"value":373},"6. Output",{"type":39,"tag":47,"props":375,"children":376},{},[377],{"type":44,"value":378},"Final structure:",{"type":39,"tag":380,"props":381,"children":385},"pre",{"className":382,"code":384,"language":44},[383],"language-text","# Target dossier — \u003CGENE_SYMBOL>\n\n## Snapshot\n- Family \u002F domain \u002F localization\n- Strongest disease association (1 sentence + PMID)\n- Druggability verdict (Tractable \u002F Challenging \u002F Undruggable + 1 sentence)\n- Pipeline status (count of trials by phase, lead sponsors)\n\n## Biology\n... cited bullets ...\n\n## Disease association\n| Disease | Evidence type | Strength | Key refs |\n\n## Existing programs\n| Asset \u002F probe | Modality | Sponsor | Phase | NCT |\n\n## Translatability + safety\n... cited bullets ...\n\n## Open questions \u002F next experiments\n... 3-5 bullets framed as testable hypotheses ...\n\n## References\nPMIDs grouped by section.\n",[386],{"type":39,"tag":72,"props":387,"children":389},{"__ignoreMap":388},"",[390],{"type":44,"value":384},{"type":39,"tag":47,"props":392,"children":393},{},[394,396,402,404,410],{"type":44,"value":395},"Optionally render a one-panel target-context diagram via\n",{"type":39,"tag":72,"props":397,"children":399},{"className":398},[],[400],{"type":44,"value":401},"visualize_concept",{"type":44,"value":403}," (",{"type":39,"tag":72,"props":405,"children":407},{"className":406},[],[408],{"type":44,"value":409},"figure_type=\"diagram\"",{"type":44,"value":411},") — protein in its pathway,\ndisease tissue overlay, existing drugs as inhibitor arrows. Useful for\nslide use.",{"type":39,"tag":53,"props":413,"children":415},{"id":414},"guardrails",[416],{"type":44,"value":417},"Guardrails",{"type":39,"tag":123,"props":419,"children":420},{},[421,426,431],{"type":39,"tag":127,"props":422,"children":423},{},[424],{"type":44,"value":425},"Distinguish \"X is associated with disease Y\" from \"X causes disease Y\" —\nuse the strength-of-evidence tag.",{"type":39,"tag":127,"props":427,"children":428},{},[429],{"type":44,"value":430},"Do not invent KO phenotypes or LoF data — if the literature does not\ncover it, write \"no published mouse KO data found\" rather than\nspeculating.",{"type":39,"tag":127,"props":432,"children":433},{},[434],{"type":44,"value":435},"Aliases matter: if PubTator3 returns multiple canonical IDs for the\nquery, run the dossier on each and note the alias mapping.",{"items":437,"total":624},[438,456,472,494,508,519,533,550,567,580,596,606],{"slug":439,"name":439,"fn":440,"description":441,"org":442,"tags":443,"stars":453,"repoUrl":454,"updatedAt":455},"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},[444,447,450],{"name":445,"slug":446,"type":16},"Documentation","documentation",{"name":448,"slug":449,"type":16},"Knowledge Base","knowledge-base",{"name":451,"slug":452,"type":16},"Search","search",6749,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fknowledge-catalog","2026-07-12T07:38:52.157375",{"slug":457,"name":458,"fn":459,"description":460,"org":461,"tags":462,"stars":453,"repoUrl":454,"updatedAt":471},"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},[463,466,467,470],{"name":464,"slug":465,"type":16},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":16},{"name":468,"slug":469,"type":16},"Knowledge Management","knowledge-management",{"name":451,"slug":452,"type":16},"2026-07-12T07:38:22.196851",{"slug":473,"name":473,"fn":474,"description":475,"org":476,"tags":477,"stars":491,"repoUrl":492,"updatedAt":493},"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},[478,481,484,487,488],{"name":479,"slug":480,"type":16},"Automation","automation",{"name":482,"slug":483,"type":16},"Engineering","engineering",{"name":485,"slug":486,"type":16},"GitHub","github",{"name":9,"slug":8,"type":16},{"name":489,"slug":490,"type":16},"Pull Requests","pull-requests",2062,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-foundation-fabric","2026-07-31T06:23:36.935005",{"slug":495,"name":495,"fn":496,"description":497,"org":498,"tags":499,"stars":491,"repoUrl":492,"updatedAt":507},"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},[500,501,504],{"name":9,"slug":8,"type":16},{"name":502,"slug":503,"type":16},"Infrastructure as Code","infrastructure-as-code",{"name":505,"slug":506,"type":16},"Terraform","terraform","2026-07-12T07:38:23.514555",{"slug":509,"name":509,"fn":510,"description":511,"org":512,"tags":513,"stars":491,"repoUrl":492,"updatedAt":518},"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},[514,515],{"name":9,"slug":8,"type":16},{"name":516,"slug":517,"type":16},"Operations","operations","2026-07-12T07:38:28.127148",{"slug":520,"name":520,"fn":521,"description":522,"org":523,"tags":524,"stars":530,"repoUrl":531,"updatedAt":532},"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},[525,528,529],{"name":526,"slug":527,"type":16},"CLI","cli",{"name":482,"slug":483,"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":534,"name":534,"fn":535,"description":536,"org":537,"tags":538,"stars":530,"repoUrl":531,"updatedAt":549},"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},[539,542,543,546],{"name":540,"slug":541,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":544,"slug":545,"type":16},"LLM","llm",{"name":547,"slug":548,"type":16},"MCP","mcp","2026-07-12T07:39:10.911302",{"slug":551,"name":551,"fn":552,"description":553,"org":554,"tags":555,"stars":530,"repoUrl":531,"updatedAt":566},"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},[556,559,562,563],{"name":557,"slug":558,"type":16},"Audio","audio",{"name":560,"slug":561,"type":16},"Creative","creative",{"name":9,"slug":8,"type":16},{"name":564,"slug":565,"type":16},"Vertex