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Self-improving prompts through meta-level optimization
Passed 22 of 22 test cases with the skill, and 16 of 22 without it.
and 9 more in the full report
6 fixed16 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When evaluating prompt output performance in the self-improvement pipeline, what percentage of the total score is allocated specifically to efficiency?
| Checked | Without skill | With skill |
|---|---|---|
| Allocates 15 percent weight to efficiency | Missed | Passed |
The instructions your model reads when the skill loads.
Optimize prompts through iterative self-improvement.
/meta-prompting "<prompt>" [--iterations <n>] [--target <score>]
Takes a prompt, executes it, scores the result, then uses the LLM to optimize the prompt for better results. Iterates until the quality target is met or max iterations reached. Stores optimized prompts in the skill library for reuse.
Optimization dimensions: clarity enhancement, example addition, instruction refinement, context optimization.
# Optimize a code generation prompt
/meta-prompting "Generate a rate limiter in Go" --target 8
# Optimize a content prompt with more iterations
/meta-prompting "Write a pitch for enterprise clients" --iterations 5
# Optimize a system prompt
/meta-prompting "You are a security auditor..." --target 9
test cases passed, vs 16 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.