[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-cloud-target-screening":3,"mdc-pj594g-key":36,"related-org-google-cloud-target-screening":231,"related-repo-google-cloud-target-screening":421},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":34,"mdContent":35},"target-screening","screen and rank acquisition targets","Methodology and ranked-shortlist output format for recommending acquisition targets: candidate generation, screening funnel, pillar scoring. Load for 'recommend targets' requests.",{"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},"Research","research","tag",{"name":18,"slug":19,"type":16},"Life Sciences","life-sciences",{"name":21,"slug":22,"type":16},"Investment Banking","investment-banking",{"name":24,"slug":25,"type":16},"Strategy","strategy",15,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FLifeSciences","2026-08-19T03:58:56.321085",null,10,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":29},[],"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002FLifeSciences\u002Ftree\u002FHEAD\u002Fapplications\u002Fpharma-on-gemini-enterprise\u002Fargus-on-gemini-enterprise\u002Fapp\u002Fskills\u002Ftarget-screening","---\nname: target-screening\ndescription: \"Methodology and ranked-shortlist output format for recommending acquisition targets: candidate generation, screening funnel, pillar scoring. Load for 'recommend targets' requests.\"\n---\n\n# Acquisition Target Screening Playbook\n\nMethodology for the \"recommend companies that might be good targets\" mode.\n\n## Inputs to elicit or infer\n\n- **Acquirer profile \u002F thesis**: therapeutic areas of interest, modality\n  preferences (small molecule, biologics, ADC, cell\u002Fgene therapy, RNA),\n  stage appetite (platform vs. de-risked late-stage vs. commercial),\n  approximate deal-size budget, and strategic gaps to fill.\n- If the user gives only a vague ask, state the assumptions you screen under.\n\n## Screening funnel\n\n1. **Generate candidate universe** — use Google Search for recent pipeline\n   news and analyst M&A speculation, and EDGAR full-text search to find\n   companies whose filings discuss the target technology\u002Findication.\n2. **First-pass filter** — modality\u002Findication fit, stage fit, and rough size\n   fit. Discard obvious mismatches; keep 8–15 names.\n3. **Score survivors** — for each, a lightweight version of the five diligence\n   pillars. Emphasize: strategic fit, catalyst timing, and (for public names)\n   cash runway as a negotiating-leverage \u002F urgency signal.\n4. **Rank** — produce a ranked shortlist with a fit score and a one-line\n   thesis per name.\n\n## Output format (screening mode)\n\nA ranked table:\n\n| Rank | Company | Ticker | Lead asset \u002F platform | Stage | Fit rationale | Key risk | Est. cash runway |\n|---|---|---|---|---|---|---|---|\n\nFollow with 2–4 sentences per top candidate expanding the thesis, and a note\non what deeper diligence (a full whitepaper) would resolve next.\n\n## Guardrails\n\n- Only recommend names you can support with at least one concrete source.\n- Distinguish public (screenable via EDGAR) from private (news)\n  candidates; flag data limitations for private ones.\n- Never present speculation as a confirmed deal rumor; attribute rumors.\n- Offer to produce a full whitepaper on any shortlisted name.\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,56,63,84,90,134,140,145,197,202,208],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"acquisition-target-screening-playbook",[47],{"type":48,"value":49},"text","Acquisition Target Screening Playbook",{"type":42,"tag":51,"props":52,"children":53},"p",{},[54],{"type":48,"value":55},"Methodology for the \"recommend companies that might be good targets\" mode.",{"type":42,"tag":57,"props":58,"children":60},"h2",{"id":59},"inputs-to-elicit-or-infer",[61],{"type":48,"value":62},"Inputs to elicit or infer",{"type":42,"tag":64,"props":65,"children":66},"ul",{},[67,79],{"type":42,"tag":68,"props":69,"children":70},"li",{},[71,77],{"type":42,"tag":72,"props":73,"children":74},"strong",{},[75],{"type":48,"value":76},"Acquirer profile \u002F thesis",{"type":48,"value":78},": therapeutic areas of interest, modality\npreferences (small molecule, biologics, ADC, cell\u002Fgene therapy, RNA),\nstage appetite (platform vs. de-risked late-stage vs. commercial),\napproximate deal-size budget, and strategic gaps to fill.",{"type":42,"tag":68,"props":80,"children":81},{},[82],{"type":48,"value":83},"If the user gives only a vague ask, state the