Loading...
Loading...
Found 5,058 Skills
ACSets (Attributed C-Sets) for categorical database design and DPO rewriting
Generate formatted changelogs from git history since the last release tag. Use when preparing release notes that categorize changes into breaking changes, features, fixes, and other sections.
Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.
Google BigQuery for analytics, ML, and data warehousing. Use for large-scale analytics.
Fiber Express-inspired Go web framework. Use for Go APIs.
Social feed with batch queries, cursor pagination, trending algorithms, and engagement tracking. Efficient database queries for infinite scroll feeds.
Use to structure collaborative success plans with milestones, KPIs, and governance.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
TanStack Query v5 expert guidance - migration gotchas (v4→v5 breaking changes), performance pitfalls (infinite refetch loops, staleness traps), and decision frameworks (when NOT to use queries, SWR vs React Query trade-offs). Use when: (1) debugging v4→v5 migration errors (gcTime, isPending, throwOnError), (2) infinite refetch loops, (3) SSR hydration mismatches, (4) choosing between React Query vs SWR vs fetch, (5) optimistic update patterns not working. NOT for basic setup (see official docs). Focuses on non-obvious decisions and patterns that cause production issues. Triggers: React Query, TanStack Query, v5 migration, refetch loop, stale data, SSR hydration, query invalidation, optimistic updates debugging.
Beta testing groups and tester management for Google Play closed testing tracks. Use when managing testers and beta groups.
Gemini CLI - Google's AI-powered command-line interface for building, debugging, and deploying with AI. Use when working with Gemini CLI configuration, commands, tools, extensions, hooks, skills, or MCP servers. Keywords: gemini-cli, google-ai, terminal, code-generation, workflow-automation, cli-commands, gemini-md, authentication, configuration, sandboxing, headless-mode, custom-commands, agent-skills, extensions, hooks, mcp-servers, file-system-tools, shell-commands, web-search, ide-integration.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.