[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-cloud-mechanism-of-action-explainer":3,"mdc--sk9qx1-key":33,"related-org-google-cloud-mechanism-of-action-explainer":424,"related-repo-google-cloud-mechanism-of-action-explainer":613},{"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},"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},"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},"Diagrams","diagrams",14,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FLifeSciences","2026-07-12T07:41:01.085638",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\u002Fmechanism-of-action-explainer","---\nname: mechanism-of-action-explainer\ndescription: 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\".\n---\n\n# Mechanism-of-Action Explainer\n\nYou are preparing a Medical Science Liaison (MSL) -grade mechanism-of-action\nbrief on a drug, target, or modality. The audience is a clinician or\ninternal scientist who needs scientifically accurate explanation plus a\ndiagram they can drop into a slide.\n\n## Workflow\n\n### 1. Identify the MoA scaffold\n\nCall `lookup_entity_id` with `concept=\"chemical\"` for the drug. If the\nuser named a class (e.g. \"GLP-1 receptor agonists\"), call it for the\ncanonical class member or treat the class as the subject directly.\n\nIdentify, from the literature:\n\n- **Primary target(s)** — the receptor, enzyme, channel, or protein the\n  drug binds. Use `find_related_entities` with `relation_type=\"inhibit\"` \u002F\n  `\"stimulate\"` \u002F `\"interact\"` and `target_type=\"gene\"`.\n- **Binding mode** — agonist \u002F antagonist \u002F inverse agonist \u002F allosteric \u002F\n  covalent \u002F PROTAC \u002F antibody-drug-conjugate \u002F oligonucleotide.\n- **Downstream signaling** — the immediate post-receptor cascade.\n- **Tissue \u002F cellular localization** — where the target is expressed\n  matters as much as the molecular effect.\n\n### 2. Map signaling to clinical effect\n\nFor each clinical effect the drug is approved or studied for, trace the\nchain from molecular interaction → cellular consequence → tissue-level\nchange → clinical phenotype. Use `search_pubmed` with query like\n`\"\u003Cdrug> AND mechanism AND \u003Cindication>\"` filtered to reviews\n(`publication_types=[\"review\"]`) for the canonical chain.\n\nNote dose-response and time-course where relevant — pharma audiences care\nwhether the effect is sustained, transient, or accumulative.\n\n### 3. Surface the differentiating biology\n\nFor an MSL brief, the most valuable content is what makes this drug or\nclass *different* from competitors:\n\n- Selectivity profile vs. related targets (e.g. SGLT2 vs. SGLT1).\n- Tissue restriction (e.g. peripheral-only vs. CNS-penetrant).\n- Off-target activities that drive class-effect AEs.\n- Pharmacokinetic differentiators that interact with the MoA (half-life,\n  tissue distribution, active metabolites).\n\nCite the canonical reviews and key primary papers with PMIDs.\n\n### 4. Render the MoA diagram\n\nCall `visualize_concept` with `figure_type=\"diagram\"`. Build the\ndescription with:\n\n- The drug\u002Fmolecule shape on one side, the target on the other.\n- Arrows showing the primary interaction with a binding-mode label.\n- The downstream signaling cascade as a vertical or horizontal flow.\n- The cellular\u002Ftissue context (membrane, organelle, organ) as the\n  background frame.\n- Every label spelled exactly as you want it rendered (Nano Banana Pro\n  has industry-leading text rendering but you must dictate spelling\n  verbatim — gene symbols are case-sensitive).\n\nPass `aspect_ratio=\"16:9\"` for slide use, `\"4:3\"` for poster use.\n\n### 5. Output\n\nFinal response structure:\n\n- One-paragraph plain-language MoA summary (the line you'd open an MSL\n  conversation with).\n- \"How it works\" — mechanism walk-through with citations.\n- \"What makes it different\" — competitor \u002F class context.\n- The rendered figure marker (pass through the `\u003Cstart_of_user_uploaded_file: ...>`\n  string verbatim so the image renders inline).\n- \"Key references\" — 5-8 PMIDs for the most cite-worthy mechanism papers.