assumptions you screen under.",{"type":42,"tag":57,"props":85,"children":87},{"id":86},"screening-funnel",[88],{"type":48,"value":89},"Screening funnel",{"type":42,"tag":91,"props":92,"children":93},"ol",{},[94,104,114,124],{"type":42,"tag":68,"props":95,"children":96},{},[97,102],{"type":42,"tag":72,"props":98,"children":99},{},[100],{"type":48,"value":101},"Generate candidate universe",{"type":48,"value":103}," — use Google Search for recent pipeline\nnews and analyst M&A speculation, and EDGAR full-text search to find\ncompanies whose filings discuss the target technology\u002Findication.",{"type":42,"tag":68,"props":105,"children":106},{},[107,112],{"type":42,"tag":72,"props":108,"children":109},{},[110],{"type":48,"value":111},"First-pass filter",{"type":48,"value":113}," — modality\u002Findication fit, stage fit, and rough size\nfit. Discard obvious mismatches; keep 8–15 names.",{"type":42,"tag":68,"props":115,"children":116},{},[117,122],{"type":42,"tag":72,"props":118,"children":119},{},[120],{"type":48,"value":121},"Score survivors",{"type":48,"value":123}," — for each, a lightweight version of the five diligence\npillars. Emphasize: strategic fit, catalyst timing, and (for public names)\ncash runway as a negotiating-leverage \u002F urgency signal.",{"type":42,"tag":68,"props":125,"children":126},{},[127,132],{"type":42,"tag":72,"props":128,"children":129},{},[130],{"type":48,"value":131},"Rank",{"type":48,"value":133}," — produce a ranked shortlist with a fit score and a one-line\nthesis per name.",{"type":42,"tag":57,"props":135,"children":137},{"id":136},"output-format-screening-mode",[138],{"type":48,"value":139},"Output format (screening mode)",{"type":42,"tag":51,"props":141,"children":142},{},[143],{"type":48,"value":144},"A ranked table:",{"type":42,"tag":146,"props":147,"children":148},"table",{},[149],{"type":42,"tag":150,"props":151,"children":152},"thead",{},[153],{"type":42,"tag":154,"props":155,"children":156},"tr",{},[157,162,167,172,177,182,187,192],{"type":42,"tag":158,"props":159,"children":160},"th",{},[161],{"type":48,"value":131},{"type":42,"tag":158,"props":163,"children":164},{},[165],{"type":48,"value":166},"Company",{"type":42,"tag":158,"props":168,"children":169},{},[170],{"type":48,"value":171},"Ticker",{"type":42,"tag":158,"props":173,"children":174},{},[175],{"type":48,"value":176},"Lead asset \u002F platform",{"type":42,"tag":158,"props":178,"children":179},{},[180],{"type":48,"value":181},"Stage",{"type":42,"tag":158,"props":183,"children":184},{},[185],{"type":48,"value":186},"Fit rationale",{"type":42,"tag":158,"props":188,"children":189},{},[190],{"type":48,"value":191},"Key risk",{"type":42,"tag":158,"props":193,"children":194},{},[195],{"type":48,"value":196},"Est. cash runway",{"type":42,"tag":51,"props":198,"children":199},{},[200],{"type":48,"value":201},"Follow with 2–4 sentences per top candidate expanding the thesis, and a note\non what deeper diligence (a full whitepaper) would resolve next.",{"type":42,"tag":57,"props":203,"children":205},{"id":204},"guardrails",[206],{"type":48,"value":207},"Guardrails",{"type":42,"tag":64,"props":209,"children":210},{},[211,216,221,226],{"type":42,"tag":68,"props":212,"children":213},{},[214],{"type":48,"value":215},"Only recommend names you can support with at least one concrete source.",{"type":42,"tag":68,"props":217,"children":218},{},[219],{"type":48,"value":220},"Distinguish public (screenable via EDGAR) from private (news)\ncandidates; flag data limitations for private ones.",{"type":42,"tag":68,"props":222,"children":223},{},[224],{"type":48,"value":225},"Never present speculation as a confirmed deal rumor; attribute rumors.",{"type":42,"tag":68,"props":227,"children":228},{},[229],{"type":48,"value":230},"Offer to produce a full whitepaper on any shortlisted name.",{"items":232,"total":420},[233,251,267,286,297,312,334,348,359,373,390,407],{"slug":234,"name":234,"fn":235,"description":236,"org":237,"tags":238,"stars":248,"repoUrl":249,"updatedAt":250},"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},[239,242,245],{"name":240,"slug":241,"type":16},"Documentation","documentation",{"name":243,"slug":244,"type":16},"Knowledge Base","knowledge-base",{"name":246,"slug":247,"type":16},"Search","search",8409,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fknowledge-catalog","2026-07-12T07:38:52.157375",{"slug":252,"name":253,"fn":254,"description":255,"org":256,"tags":257,"stars":248,"repoUrl":249,"updatedAt":266},"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},[258,261,262,265],{"name":259,"slug":260,"type":16},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":16},{"name":263,"slug":264,"type":16},"Knowledge