prompt-optimization

Prompt Optimization Skill

Purpose

Improve prompts while preserving user intent.

Core Principles

1. Procedural Gates: Complete analysis, optimization, and balance in order. Each phase has observable pass conditions that control the next transition.
2. Intent Preservation: Record the original requirements before changing wording, structure, context, constraints, or examples.
3. Necessary and Sufficient Context: Include the information needed for a decision, action, or verification result. Condense or omit information that does not affect one of them.
4. Minimal Sufficient Work: Classify discovered possibilities as candidates. Add work only when it changes the outcome, protects a required boundary, serves a real consumer, or supplies necessary proof.
5. Traceable Changes: Connect every change to a finding or project-specific source.
6. Self-Contained Execution: Require no temporary artifacts or additional agents.

Required References

Load references at the phase where their rules become necessary:

Phase	Condition	Required reference
Analysis	Every prompt	references/patterns.yaml
Analysis	Target is a SKILL.md, agent, or skill reference	references/skills.md
Balance	Every prompt	references/execution-quality.yaml

Record the loaded path and covered rule IDs in the phase's reference_coverage. A phase passes only when its required references and rule coverage are present.

Output Mode

Select the output mode before analysis. An explicit output_mode of interactive or machine takes priority; for any other value, ask the caller to choose one. When it is absent, use Machine mode only if the caller explicitly requests the Machine Output Contract as the response format; otherwise use Interactive mode. Apply the same rule to human and LLM callers.

Keep output_mode outside the target prompt and analysis.original_prompt.

Pattern Detection

Evaluate every pattern and record issue, already_satisfied, or not_applicable.