\n\n## Guardrails\n\n- Distinguish *approved indications* from *investigational uses*. Cite the\n  FDA \u002F EMA label section for approved claims and the trial NCT for\n  investigational claims.\n- Off-label or pre-clinical mechanisms must be clearly tagged as such.\n- If the literature contradicts itself (e.g. multiple proposed mechanisms\n  for an effect), present both with PMIDs rather than choosing.\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,46,52,59,66,88,93,178,184,213,218,224,237,260,265,271,290,318,339,345,350,386,392],{"type":39,"tag":40,"props":41,"children":42},"element","h1",{"id":4},[43],{"type":44,"value":45},"text","Mechanism-of-Action Explainer",{"type":39,"tag":47,"props":48,"children":49},"p",{},[50],{"type":44,"value":51},"You are preparing a Medical Science Liaison (MSL) -grade mechanism-of-action\nbrief on a drug, target, or modality. The audience is a clinician or\ninternal scientist who needs scientifically accurate explanation plus a\ndiagram they can drop into a slide.",{"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-moa-scaffold",[64],{"type":44,"value":65},"1. Identify the MoA scaffold",{"type":39,"tag":47,"props":67,"children":68},{},[69,71,78,80,86],{"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=\"chemical\"",{"type":44,"value":87}," for the drug. If the\nuser named a class (e.g. \"GLP-1 receptor agonists\"), call it for the\ncanonical class member or treat the class as the subject directly.",{"type":39,"tag":47,"props":89,"children":90},{},[91],{"type":44,"value":92},"Identify, from the literature:",{"type":39,"tag":94,"props":95,"children":96},"ul",{},[97,148,158,168],{"type":39,"tag":98,"props":99,"children":100},"li",{},[101,107,109,115,116,122,124,130,132,138,140,146],{"type":39,"tag":102,"props":103,"children":104},"strong",{},[105],{"type":44,"value":106},"Primary target(s)",{"type":44,"value":108}," — the receptor, enzyme, channel, or protein the\ndrug binds. Use ",{"type":39,"tag":72,"props":110,"children":112},{"className":111},[],[113],{"type":44,"value":114},"find_related_entities",{"type":44,"value":79},{"type":39,"tag":72,"props":117,"children":119},{"className":118},[],[120],{"type":44,"value":121},"relation_type=\"inhibit\"",{"type":44,"value":123}," \u002F\n",{"type":39,"tag":72,"props":125,"children":127},{"className":126},[],[128],{"type":44,"value":129},"\"stimulate\"",{"type":44,"value":131}," \u002F ",{"type":39,"tag":72,"props":133,"children":135},{"className":134},[],[136],{"type":44,"value":137},"\"interact\"",{"type":44,"value":139}," and ",{"type":39,"tag":72,"props":141,"children":143},{"className":142},[],[144],{"type":44,"value":145},"target_type=\"gene\"",{"type":44,"value":147},".",{"type":39,"tag":98,"props":149,"children":150},{},[151,156],{"type":39,"tag":102,"props":152,"children":153},{},[154],{"type":44,"value":155},"Binding mode",{"type":44,"value":157}," — agonist \u002F antagonist \u002F inverse agonist \u002F allosteric \u002F\ncovalent \u002F PROTAC \u002F antibody-drug-conjugate \u002F oligonucleotide.",{"type":39,"tag":98,"props":159,"children":160},{},[161,166],{"type":39,"tag":102,"props":162,"children":163},{},[164],{"type":44,"value":165},"Downstream signaling",{"type":44,"value":167}," — the immediate post-receptor cascade.",{"type":39,"tag":98,"props":169,"children":170},{},[171,176],{"type":39,"tag":102,"props":172,"children":173},{},[174],{"type":44,"value":175},"Tissue \u002F cellular localization",{"type":44,"value":177}," — where the target is expressed\nmatters as much as the molecular effect.",{"type":39,"tag":60,"props":179,"children":181},{"id":180},"_2-map-signaling-to-clinical-effect",[182],{"type":44,"value":183},"2. Map signaling to clinical effect",{"type":39,"tag":47,"props":185,"children":186},{},[187,189,195,197,203,205,211],{"type":44,"value":188},"For each clinical effect the drug is approved or studied for, trace the\nchain from molecular interaction → cellular consequence → tissue-level\nchange → clinical phenotype. Use ",{"type":39,"tag":72,"props":190,"children":192},{"className":191},[],[193],{"type":44,"value":194},"search_pubmed",{"type":44,"value":196}," with query like\n",{"type":39,"tag":72,"props":198,"children":200},{"className":199},[],[201],{"type":44,"value":202},"\"\u003Cdrug> AND