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Found 1,564 Skills
Query Google Gemini 3 Pro via grsai.com API for text generation and image analysis. Use for text generation, Q&A, summarization, code generation, creative writing, image analysis/vision, complex reasoning, and structured document generation. Triggers on "ask gemini", "use gemini", "query gemini", "analyze this image with gemini", or when a second opinion from another LLM is needed. Optionally accepts an image input for vision tasks.
MixSeek-Coreで利用可能なLLMモデルの一覧を表示します。「使えるモデル」「モデル一覧」「どのモデルがある」「モデルを取得」「APIからモデル」といった依頼で使用してください。API経由でプロバイダー別のモデル情報を動的取得し、推奨設定、互換性情報を提供します。
Diseño de prompts para LLMs: system prompts, few-shot examples, chain-of-thought, RAG, structured outputs.
Real-time investment context from Primary Logic — LLM-ranked relevance and impact signals from podcasts, articles, X/Twitter, Kalshi, Polymarket, earnings calls, filings, and other monitored sources across public and private companies.
Edit prose to sound more natural, direct, and engaging. Works top-down through four levels (Document → Paragraph → Sentence → Word) with human checkpoints at each stage. Fixes LLM patterns, writerly bad habits, and style deficits. Works for academic papers, reports, memos, essays, blog posts, proposals, and other nonfiction. Use when prose sounds robotic, dull, or inaccessible.
MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold).
Systematic LLM prompt engineering: analyzes existing prompts for failure modes, generates structured variants (direct, few-shot, chain-of-thought), designs evaluation rubrics with weighted criteria, and produces test case suites for comparing prompt performance. Triggers on: "prompt engineering", "prompt lab", "generate prompt variants", "A/B test prompts", "evaluate prompt", "optimize prompt", "write a better prompt", "prompt design", "prompt iteration", "few-shot examples", "chain-of-thought prompt", "prompt failure modes", "improve this prompt". Use this skill when designing, improving, or evaluating LLM prompts specifically. NOT for evaluating Claude Code skills or SKILL.md files — use skill-evaluator instead.
Build AI-powered chat applications with TanStack AI and React. Use when working with @tanstack/ai, @tanstack/ai-react, @tanstack/ai-client, or any TanStack AI packages. Covers useChat hook, streaming, tools (server/client/hybrid), tool approval, structured outputs, multimodal content, adapters (OpenAI, Anthropic, Gemini, Ollama, Grok), agentic cycles, devtools, and type safety patterns. Triggers on AI chat UI, function calling, LLM integration, or streaming response tasks using TanStack AI.
Ultra-lightweight AI assistant in Go that runs on $10 hardware with <10MB RAM, supporting multiple LLM providers, tools, and single-binary deployment across RISC-V, ARM, MIPS, and x86.
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.
Adds OpenTelemetry-based tracing to applications via TrueFoundry's tracing platform (Traceloop SDK). Creates tracing projects, instruments Python/TypeScript code, and captures LLM calls and custom spans.