prompt-optimizer

name

prompt-optimizer

description

Iteratively evaluate and optimize task prompts or system rules using a 6-dimension scoring system. Auto-detects whether input is a Prompt or Rule and switches scoring criteria accordingly. Use when the user mentions "optimize prompt", "improve prompt", "prompt scoring", "refine prompt", "优化提示词", "改进 prompt", "打磨提示词", "优化 rules", "改进规则", "optimize rule", or wants to improve the quality of any prompt or rule.

Prompt Optimizer

A semi-automatic iterative optimization skill inspired by the autoresearch paradigm. Supports two modes — Prompt Mode for task-specific prompts and Rules Mode for persistent system-level rules — with auto-detection.

Workflow

Step 1: Receive Input

Accept a prompt or rule via inline text or file path. If the user provides a file path, read the file contents. If neither, ask the user to provide the text they want to optimize.

Step 1.5: Auto-Detect Mode

Classify the input as Prompt or Rule based on these signals:

Signal	Prompt	Rule
Describes a single task with expected output	Yes	No
Uses persistent behavioral language ("always", "never", "when X do Y")	No	Yes
Contains role/persona definition for ongoing use	No	Yes
Expects a one-time deliverable	Yes	No
Located in .cursor/rules/ or user_rules config	No	Yes
References other rules or system-level concerns	No	Yes

If ambiguous, ask the user to confirm. Display the detected mode: [Mode: Prompt] or [Mode: Rules].

Step 2: Evaluate — 6 Dimensions (1-10)

Select the scoring table matching the detected mode.

Prompt Mode — for task-specific, one-off prompts:

Dim	Name	Guiding Question
C	Clarity	Would a context-free LLM interpret this unambiguously?
S	Specificity	Are constraints, output format, and expected behavior explicit?
T	Structure	Is the information logically organized with clear hierarchy?
O	Completeness	Does it cover context, examples, edge cases, and error handling?
E	Efficiency	Is every sentence carrying necessary information? Zero fluff?
R	Robustness	Would 10 runs produce consistent, high-quality outputs?

Rules Mode — for persistent system-level rules:

Dim	Name	Guiding Question
C	Clarity	Would any LLM unambiguously understand the behavioral intent?
S	Scope Fit	Is the rule's breadth appropriate — not too broad, not too narrow?
T	Structure	Is the rule well-organized and easy to scan during every conversation?
O	Coverage	Does it handle the relevant scenarios without over-specifying?
E	Efficiency	Is the token cost justified given this runs on EVERY conversation?
R	Composability	Does this rule coexist peacefully with other rules? No conflicts?

Composite score = unweighted average of all 6 dimensions (user may override weights).

For detailed scoring rubrics and anchor examples, see scoring-rubric.md.

Step 3: Output the Scorecard

Use this exact format:

Prompt Mode:

== Prompt Scorecard v{N} ==
Clarity:      {score}/10  {delta}
Specificity:  {score}/10  {delta}
Structure:    {score}/10  {delta}
Completeness: {score}/10  {delta}
Efficiency:   {score}/10  {delta}
Robustness:   {score}/10  {delta}
------------------------------
Composite:    {avg}/10    {delta}

Weakest:  {dimension_name}
Verdict:  {one-line diagnosis}

Rules Mode:

== Rules Scorecard v{N} ==
Clarity:       {score}/10  {delta}
Scope Fit:     {score}/10  {delta}
Structure:     {score}/10  {delta}
Coverage:      {score}/10  {delta}
Efficiency:    {score}/10  {delta}
Composability: {score}/10  {delta}
------------------------------
Composite:     {avg}/10    {delta}

Weakest:  {dimension_name}
Verdict:  {one-line diagnosis}

• For v1, leave {delta} blank.
• For v2+, show delta as (+1), (-1), or (=) relative to previous version.

Step 4: Suggest Improvements

Focus on the weakest 1-2 dimensions only. Greedy strategy — small targeted fixes avoid regression on other dimensions.

Each suggestion must be:

1. Concrete — show the exact text to add, remove, or rewrite.
2. Justified — explain which dimension it targets and why.
3. Minimal — smallest change for maximum score uplift.

Step 5: User Confirmation

Present the suggested changes and wait for user confirmation:

• Confirmed → apply changes, go to Step 6.
• Modified → incorporate user adjustments, then go to Step 6.
• Rejected → generate alternative suggestions, return to Step 4.

Step 6: Apply and Re-evaluate

1. Produce the new prompt version.
2. Re-run the 6-dimension evaluation (Step 2).
3. Output the updated scorecard with deltas.
4. Append to the version history table.

Step 7: Version History

Maintain a running table throughout the session. Column headers adapt to mode:

Prompt Mode: | Version | C | S | T | O | E | R | Composite | Change Summary | Rules Mode: | Version | C | SF | T | Cov | E | Comp | Composite | Change Summary |

| Version | C | S | T | O | E | R | Composite | Change Summary |
|---------|---|---|---|---|---|---|-----------|----------------|
| v1      | 5 | 4 | 6 | 3 | 7 | 4 | 4.8       | baseline       |
| v2      | 7 | 4 | 6 | 5 | 7 | 5 | 5.7       | added examples  |

Step 8: Termination

The loop ends when:

• Composite score >= 8.5, OR
• User explicitly says they are satisfied.

On termination, output:

1. The final optimized prompt (complete text).
2. The full version history table.
3. A summary of key improvements made.

Optimization Principles

1. One thing at a time — never rewrite the entire prompt in one iteration. Target the weakest dimension with surgical changes.
2. Never break what works — if a dimension scored 8+, do not touch the text responsible for that score unless absolutely necessary.
3. Simplicity over cleverness — if two rewrites achieve the same score gain, pick the shorter one.
4. Evidence over intuition — justify every score with a specific quote or absence from the prompt text.
5. Respect user intent — the optimization must preserve the user's original purpose. If unclear, ask before changing.

Quick Examples

For complete optimization walkthroughs (from low-score to high-score), see examples.md.