By alexclowe
Build and run production-grade evaluations for LLM prompts and skills, including A/B testing, stress-testing with adversarial inputs, statistical comparisons, and static audits of skill files to ensure quality, coverage, and budget compliance.
Build a custom evaluation rubric for a skill or prompt covering accuracy, tone, compliance, and latency
Static analysis of SKILL.md — checks frontmatter, edge cases, prompt-injection surface, and length budget
Version-control a skill, A/B test two variants, and output a statistical-significance verdict
Run a prompt against 10–100 synthetic test cases and produce a coverage and quality report
Prompt-engineering expertise for designing test cases, edge cases, adversarial inputs, and iterating on prompts based on eval results
Skill and prompt benchmarking expertise for measuring latency, accuracy, token cost, and token-budget compliance across variants
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