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Found 30 Skills
Analyzes and optimizes an existing agent skill for conciseness, discoverability, and adherence to best practices. Use when a skill needs improvement, is too verbose, has poor activation rates, or fails to follow progressive disclosure patterns. Do NOT use for creating a new skill from scratch — use create-skill instead.
Create or update Claude skills. Use for new skills, skill references, skill scripts, optimizing existing skills, extending Claude's capabilities.
Orchestrates complete skill lifecycle from creation to optimization. Use for comprehensive skill development, reviewing skills, or managing skill quality.
Use when auditing, trimming, or restructuring AI skill files to reduce context-window consumption. Trigger whenever a SKILL.md exceeds 120 lines, skills share duplicated content, AGENTS.md has large inline blocks, or the user asks to optimize, slim down, or reduce token usage of their skills.
Define the design rules (Skill Laws) that all Skills must follow, including core principles such as AI-first, human-centric, and ready-to-use. When to use: When users create a new Skill, optimize an existing Skill, ask about Skill design specifications, or need to evaluate Skill quality.
Create, edit, and refine agent skills through co-development and eval loops. Use for ANY question about skills or request to create/modify them.
Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.
Autonomously optimize an existing AI skill by running it repeatedly against binary evals, mutating one instruction at a time, and keeping only changes that improve pass rate. Based on Karpathy-style autoresearch, but applied to SKILL.md iteration instead of ML training. Use when optimizing a skill, benchmarking prompt quality, building evals for a skill, or running self-improvement loops on reusable agent instructions. Triggers on: skill-autoresearch, optimize this skill, improve this skill, benchmark this skill, eval my skill, run autoresearch on this skill, self-improve skill.
Use when creating, updating, or improving agent skills.
Active diagnostic tool for analyzing skill prompts to identify token waste, anti-patterns, trigger issues, and optimization opportunities. Use when reviewing skill prompts, debugging why skills aren't triggering, optimizing token usage, or preparing skills for publication. Provides specific, actionable suggestions with examples.
This skill should be used when the user asks to "improve my setup", "learn from this session", "fix my config", "stop asking for permissions", or reports friction with skills, agents, hooks, or permissions. Analyzes conversation history and proposes configuration improvements.
Use when creating a new skill with maximum quality. Launches 3 parallel competing approaches (skill-creator, superpowers writing-skills, and manual), compares results on 5 dimensions, then synthesizes the best elements into a final skill. Triggers on "build a skill", "create a skill", "new skill".