Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when creating, modifying, or reviewing XTuner RL judgers under xtuner/v1/rl/judger or configs that build custom judgers. Enforces the preferred Judger payload contract and the BaseJudger/ComposedJudger constraints.
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management", or needs ADK (Agent Development Kit) Python API patterns and code examples. Part of the Google ADK skills suite. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, and state management. Do NOT use for creating new projects (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
Switch a Worker Agent's LLM model via hiclaw CLI. Use when the human admin requests changing a Worker's model.
Use when a QwenPaw-backed Worker needs to decide whether to do work directly, use native subagents for internal parallelism, or create a temporary QwenPaw agent with a custom AGENTS.md and skills.
Audit and update rorkai/app-store-connect-cli-skills against the current ASC CLI surface. Use when the user asks whether ASC agent skills need updates, requests a post-release skills sync, or wants command, flag, output, auth, or workflow examples checked for drift.
Implements end-to-end plugin changes spanning router config, post-decision processing, optional CLI/UI exposure, and E2E test coverage. Use when adding a new plugin type, changing plugin config schema or execution semantics, updating plugin chain behavior, or modifying plugin-exposed metadata across surfaces.
Modifies routing policy after signal extraction, including matched-decision logic, candidate-model selection, and downstream looper behavior. Use when changing decision predicates, thresholds, priorities, model ranking, cost or latency routing, or other post-signal routing policy.
Modifies training-stack workflows, selector or embedding pipelines, evaluation artifacts, or runtime-facing outputs under src/training. Use when changing model selection training, embedding pipelines, evaluation scripts, experiments, or other training outputs that feed runtime behavior.
Bridges native skill discovery into the vLLM Semantic Router repository harness, routing tasks through the canonical agent-report flow, repo-local skill registry, and validation commands. Use when starting any task inside the vLLM Semantic Router repository to resolve the correct primary skill, read canonical docs, and run harness validation.