prompt-optimizer

Prompt optimizer

Contract

Field	Bound contract
Trigger	User asks to improve, optimize, rewrite, tune, or port a prompt, or to audit prompt text for dated instructions
Authority	Read-only: no file, VCS, credential, paid, published, deployed, or remote mutation. A proposed diff is chat output; applying it is outside this skill.
Side effect	Chat output returns either an optimized prompt or an audit report plus a proposed diff
Done	Optimize mode: shorter prompt validated on holdout cases with one owner per behavior rule. Audit mode: every finding names a pattern and a target-model reason, and the diff carries only high- and medium-confidence hunks.

Inputs

Required:

• The source prompt text or document to optimize or audit
• The target task or goal the prompt must serve (optimize mode)

Optional:

• Mode (optional): optimize (default) or audit. Audit when the request names auditing, dated instructions, cruft, or a model migration; optimize otherwise. When a request asks for both, run audit first and report each result separately.
• Known failure cases or error patterns from prior runs
• Model family or adapter context (e.g., Claude, GPT-4, Gemini); audit mode reads this as the target model
• Evaluation criteria the user already accepts

Procedure

Optimize mode (default)

1. Capture the source. Record the exact prompt text or document. Note any quoted variable slots, numbered steps, conditional branches, or formatting constraints present in the original. Done when: the source prompt is recorded with all structural features noted.
2. Identify the target. Confirm the single goal the optimized prompt must serve. Reject scope that would require two different outputs or two disjoint audiences. Done when: a single goal is confirmed or scope is rejected.
3. Extract behavior rules. Enumerate every requirement the prompt must satisfy: output format, tone, constraints, handling of edge cases. Assign one named owner per rule. Collapse rules that overlap. Done when: behavior rules are enumerated with one named owner per rule and overlaps collapsed.
4. Write the optimized prompt. Apply these transformations:
   • Remove every sentence that does not change a routing, format, or constraint decision
   • Replace vague verbs with concrete imperatives
   • Flatten nested conditionals into numbered choices
   • Substitute one placeholder per variable slot; name the slot by its semantic role
   • Add a final residual-risks clause naming the prompt behaviors that are not guaranteed under distribution shift or novel inputs Done when: the optimized prompt applies all transformations and includes a residual-risks clause.
5. Build holdout cases. Write three cases on which the original prompt failed or would fail: one at each boundary (minimum valid input, maximum valid input, empty or malformed input). Verify the optimized prompt handles all three without contradictory outputs. Done when: three holdout cases are written and verified against the optimized prompt.
6. Validate one owner per rule. Confirm each behavior rule from step 3 is observable in the optimized prompt or in the holdout cases. Flag any rule that appears nowhere. Done when: every behavior rule is observable in the prompt or holdout cases, or unobservable rules are flagged.
7. Annotate adapter notes. Record model-family-specific adjustments (token budget, instruction hierarchy, chat-template constraints) that would affect reliability if changed. Done when: adapter notes are recorded for each model family.
8. Return the result. Output the optimized prompt, target, success criteria (rule list), external context (adapter notes), residual risks, and holdout validation summary as a structured response. Done when: the structured response is returned with all six elements.