Run evaluations for one, multiple, or all skills using the agent orchestration framework. Make sure to use this skill whenever the user asks to run evals, test a skill's performance, run benchmarks, or compare baseline versus with-skill execution.
npx skills add https://github.com/flutter/packages --skill run-evals
<target-package-root>/evals/README.md for understanding the difference between per-skill evals and cross-skill evals (where <target-package-root> is the directory containing the .agents or skills folder).evals/evals.json files inside .agents/skills/ and/or skills/. For cross-skill evaluations, look for *_evals.json files directly in <target-package-root>/evals/.agent_config field in the target target JSON file to determine the environment/harness to spawn. If agent_config is "bare-agent", spawn a subagent with the bare-agent profile. If it is a specific contributor profile (e.g. "reidbaker-agent"), use that profile to provide the necessary contributor context.Workspace: branch and the identified agent_config.resources/with_skill_execution_prompt.md for the template. When filling in <path-to-skill>, you MUST use a relative path from the repository root, not an absolute path. If you are running a cross-skill evaluation, fill in <path-to-skill> with "none (cross-skill meta-eval)". Also, replace <target-package-root> with the actual directory path in both templates.resources/baseline_execution_prompt.md for the template.Instruct the subagent(s) to return their git diff and verification outputs (dart pub get, dart format, dart analyze, dart test) without committing. Ensure you instruct them to run these commands exclusively from within the <target-package-root> directory to avoid analyzing unrelated packages.
CRITICAL: You must explicitly warn the subagent(s) to confine all file edits strictly to their current working directory and avoid using absolute paths to modify the parent workspace.
WORKSPACE LIMITATION WARNING: If the user has multiple active workspaces mounted, the Workspace: branch feature will fail. In this situation, you MUST warn the user that running concurrent evaluations in Workspace: inherit mode will cause git state bleed and cross-eval pollution (e.g., changes made by a failure scenario will be visible to a success scenario running simultaneously in the same shared directory). Instruct the user to fix this by closing all workspaces except the primary package workspace, and then re-run the evaluations. Do NOT silently fallback to Workspace: inherit for concurrent tasks.
repo_criteria + evals.json expectations). Use the grading instructions in resources/agent_judge_prompt.md. When an expectation fails, you MUST explicitly list both the expectation and what was actually found that caused the failure.<skill>_eval_results.md) containing the metadata, pass/fail rationale, and raw diffs/stdout.Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
亚马逊卖家专用的 skill 创建器(中文)。当用户想把一个亚马逊运营/自媒体/日常工作流程变成可复用的 skill 时使用。触发场景包括但不限于:用户说"我想做一个 skill""把这个流程变成 skill""帮我写个自动化""优化我已有的 skill""给这个工作流做个自动化",即使用户没用"skill"这个词,只要在描述"以后每次都这样做"的重复性工作时也应触发。本 skill 的核心差异:强制用户先回答 6 个业务问题(业务目标/过去做法/具体步骤/方法论/调用方式/期望输出)再进入创建流程,防止产出空洞 skill。Create new skills, improve existing skills, run evals and benchmarks — tailored for Amazon sellers with a Chinese-first workflow.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Take flutter/run-evals from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.