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Found 566 Skills
Query Pollinations text API with web-search models (gemini-search, perplexity-fast, nomnom, etc.). Use when you need web search grounded answers via Pollinations.
Sync a commit from the gemini remote into sync-upstream for empower-site.
Actionable design system complementing official frontend-design plugin. The plugin provides philosophy; this skill provides executable patterns. Invoke when: - Building web components, pages, or applications - Designing UI that needs to stand out (not generic) - Implementing visual polish and micro-interactions - Choosing typography, color palettes, or spatial layouts CRITICAL: Avoid "AI slop" aesthetics. Make creative, unexpected choices. MANDATORY: Consult Gemini before any frontend work. See also: references/dna-codes.md, references/banned-patterns.md
Access Google's developer documentation programmatically using the Developer Knowledge REST API. Use when you need to search, retrieve, or reference official Google documentation (Firebase, Android, Cloud, Gemini, etc.) via direct HTTP calls instead of MCP.
Synthesize outputs from multiple AI models into a comprehensive, verified assessment. Use when: (1) User pastes feedback/analysis from multiple LLMs (Claude, GPT, Gemini, etc.) about code or a project, (2) User wants to consolidate model outputs into a single reliable document, (3) User needs conflicting model claims resolved against actual source code. This skill verifies model claims against the codebase, resolves contradictions with evidence, and produces a more reliable assessment than any single model.
Add new LLM model pricing entries to Langfuse's default-model-prices.json. Use when adding model prices, updating model pricing, creating model entries, adding Claude/OpenAI/Anthropic/Google/Gemini/AWS Bedrock/Azure/Vertex AI model pricing, working with matchPattern regex, pricingTiers, or model cost configuration. Covers model price JSON structure, regex patterns for multi-provider matching, tiered pricing with conditions, cache pricing, and validation rules.
Worker that runs parallel external agent reviews (Codex + Gemini) on code changes. Background tasks, process-as-arrive, critical verification with debate. Returns filtered suggestions with confidence scoring.
Respond to PR review comments with critical evaluation. Use when addressing code review feedback, responding to bot review comments (Gemini Code Assist, CodeRabbit, etc.), or handling PR suggestions. Fetches comments, evaluates each against project context, applies valid fixes, declines invalid suggestions with reasoning, and posts responses.
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
Universal context reviewer: delegates arbitrary context (plans, decisions, documents, architecture proposals) to external agents (Codex + Gemini) for independent review with debate protocol. Context always passed via files.
Adapter boundary rules for plugin integrations. Trigger: Changes in plugin scripts/hooks for Claude, OpenCode, Gemini, or Codex.
Repository packaging for AI/LLM analysis. Capabilities: pack repos into single files, generate AI-friendly context, codebase snapshots, security audit prep, filter/exclude patterns, token counting, multiple output formats. Actions: pack, generate, export, analyze repositories for LLMs. Keywords: Repomix, repository packaging, LLM context, AI analysis, codebase snapshot, Claude context, ChatGPT context, Gemini context, code packaging, token count, file filtering, security audit, third-party library analysis, context window, single file output. Use when: packaging codebases for AI, generating LLM context, creating codebase snapshots, analyzing third-party libraries, preparing security audits, feeding repos to Claude/ChatGPT/Gemini.