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Found 1,203 Skills
Claude Code skill that makes AI agents respond in caveman-speak, cutting ~65-75% of output tokens while preserving full technical accuracy
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
Run the Codex Readiness integration test. Use when you need an end-to-end agentic loop with build/test scoring.
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, observability, token economics, ROI frameworks, and build-vs-not decision guidance (modern best practices)
Creates minimal, effective AGENTS.md files using progressive disclosure. Triggers on "create agents.md", "refactor agents.md", "review my agents.md", "claude.md", or questions about agent configuration files. Also triggers proactively when a project is missing AGENTS.md.
Manage Draxarp Intelligence — projects, tasks, specs, docs, memories, sprints, knowledge graph, context captures, and task decomposition via orbit CLI
Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
Systematic debugging for ADK agents — trace reading, log analysis, common failure diagnosis, and the debug loop.
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).
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.