AI","vertex-ai","2026-07-12T07:39:16.623879",{"slug":568,"name":568,"fn":569,"description":570,"org":571,"tags":572,"stars":530,"repoUrl":531,"updatedAt":579},"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},[573,574,575,578],{"name":560,"slug":561,"type":16},{"name":9,"slug":8,"type":16},{"name":576,"slug":577,"type":16},"Image Generation","image-generation",{"name":564,"slug":565,"type":16},"2026-07-12T07:39:15.372822",{"slug":581,"name":581,"fn":582,"description":583,"org":584,"tags":585,"stars":530,"repoUrl":531,"updatedAt":595},"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},[586,587,588,589,592],{"name":557,"slug":558,"type":16},{"name":560,"slug":561,"type":16},{"name":9,"slug":8,"type":16},{"name":590,"slug":591,"type":16},"Media","media",{"name":593,"slug":594,"type":16},"Video","video","2026-07-12T07:39:09.672849",{"slug":597,"name":597,"fn":598,"description":599,"org":600,"tags":601,"stars":530,"repoUrl":531,"updatedAt":605},"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},[602,603,604],{"name":560,"slug":561,"type":16},{"name":9,"slug":8,"type":16},{"name":593,"slug":594,"type":16},"2026-07-12T07:39:13.749081",{"slug":607,"name":607,"fn":608,"description":609,"org":610,"tags":611,"stars":530,"repoUrl":531,"updatedAt":623},"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},[612,613,614,617,620],{"name":557,"slug":558,"type":16},{"name":560,"slug":561,"type":16},{"name":615,"slug":616,"type":16},"Gemini","gemini",{"name":618,"slug":619,"type":16},"Speech","speech",{"name":621,"slug":622,"type":16},"Text-to-Speech","text-to-speech","2026-07-12T07:39:17.86673",80,{"items":626,"total":690},[627,639,651,663,674,684],{"slug":628,"name":628,"fn":629,"description":630,"org":631,"tags":632,"stars":23,"repoUrl":24,"updatedAt":638},"competitive-landscape-scan","scan competitive drug development landscapes","Build a competitor pipeline view for a target \u002F mechanism \u002F indication — who is in the clinic, what phase, what differentiation. Use for BD \u002F portfolio \u002F commercial-strategy questions like \"who else is developing X\" or \"what's the pipeline for indication Y\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[633,636,637],{"name":634,"slug":635,"type":16},"Competitive Intelligence","competitive-intelligence",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:55.676084",{"slug":640,"name":640,"fn":641,"description":642,"org":643,"tags":644,"stars":23,"repoUrl":24,"updatedAt":650},"drug-safety-signal-scan","scan drug safety signals","Surface emerging safety signals for a drug or class from PubMed (case reports \u002F letters \u002F RCT AE tables), plus relevant trial AE disclosures. Use for pharmacovigilance triage, medical-affairs safety updates, or competitive risk assessment. NOT a substitute for formal PV systems.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[645,646,649],{"name":18,"slug":19,"type":16},{"name":647,"slug":648,"type":16},"PubMed","pubmed",{"name":14,"slug":15,"type":16},"2026-07-12T07:40:57.110395",{"slug":652,"name":652,"fn":653,"description":654,"org":655,"tags":656,"stars":23,"repoUrl":24,"updatedAt":662},"mechanism-of-action-explainer","explain drug mechanisms of action","Produce a literature-grounded mechanism-of-action explanation for a drug or drug class — receptor \u002F pathway \u002F downstream effects \u002F clinical relevance — with a Nano Banana Pro diagram and citations. Use for medical-affairs MSL prep, internal training, or when the user asks \"how does drug X work\" or \"what's the MoA of class Y\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[657,660,661],{"name":658,"slug":659,"type":16},"Diagrams","diagrams",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:41:01.085638",{"slug":664,"name":664,"fn":665,"description":666,"org":667,"tags":668,"stars":23,"repoUrl":24,"updatedAt":673},"pico-search-strategy","translate research questions into PubMed search strategies","Translate a clinical or research question into a PICO\u002FPECO-structured PubMed + Europe PMC search strategy with MeSH terms, field tags, and search hedges. Use whenever the user asks a comparative-effectiveness, etiology, prognosis, diagnosis, or HEOR question, OR when they explicitly ask for a \"search strategy\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[669,670,671,672],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":647,"slug":648,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:59.811387",{"slug":675,"name":675,"fn":676,"description":677,"org":678,"tags":679,"stars":23,"repoUrl":24,"updatedAt":683},"prisma-systematic-review","conduct PRISMA systematic reviews","Run a PRISMA 2020-aligned systematic-review workflow — identification → screening → eligibility → included — with a transparent record of exclusions at each step and a rendered PRISMA flow diagram. Use when the user asks for a systematic review, evidence map, scoping review, or any task that requires a defensible screening audit trail.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[680,681,682],{"name":658,"slug":659,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:58.359064",{"slug":4,"name":4,"fn":5,"description":6,"org":685,"tags":686,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[687,688,689],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},6]