mechanism AND \u003Cindication>\"",{"type":44,"value":204}," filtered to reviews\n(",{"type":39,"tag":72,"props":206,"children":208},{"className":207},[],[209],{"type":44,"value":210},"publication_types=[\"review\"]",{"type":44,"value":212},") for the canonical chain.",{"type":39,"tag":47,"props":214,"children":215},{},[216],{"type":44,"value":217},"Note dose-response and time-course where relevant — pharma audiences care\nwhether the effect is sustained, transient, or accumulative.",{"type":39,"tag":60,"props":219,"children":221},{"id":220},"_3-surface-the-differentiating-biology",[222],{"type":44,"value":223},"3. Surface the differentiating biology",{"type":39,"tag":47,"props":225,"children":226},{},[227,229,235],{"type":44,"value":228},"For an MSL brief, the most valuable content is what makes this drug or\nclass ",{"type":39,"tag":230,"props":231,"children":232},"em",{},[233],{"type":44,"value":234},"different",{"type":44,"value":236}," from competitors:",{"type":39,"tag":94,"props":238,"children":239},{},[240,245,250,255],{"type":39,"tag":98,"props":241,"children":242},{},[243],{"type":44,"value":244},"Selectivity profile vs. related targets (e.g. SGLT2 vs. SGLT1).",{"type":39,"tag":98,"props":246,"children":247},{},[248],{"type":44,"value":249},"Tissue restriction (e.g. peripheral-only vs. CNS-penetrant).",{"type":39,"tag":98,"props":251,"children":252},{},[253],{"type":44,"value":254},"Off-target activities that drive class-effect AEs.",{"type":39,"tag":98,"props":256,"children":257},{},[258],{"type":44,"value":259},"Pharmacokinetic differentiators that interact with the MoA (half-life,\ntissue distribution, active metabolites).",{"type":39,"tag":47,"props":261,"children":262},{},[263],{"type":44,"value":264},"Cite the canonical reviews and key primary papers with PMIDs.",{"type":39,"tag":60,"props":266,"children":268},{"id":267},"_4-render-the-moa-diagram",[269],{"type":44,"value":270},"4. Render the MoA diagram",{"type":39,"tag":47,"props":272,"children":273},{},[274,275,281,282,288],{"type":44,"value":70},{"type":39,"tag":72,"props":276,"children":278},{"className":277},[],[279],{"type":44,"value":280},"visualize_concept",{"type":44,"value":79},{"type":39,"tag":72,"props":283,"children":285},{"className":284},[],[286],{"type":44,"value":287},"figure_type=\"diagram\"",{"type":44,"value":289},". Build the\ndescription with:",{"type":39,"tag":94,"props":291,"children":292},{},[293,298,303,308,313],{"type":39,"tag":98,"props":294,"children":295},{},[296],{"type":44,"value":297},"The drug\u002Fmolecule shape on one side, the target on the other.",{"type":39,"tag":98,"props":299,"children":300},{},[301],{"type":44,"value":302},"Arrows showing the primary interaction with a binding-mode label.",{"type":39,"tag":98,"props":304,"children":305},{},[306],{"type":44,"value":307},"The downstream signaling cascade as a vertical or horizontal flow.",{"type":39,"tag":98,"props":309,"children":310},{},[311],{"type":44,"value":312},"The cellular\u002Ftissue context (membrane, organelle, organ) as the\nbackground frame.",{"type":39,"tag":98,"props":314,"children":315},{},[316],{"type":44,"value":317},"Every label spelled exactly as you want it rendered (Nano Banana Pro\nhas industry-leading text rendering but you must dictate spelling\nverbatim — gene symbols are case-sensitive).",{"type":39,"tag":47,"props":319,"children":320},{},[321,323,329,331,337],{"type":44,"value":322},"Pass ",{"type":39,"tag":72,"props":324,"children":326},{"className":325},[],[327],{"type":44,"value":328},"aspect_ratio=\"16:9\"",{"type":44,"value":330}," for slide use, ",{"type":39,"tag":72,"props":332,"children":334},{"className":333},[],[335],{"type":44,"value":336},"\"4:3\"",{"type":44,"value":338}," for poster use.",{"type":39,"tag":60,"props":340,"children":342},{"id":341},"_5-output",[343],{"type":44,"value":344},"5. Output",{"type":39,"tag":47,"props":346,"children":347},{},[348],{"type":44,"value":349},"Final response structure:",{"type":39,"tag":94,"props":351,"children":352},{},[353,358,363,368,381],{"type":39,"tag":98,"props":354,"children":355},{},[356],{"type":44,"value":357},"One-paragraph plain-language MoA summary (the line you'd open an MSL\nconversation with).",{"type":39,"tag":98,"props":359,"children":360},{},[361],{"type":44,"value":362},"\"How it works\" — mechanism walk-through with citations.",{"type":39,"tag":98,"props":364,"children":365},{},[366],{"type":44,"value":367},"\"What makes it different\" — competitor \u002F class context.",{"type":39,"tag":98,"props":369,"children":370},{},[371,373,379],{"type":44,"value":372},"The rendered figure marker (pass through the ",{"type":39,"tag":72,"props":374,"children":376},{"className":375},[],[377],{"type":44,"value":378},"\u003Cstart_of_user_uploaded_file: ...