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Found 36 Skills
This skill should be used when the user wants to "run an evaluation", "evaluate my ADK agent", "write an evalset", "debug eval scores", "compare eval results", or needs guidance on ADK (Agent Development Kit) evaluation methodology and the eval-fix loop. Covers eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Part of the Google ADK (Agent Development Kit) skills suite. Do NOT use for API code patterns (use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
Large Language Model development, training, fine-tuning, and deployment best practices.
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Run the Codex Readiness integration test. Use when you need an end-to-end agentic loop with build/test scoring.
Use when discussing or working with DeepEval (the python AI evaluation framework)
MLflow ML lifecycle management. Use for ML experiment tracking.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Use when working on the Evaluator plugin CLI, jobs, SDK-backed specs, or plugin-owned Evaluator skills.
Use when user wants to find a note to publish as a blog post. Triggers on「选一篇笔记发博客」「note to blog」「写博客」「博客选题」. Scans Obsidian notes via Python script, evaluates blog-readiness, supports batch selection with fast/deep dual-track and parallel Agent dispatch.
Generate and curate evaluation datasets — structured generation via dimensions-tuples-NL, quick from description, expansion from existing data, plus dataset maintenance through deduplication, rebalancing, and gap-filling. Use when creating eval data, expanding test coverage, or cleaning datasets. Do NOT use when sufficient real production data exists (use analyze-trace-failures instead). Do NOT use for evaluator creation (use build-evaluator).
Complete reference for the Galileo AI platform TypeScript/JS SDK for evaluating, observing, and protecting GenAI applications. Use when building Node.js or TypeScript applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.