Management","knowledge-management",{"name":246,"slug":247,"type":16},"2026-07-12T07:38:22.196851",{"slug":268,"name":268,"fn":269,"description":270,"org":271,"tags":272,"stars":283,"repoUrl":284,"updatedAt":285},"agent-eval","execute high-performance agent evaluations","Executes high-performance agent evaluations, multi-turn UserSim simulations, and declarative metric grading aligned with google\u002Fagents-cli and the Quality Flywheel. Publishes benchmark artifacts to the GCS Evaluation Registry, executes automated head-to-head delta comparisons (--compare-to), and optimizes system instructions via ADK GEPA (Genetic Evolutionary Prompt Optimization). Use when running agent benchmarks, evaluating ADK\u002FFastAPI agents, diagnosing loss clusters, comparing prompt iterations, running GEPA prompt optimization, or serving evaluation dashboards. Don't use for raw agent code scaffolding (use google-agents-cli-scaffold) or infrastructure deployment (use google-agents-cli-deploy).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[273,276,279,282],{"name":274,"slug":275,"type":16},"Agents","agents",{"name":277,"slug":278,"type":16},"Benchmarking","benchmarking",{"name":280,"slug":281,"type":16},"Evals","evals",{"name":9,"slug":8,"type":16},3059,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fprofessional-services","2026-08-13T05:04:15.77511",{"slug":287,"name":287,"fn":288,"description":289,"org":290,"tags":291,"stars":283,"repoUrl":284,"updatedAt":296},"agent-eval-workflow","design and run agent evaluation workflows","This skill should be used when the user wants to evaluate an AI agent end-to-end: scaffold an evaluation, design metrics that test a real hypothesis, make an agent measurable, audit generated eval config, read evaluation results, or run an improvement (\"hill climbing\") loop. Covers evaluation methodology, metric design, dataset coverage, reading deterministic vs LLM-judged metrics, and the traps that make eval runs silently measure nothing. Use alongside the tool-specific skills (agents-cli-eval, adk-eval-guide) — those cover commands and schemas, this covers the process and judgement.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[292,293,294,295],{"name":274,"slug":275,"type":16},{"name":277,"slug":278,"type":16},{"name":280,"slug":281,"type":16},{"name":9,"slug":8,"type":16},"2026-08-13T05:04:18.890378",{"slug":298,"name":298,"fn":299,"description":300,"org":301,"tags":302,"stars":283,"repoUrl":284,"updatedAt":311},"eval-breakdown","diagnose agent evaluation benchmark results","Performs an exhaustive, question-by-question narrative diagnostic breakdown of an agent-eval benchmark run by analyzing question_answer_log.md, eval_summary.json, and raw trajectory traces. Use when diagnosing low score causes, investigating the Memory Reuse vs. Traceability rubric clash, performing pre-release failure audits, or examining judge reasoning across individual scenarios. Don't use for running the benchmark CLI pipeline itself (use agent-eval) or automated genetic prompt tuning (use google-agents-cli-eval).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[303,306,309,310],{"name":304,"slug":305,"type":16},"Code Analysis","code-analysis",{"name":307,"slug":308,"type":16},"Debugging","debugging",{"name":280,"slug":281,"type":16},{"name":9,"slug":8,"type":16},"2026-08-13T05:04:18.336276",{"slug":313,"name":313,"fn":314,"description":315,"org":316,"tags":317,"stars":331,"repoUrl":332,"updatedAt":333},"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},[318,321,324,327,328],{"name":319,"slug":320,"type":16},"Automation","automation",{"name":322,"slug":323,"type":16},"Engineering","engineering",{"name":325,"slug":326,"type":16},"GitHub","github",{"name":9,"slug":8,"type":16},{"name":329,"slug":330,"type":16},"Pull Requests","pull-requests",2077,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fcloud-foundation-fabric","2026-07-31T06:23:36.935005",{"slug":335,"name":335,"fn":336,"description":337,"org":338,"tags":339,"stars":331,"repoUrl":332,"updatedAt":347},"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},[340,341,344],{"name":9,"slug":8,"type":16},{"name":342,"slug":343,"type":16},"Infrastructure as