AgentSkillScore
Why This is an Opportunity
Andrej Karpathy says when AI agents fail it is usually a skill issue not a capability issue — bad instructions, wrong memory tools, no parallelization. Matt Shumer wrote that most people use Claude Code wrong and miss massive productivity gains (348 likes, 146K views). The top 50 Claude Skills list got 1.1K likes and 293K views. Demand for agent skill optimization is massive but there is no tool for it.
Key Pain Points
- •You do not know if your CLAUDE.md is good or terrible until your agent wastes 30 minutes on a task it should have finished in 5
- •Your agent repeats the same mistakes and you have no systematic way to identify and fix the patterns causing failures
- •You see other people getting 10x productivity from Claude Code but cannot figure out what they are doing differently
- •Every new agent project starts from scratch with no way to benchmark against what actually works
Original Discovery
A benchmarking and optimization platform for AI agent configurations. You submit your CLAUDE.md files, skills, MCP configs, and prompts, and it scores them against best practices, identifies anti-patterns, suggests improvements, and benchmarks performance against anonymized community data. Think ESLint for agent configurations — it catches the mistakes that make agents fail (bad instructions, missing memory, no parallelization) and shows you exactly how to fix them. Includes A/B testing to compare config variants.
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