agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent
Claude Code
$ npx -y skills add davila7/claude-code-templates --skill agent-evaluation --agent claude-code
Codex
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a codex -y
Cursor
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a cursor -y
Gemini CLI
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a gemini-cli -y
GitHub Copilot
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a github-copilot -y
OpenCode
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a opencode -y
Windsurf
$ npx skills add davila7/claude-code-templates --skill agent-evaluation -a windsurf -y
name: agent-evaluation description: "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent." metadata: source: vibeship-spawner-skills (Apache 2.0)
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
You've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it
Run tests multiple times and analyze result distributions
Define and test agent behavioral invariants
Actively try to break agent behavior
| Issue | Severity | Solution | |-------|----------|----------| | Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation | | Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation | | Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming | | Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |
Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.