Code","infrastructure-as-code",{"name":345,"slug":346,"type":16},"Terraform","terraform","2026-08-10T04:16:46.817883",{"slug":349,"name":349,"fn":350,"description":351,"org":352,"tags":353,"stars":331,"repoUrl":332,"updatedAt":358},"fast-prerequisites","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},[354,355],{"name":9,"slug":8,"type":16},{"name":356,"slug":357,"type":16},"Operations","operations","2026-08-06T05:36:21.590622",{"slug":360,"name":360,"fn":361,"description":362,"org":363,"tags":364,"stars":370,"repoUrl":371,"updatedAt":372},"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},[365,368,369],{"name":366,"slug":367,"type":16},"CLI","cli",{"name":322,"slug":323,"type":16},{"name":9,"slug":8,"type":16},1178,"https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fvertex-ai-creative-studio","2026-07-12T07:39:08.41406",{"slug":374,"name":374,"fn":375,"description":376,"org":377,"tags":378,"stars":370,"repoUrl":371,"updatedAt":389},"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},[379,382,383,386],{"name":380,"slug":381,"type":16},"API Development","api-development",{"name":9,"slug":8,"type":16},{"name":384,"slug":385,"type":16},"LLM","llm",{"name":387,"slug":388,"type":16},"MCP","mcp","2026-07-12T07:39:10.911302",{"slug":391,"name":391,"fn":392,"description":393,"org":394,"tags":395,"stars":370,"repoUrl":371,"updatedAt":406},"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},[396,399,402,403],{"name":397,"slug":398,"type":16},"Audio","audio",{"name":400,"slug":401,"type":16},"Creative","creative",{"name":9,"slug":8,"type":16},{"name":404,"slug":405,"type":16},"Vertex AI","vertex-ai","2026-07-12T07:39:16.623879",{"slug":408,"name":408,"fn":409,"description":410,"org":411,"tags":412,"stars":370,"repoUrl":371,"updatedAt":419},"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},[413,414,415,418],{"name":400,"slug":401,"type":16},{"name":9,"slug":8,"type":16},{"name":416,"slug":417,"type":16},"Image Generation","image-generation",{"name":404,"slug":405,"type":16},"2026-07-12T07:39:15.372822",61,{"items":422,"total":510},[423,435,453,465,477,490,500],{"slug":424,"name":424,"fn":425,"description":426,"org":427,"tags":428,"stars":26,"repoUrl":27,"updatedAt":434},"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},[429,432,433],{"name":430,"slug":431,"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":436,"name":436,"fn":437,"description":438,"org":439,"tags":440,"stars":26,"repoUrl":27,"updatedAt":452},"diligence-playbook","perform life sciences M&A diligence","The five life-sciences M&A diligence pillars (scientific, clinical\u002Fregulatory, commercial, financial, deal\u002Frisk), the cash-runway calculation, deal-thesis archetypes, evidence standards, and the red-flag checklist. Load for any acquisition assessment.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[441,444,445,446,449],{"name":442,"slug":443,"type":16},"Finance","finance",{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":447,"slug":448,"type":16},"Regulatory Compliance","regulatory-compliance",{"name":450,"slug":451,"type":16},"Risk Assessment","risk-assessment","2026-08-19T03:58:59.400651",{"slug":454,"name":454,"fn":455,"description":456,"org":457,"tags":458,"stars":26,"repoUrl":27,"updatedAt":464},"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},[459,460,463],{"name":18,"slug":19,"type":16},{"name":461,"slug":462,"type":16},"PubMed","pubmed",{"name":14,"slug":15,"type":16},"2026-07-12T07:40:57.110395",{"slug":466,"name":466,"fn":467,"description":468,"org":469,"tags":470,"stars":26,"repoUrl":27,"updatedAt":476},"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},[471,474,475],{"name":472,"slug":473,"type":16},"Diagrams","diagrams",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:41:01.085638",{"slug":478,"name":478,"fn":479,"description":480,"org":481,"tags":482,"stars":26,"repoUrl":27,"updatedAt":489},"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},[483,486,487,488],{"name":484,"slug":485,"type":16},"Bioinformatics","bioinformatics",{"name":18,"slug":19,"type":16},{"name":461,"slug":462,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:59.811387",{"slug":491,"name":491,"fn":492,"description":493,"org":494,"tags":495,"stars":26,"repoUrl":27,"updatedAt":499},"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},[496,497,498],{"name":472,"slug":473,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:58.359064",{"slug":501,"name":501,"fn":502,"description":503,"org":504,"tags":505,"stars":26,"repoUrl":27,"updatedAt":509},"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},[506,507,508],{"name":484,"slug":485,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T07:40:53.048652",9]