Loading...
Loading...
Found 21 Skills
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or implementing evals. Triggers include "agent performance", "token usage", "latency optimization", "eval", "agent metrics", "cost optimization", "agent benchmarking".
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.
Query cross-project usage analytics. Use when reviewing agent, skill, hook, or team performance across OrchestKit projects. Also replay sessions, estimate costs, and view model delegation trends.
Review recent work, identify process gaps and repeated mistakes, and produce specific file edits to prevent them. Not a reflection exercise — outputs config and identity changes. Trigger manually after sprints, or automate weekly.
Japanese version of the PUA Universal Motivation Engine. It compels exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology in Japanese. MUST trigger under the following conditions: (1) Any task has failed 2+ times, or you're stuck in a loop of tweaking the same approach; (2) You're about to say 'I cannot', suggest manual handling to the user, or blame the environment without verification; (3) You find yourself being passive — not searching, not reading source code, not verifying, just waiting for instructions; (4) The user expresses frustration in any form: 'try harder', 'stop giving up', 'figure it out', 'why isn't this working', 'again???', 'もっと頑張れ', 'なんでまた失敗したの', 'もう一回やって', 'なんとかしろ', or any similar sentiment regardless of phrasing. It should also trigger when facing complex multi-step debugging, environment issues, configuration problems, or deployment failures where early surrender is tempting. Applies to ALL task types: code, configuration, research, writing, deployment, infrastructure, API integration. DO NOT trigger on first-attempt failures or when a known fix is already executing successfully.
Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.