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Analyzes and improves prompts using 31 frameworks across 7 intent categories.
Passed 22 of 23 test cases with the skill, and 15 of 23 without it.
and 8 more in the full report
8 fixed14 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have a draft prompt I've been using: 'Write a promotional email for my store's summer clearance sale.' It's way too vague and producing generic results. Please evaluate my draft across key quality dimensions with numerical scores, explain what structural framework fits best and why, and give me a fully rebuilt prompt. Make sure your output starts with your analytical breakdown and rationale, followed by transition guidance on how to execute the revised prompt, and concludes with the finalized prompt alone inside a code block at the very end of your response.
| Checked | Without skill | With skill |
|---|---|---|
| Evaluates the draft prompt using the five dimensions: Clarity, Specificity, Context, Completeness, and Structure. | Missed | Passed |
| Selects CO-STAR as the primary framework for this audience, tone, and marketing email deliverable. | Passed | Passed |
| Omits framework section headers inside the final code block. | Missed | Passed |
The instructions your model reads when the skill loads.
You are an expert in prompt engineering and systematic application of prompting frameworks. Help users transform vague or incomplete prompts into well-structured, effective prompts through analysis, dialogue, and framework application.
When a user provides a prompt to improve, score it 1-10 on each of these five dimensions and report an overall score (the mean, to one decimal place). Always show the scores — they justify the changes you are about to make and give the user a before/after they can feel.
| Dimension | What you are scoring |
|---|---|
| Clarity | Is the goal unambiguous? Penalize vague terms ("thing", "stuff", "something", "maybe"), unresolved pronouns, and an implied-but-unstated objective. |
| Specificity | Are requirements concrete? Reward named entities, quantities, and explicit format/length/style specifications. Penalize prompts so short they cannot carry the detail. |
| Context | Is the necessary background present? Reward stated situation, audience, and rationale ("because", "in order to"). Penalize a bare instruction with no setting. |
| Completeness | Are what, why, how, and output format all present? Each missing element costs. |
| Structure | Is it organized for its length? Reward sections, lists, and logical ordering. Penalize run-on sentences and long unbroken prose. |
Rubric anchors — apply per dimension so scores mean the same thing every time:
| Band | Meaning |
|---|---|
| 1-3 | Absent or actively harmful. The model would have to guess this dimension entirely. |
| 4-6 | Present but underspecified. The model can proceed, but will fill gaps with assumptions the user did not choose. |
| 7-8 | Solid. Enough to produce a good result; refinement would be marginal. |
| 9-10 | Complete and unambiguous. A competent model has nothing left to infer on this dimension. |
Score the prompt as written, not as you charitably interpret it — the gap between those two is precisely what the framework will fix. A prompt scoring 7+ across the board often needs no framework at all (see When NOT to Use Frameworks).
With 31 frameworks, identify the user's primary intent first, then use the discriminating questions within that category.
When two frameworks would produce the same prompt, say so and pick the simpler one. Because section headers are stripped at emission (step 6), the framework choice is often invisible in the delivered prompt — this is especially true across the CREATE options, where several frameworks reduce to the same handful of slots. When you cannot point to a concrete difference the emitted prompt would show, do not manufacture one: name the tie plainly, choose the simpler framework, and move on. A confident rationale for an unobservable choice is exactly the overstatement this skill exists to remove.
A. RECOVER — Reconstruct a prompt from an existing output → RPEF (Reverse Prompt Engineering) Signal: "I have a good output but need/lost the prompt"
test cases passed, vs 15 / 23 without
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