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Found 1,480 Skills
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when you need to generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
API security checklist for reviewing endpoints before deployment. Use when creating or modifying API routes to ensure proper authentication, authorization, and input validation.
Enforce disciplined agent development workflows with plan-first development, small-slice execution, specialized self-review roles, quality gates, and project setup. Use when starting a new project, setting up development conventions, wanting structured planning, or needing the agent to follow best practices for code quality, review, and validation.
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.
Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK/IAM/quota error diagnosis, version management, rollback, and canary deployments. Triggers on: "deploy my agent", "agentcore deploy", "deploy failed", "CDK error", "rollback", "canary deploy", "pin version", "redeploy", "deploy stuck". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.
Expert at securing web applications against OWASP Top 10 vulnerabilities. Covers authentication, authorization, input validation, XSS prevention, CSRF protection, secure headers, and security testing. Treats security as a first-class requirement, not an afterthought. Use when "security, OWASP, XSS, CSRF, SQL injection, authentication security, authorization, input validation, secure headers, vulnerability, penetration testing, security, owasp, authentication, authorization, xss, csrf, injection, headers" mentioned.
ZeroBounce platform help — email validation, email finder, AI scoring, activity data, inbox placement testing, blacklist monitoring, DMARC, warmup. Use when your email list has too many bounces, catch-all addresses are hurting deliverability, you need to check if your IP is blacklisted, DMARC reports show unauthorized senders, or the ZeroBounce API isn't returning expected validation results. Do NOT use for general deliverability strategy (use /sales-deliverability), enrichment strategy (use /sales-enrich), or prospect list building strategy (use /sales-prospect-list).
Deliver Python backends across async FastAPI and Django or Flask service styles while keeping API design, validation, auth, and service behavior explicit.
Build interactive terminal forms and prompts in Go with huh - input, select, confirm, multiselect, validation, theming. Use when building Go terminal forms, huh, interactive Go prompts, or form fields with validation. NOT for shell script prompts (use gum).
Step-by-step process for adopting Cavekit on an existing codebase. Covers the 6-step brownfield process, bootstrap prompt design, spec validation against existing behavior, and the decision between brownfield adoption vs deliberate rewrite. Trigger phrases: "brownfield", "existing codebase", "add Cavekit to existing project", "adopt Cavekit", "layer kits on code", "retrofit kits"
Complete QA, playtesting, and code quality team for game development. Use when evaluating player experience, fun factor, game feel, code quality, test coverage, refactoring, performance testing, or build validation. Covers playtesting methodologies, UX research, unit/integration/e2e testing, code review, linting, accessibility, and continuous quality assurance. Triggers on requests for testing, QA, playtesting, code review, refactoring, bug tracking, or quality validation.