>",{"type":44,"value":380},"\nstring verbatim so the image renders inline).",{"type":39,"tag":98,"props":382,"children":383},{},[384],{"type":44,"value":385},"\"Key references\" — 5-8 PMIDs for the most cite-worthy mechanism papers.",{"type":39,"tag":53,"props":387,"children":389},{"id":388},"guardrails",[390],{"type":44,"value":391},"Guardrails",{"type":39,"tag":94,"props":393,"children":394},{},[395,414,419],{"type":39,"tag":98,"props":396,"children":397},{},[398,400,405,407,412],{"type":44,"value":399},"Distinguish ",{"type":39,"tag":230,"props":401,"children":402},{},[403],{"type":44,"value":404},"approved indications",{"type":44,"value":406}," from ",{"type":39,"tag":230,"props":408,"children":409},{},[410],{"type":44,"value":411},"investigational uses",{"type":44,"value":413},". Cite the\nFDA \u002F EMA label section for approved claims and the trial NCT for\ninvestigational claims.",{"type":39,"tag":98,"props":415,"children":416},{},[417],{"type":44,"value":418},"Off-label or pre-clinical mechanisms must be clearly tagged as such.",{"type":39,"tag":98,"props":420,"children":421},{},[422],{"type":44,"value":423},"If the literature contradicts itself (e.g. multiple proposed mechanisms\nfor an effect), present both with PMIDs rather than choosing.",{"items":425,"total":612},[426,444,460,482,496,507,521,538,555,568,584,594],{"slug":427,"name":427,"fn":428,"description":429,"org":430,"tags":431,"stars":441,"repoUrl":442,"updatedAt":443},"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},[432,435,438],{"name":433,"slug":434,"type":16},"Documentation","documentation",{"name":436,"slug":437,"type":16},"Knowledge Base","knowledge-base",{"name":439,"slug":440,"type":16},"Search","search",6749,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fknowledge-catalog","2026-07-12T07:38:52.157375",{"slug":445,"name":446,"fn":447,"description":448,"org":449,"tags":450,"stars":441,"repoUrl":442,"updatedAt":459},"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},[451,454,455,458],{"name":452,"slug":453,"type":16},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":16},{"name":456,"slug":457,"type":16},"Knowledge Management","knowledge-management",{"name":439,"slug":440,"type":16},"2026-07-12T07:38:22.196851",{"slug":461,"name":461,"fn":462,"description":463,"org":464,"tags":465,"stars":479,"repoUrl":480,"updatedAt":481},"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},[466,469,472,475,476],{"name":467,"slug":468,"type":16},"Automation","automation",{"name":470,"slug":471,"type":16},"Engineering","engineering",{"name":473,"slug":474,"type":16},"GitHub","github",{"name":9,"slug":8,"type":16},{"name":477,"slug":478,"type":16},"Pull Requests","pull-requests",2062,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-foundation-fabric","2026-07-31T06:23:36.935005",{"slug":483,"name":483,"fn":484,"description":485,"org":486,"tags":487,"stars":479,"repoUrl":480,"updatedAt":495},"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},[488,489,492],{"name":9,"slug":8,"type":16},{"name":490,"slug":491,"type":16},"Infrastructure as