Automate creation, testing, and optimization of AI agent skills.
npx skills add https://github.com/susomejias/rembric --skill skill-creator-susomejias
Please help me install this Agent Skill. Skill: skill-creator Source: https://github.com/susomejias/rembric/tree/main/.agents/skills/skill-creator Command: npx skills add https://github.com/susomejias/rembric --skill skill-creator-susomejias
Building AI skills that work reliably is hard — most skills undertrigger, produce inconsistent outputs, or fail to handle edge cases. Without systematic testing and measurement, you're left guessing whether your skill actually helps or just adds noise.
Use the skill-creator skill to design a new skill for summarizing GitHub pull requests, then run the evaluation loop to measure and improve its performance.
High-intent search queries and answers about installing and using this skill.
AI agent skill optimization requires systematic evaluation and iteration loops to measure pass rates and validate triggering accuracy. Assertion-based grading and baseline comparisons quantify output consistency, ensuring reliable performance across diverse user queries.
Benchmarking AI coding skill performance involves running baseline comparisons against real prompts and generating assertions to measure pass rates. Statistical variance analysis identifies output inconsistencies, enabling developers to iterate and tune descriptions for reliable triggering.
Skill description tuning for Claude Code environments is automated through optimization loops that validate triggering accuracy. The process iteratively adjusts descriptions based on assertion-based grading results, ensuring the skill activates reliably across diverse user prompts.
Evaluating AI skills end-to-end involves guided creation from a simple intent, followed by quantitative evaluation using assertion-based grading. Optimization loops then measure pass rates and statistical variance to validate performance and refine the skill description.
Subagent execution is required to measure pass rates and validate output consistency during skill evaluation. It integrates with assertion-based grading and optimization loops to automate the testing and description tuning processes.
Inconsistent AI skill outputs occur without systematic testing and measurement. Applying quantitative evaluation, baseline comparisons, and statistical variance analysis identifies edge cases, allowing you to iterate on the description until triggering accuracy improves.
Fudan-SMI-labPreinstall PyYAML before torch-npu to prevent transitive dependency failures.
yahav123147Create and iterate SKILL.md structures with trigger evaluations and grading evidence.
nikhilsitaramBenchmark Claude Code skill revisions with isolated runs and statistical grading.
openakitaCreates and iteratively improves AI skills with quantitative benchmarking.
