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Found 1,268 Skills
Use when building anything non-trivial. Enforces a spec → plan → execute → verify loop that prevents "looks right" failures. Creates spec.md, todo.md, and decisions.md before writing code.
Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
VCR.py HTTP recording for Python tests. Use when testing Python code making HTTP requests, recording API responses for replay, or creating deterministic tests for external services.
Convert PDF to clean Markdown with image content described as text. Use when user wants to convert a PDF to markdown, extract content from PDF, or prepare PDF content for AI tools.
Specialized AI assistant for DSPy development with deep knowledge of predictors, optimizers, adapters, and GEPA integration. Provides session management, codebase indexing, and command-based workflows.
Plain text formatting that's readable raw AND rendered
Run application agents through SpendGuard with strict hard budget caps. Use when setting up `spendguard-sidecar`, creating agent IDs, setting or topping budgets, sending OpenAI/Grok/Gemini/Anthropic calls through SpendGuard endpoints, and troubleshooting budget enforcement errors like insufficient budget, in-flight lock conflicts, missing `x-cynsta-agent-id`, or remote pricing signature failures.
Adversarial code review using the opposite model. Spawns 1–3 reviewers on the opposing model (Claude spawns Codex, Codex spawns Claude) to challenge work from distinct critical lenses. Triggers: "adversarial review".
OpenRouter AI integration — list available models, get integration code examples for different environments, and send prompts to any OpenRouter-compatible model. Requires OPENROUTER_API_KEY env var for chat operations.
Analyse agent execution to find wasted tool calls, wrong turns, and blind alleys. Optimise agents to reach their goal in the fewest turns, tokens, and least time. Recommend harness/model changes — never apply without user approval.
Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.
Invoke orq.ai deployments, agents, and models via the Python SDK or HTTP API. Use when a user wants to call a deployment with prompt variables, invoke an agent in a conversation, or call a model directly through the AI Router. Do NOT use for creating or editing deployments/agents (use optimize-prompt or build-agent). Do NOT use for running evaluations (use run-experiment).