Code","infrastructure-as-code",{"name":493,"slug":494,"type":16},"Terraform","terraform","2026-07-12T07:38:23.514555",{"slug":497,"name":497,"fn":498,"description":499,"org":500,"tags":501,"stars":479,"repoUrl":480,"updatedAt":506},"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},[502,503],{"name":9,"slug":8,"type":16},{"name":504,"slug":505,"type":16},"Operations","operations","2026-07-12T07:38:28.127148",{"slug":508,"name":508,"fn":509,"description":510,"org":511,"tags":512,"stars":518,"repoUrl":519,"updatedAt":520},"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},[513,516,517],{"name":514,"slug":515,"type":16},"CLI","cli",{"name":470,"slug":471,"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":522,"name":522,"fn":523,"description":524,"org":525,"tags":526,"stars":518,"repoUrl":519,"updatedAt":537},"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},[527,530,531,534],{"name":528,"slug":529,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":532,"slug":533,"type":16},"LLM","llm",{"name":535,"slug":536,"type":16},"MCP","mcp","2026-07-12T07:39:10.911302",{"slug":539,"name":539,"fn":540,"description":541,"org":542,"tags":543,"stars":518,"repoUrl":519,"updatedAt":554},"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},[544,547,550,551],{"name":545,"slug":546,"type":16},"Audio","audio",{"name":548,"slug":549,"type":16},"Creative","creative",{"name":9,"slug":8,"type":16},{"name":552,"slug":553,"type":16},"Vertex AI","vertex-ai","2026-07-12T07:39:16.623879",{"slug":556,"name":556,"fn":557,"description":558,"org":559,"tags":560,"stars":518,"repoUrl":519,"updatedAt":567},"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},[561,562,563,566],{"name":548,"slug":549,"type":16},{"name":9,"slug":8,"type":16},{"name":564,"slug":565,"type":16},"Image Generation","image-generation",{"name":552,"slug":553,"type":16},"2026-07-12T07:39:15.372822",{"slug":569,"name":569,"fn":570,"description":571,"org":572,"tags":573,"stars":518,"repoUrl":519,"updatedAt":583},"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},[574,575,576,577,580],{"name":545,"slug":546,"type":16},{"name":548,"slug":549,"type":16},{"name":9,"slug":8,"type":16},{"name":578,"slug":579,"type":16},"Media","media",{"name":581,"slug":582,"type":16},"Video","video","2026-07-12T07:39:09.672849",{"slug":585,"name":585,"fn":586,"description":587,"org":588,"tags":589,"stars":518,"repoUrl":519,"updatedAt":593},"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},[590,591,592],{"name":548,"slug":549,"type":16},{"name":9,"slug":8,"type":16},{"name":581,"slug":582,"type":16},"2026-07-12T07:39:13.749081",{"slug":595,"name":595,"fn":596,"description":597,"org":598,"tags":599,"stars":518,"repoUrl":519,"updatedAt":611},"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},[600,601,602,605,608],{"name":545,"slug":546,"type":16},{"name":548,"slug":549,"type":16},{"name":603,"slug":604,"type":16},"Gemini","gemini",{"name":606,"slug":607,"type":16},"Speech","speech",{"name":609,"slug":610,"type":16},"Text-to-Speech","text-to-speech","2026-07-12T07:39:17.86673",80,{"items":614,"total":678},[615,627,639,645,658,668],{"slug":616,"name":616,"fn":617,"description":618,"org":619,"tags":620,"stars":23,"repoUrl":24,"updatedAt":626},"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},[621,624,625],{"name":622,"slug":623,"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":628,"name":628,"fn":629,"description":630,"org":631,"tags":632,"stars":23,"repoUrl":24,"updatedAt":638},"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},[633,634,637],{"name":18,"slug":19,"type":16},{"name":635,"slug":636,"type":16},"PubMed","pubmed",{"name":14,"slug":15,"type":16},"2026-07-12T07:40:57.110395",{"slug":4,"name":4,"fn":5,"description":6,"org":640,"tags":641,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[642,643,644],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":646,"name":646,"fn":647,"description":648,"org":649,"tags":650,"stars":23,"repoUrl":24,"updatedAt":657},"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},[651,654,655,656],{"name":652,"slug":653,"type":16},"Bioinformatics","bioinformatics",{"name":18,"slug":19,"type":16},{"name":635,"slug":636,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:59.811387",{"slug":659,"name":659,"fn":660,"description":661,"org":662,"tags":663,"stars":23,"repoUrl":24,"updatedAt":667},"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},[664,665,666],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:58.359064",{"slug":669,"name":669,"fn":670,"description":671,"org":672,"tags":673,"stars":23,"repoUrl":24,"updatedAt":677},"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},[674,675,676],{"name":652,"slug":653,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:53.048652",6]