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Get Started Free →Build, refine, and quality-check reusable image-generation prompts for multi-brand creative pitches and campaign proposals. Use when Codex needs to turn a brief, strategy, slide, reference image, brand system, event concept, product idea, UI flow, social-content concept, or revision request into an executable prompt for full-slide images, pitch-deck visual assets, realistic activation scenes, product or merchandise mockups, app/UI visuals, social-content samples, multi-panel compositions, or con
.claude/skills/kiakun-collab-pitch-visual-prompting/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 91% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 42% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 29% | 0% |
Turn a pitch brief into a model-ready visual prompt that makes the strategic idea visible. Keep the workflow brand-agnostic: derive every color, tone, audience, product, IP constraint, and visual convention from the current brief and references.
Do not hardcode any past client, campaign, palette, platform, or IP into the output.
Read intake-and-routing.md when the task type, reference roles, or output mode is unclear.
Do not mix full-slide generation with asset generation. If the deliverable is an editable deck, generate assets without text and keep copy in native deck objects. If the user explicitly wants a complete slide image, keep the amount of model-rendered copy proportional to the model's reliability.
Read pitch-visual-patterns.md only when choosing a composition or visual evidence pattern.
Give every reference one explicit role:
Do not tell the model to “refer to all images” without assigning roles. State what to copy, what to reinterpret, and what to ignore from each reference.
Route each supplied image as one of:
Always load strong references for tasks such as replacing a person with a supplied character, placing an exact product or logo into a scene, preserving a subject or composition, or turning an uploaded 3D model design into a physical object visualization. If the source is a 3D file rather than a renderable image, first obtain useful rendered views and pass those views to the image model.
Do not load an image merely because it was uploaded. Style-only references can contaminate the new subject or composition when passed as pixels.
Read reference-image-routing.md whenever any image reference is supplied.
Include only sections that affect the output:
Use concrete visual instructions. Replace vague requests such as “premium,” “more impactful,” or “make it creative” with observable choices: scale, camera distance, material quality, density, whitespace, number of people, lighting contrast, image-to-text ratio, and focal hierarchy.
Read prompt-modules.md when composing the final model prompt.
Treat visible text as a separate reliability decision:
Text: none unless text is intrinsic to the requested object.For a revision, state:
Do not redesign the entire image when the user asks for a local correction. If the model uses the wrong reference or changes frozen content, stop and re-anchor the target before another generation.
Read revision-and-qa.md for repair prompts and validation checks.
When the user asks only for a prompt, return:
When the user asks for the image itself, use the prompt with the image-generation tool they selected or the appropriate available image tool. Inspect the result against the brief before reporting success.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 12,719 | 10,554 | -17% | 1 | 1 | 0% | 2,168 | 3,201 | +48% | 0 | 0 | — |
case-01 | fail→pass | 14,196 | 16,713 | +18% | 1 | 1 | 0% | 2,366 | 4,022 | +70% | 0 | 0 | — |
case-03 | fail→pass | 10,865 | 13,444 | +24% | 1 | 1 | 0% | 1,856 | 3,541 | +91% | 0 | 0 | — |
case-04 | pass→fail | 10,445 | 15,192 | +45% | 1 | 1 | 0% | 1,618 | 3,928 | +143% | 0 | 0 | — |
case-05 | pass→pass | 15,917 | 14,101 | -11% | 1 | 1 | 0% | 2,772 | 3,768 | +36% | 0 | 0 | — |
case-06 | pass→pass | 6,961 | 8,621 | +24% | 1 | 1 | 0% | 1,240 | 2,860 | +131% | 0 | 0 | — |
case-07 | pass→pass | 5,244 | 6,872 | +31% | 1 | 1 | 0% | 893 | 2,597 | +191% | 0 | 0 | — |
case-08 | pass→pass | 10,715 | 9,400 | -12% | 1 | 1 | 0% | 1,591 | 2,832 | +78% | 0 | 0 | — |
case-09 | pass→pass | 14,693 | 9,389 | -36% | 1 | 1 | 0% | 2,436 | 3,036 | +25% | 0 | 0 | — |
case-10 | fail→fail | 12,329 | 9,822 | -20% | 1 | 1 | 0% | 2,053 | 2,978 | +45% | 0 | 0 | — |
case-11 | fail→pass | 12,775 | 11,319 | -11% | 1 | 1 | 0% | 2,233 | 3,170 | +42% | 0 | 0 | — |
case-12 | pass→pass | 10,190 | 9,385 | -8% | 1 | 1 | 0% | 1,710 | 2,975 | +74% | 0 | 0 | — |
case-13 | pass→pass | 7,048 | 5,528 | -22% | 1 | 1 | 0% | 1,200 | 2,383 | +99% | 0 | 0 | — |
case-14 | pass→pass | 7,377 | 10,018 | +36% | 1 | 1 | 0% | 1,372 | 3,372 | +146% | 0 | 0 | — |
case-15 | pass→pass | 11,488 | 9,238 | -20% | 1 | 1 | 0% | 1,984 | 2,992 | +51% | 0 | 0 | — |
case-16 | fail→pass | 12,769 | 9,061 | -29% | 1 | 1 | 0% | 2,199 | 2,839 | +29% | 0 | 0 | — |
case-17 | fail→pass | 9,760 | 6,228 | -36% | 1 | 1 | 0% | 1,653 | 2,497 | +51% | 0 | 0 | — |
case-18 | fail→pass | 8,628 | 4,539 | -47% | 1 | 1 | 0% | 1,582 | 2,235 | +41% | 0 | 0 | — |
case-19 | fail→pass | 12,651 | 6,407 | -49% | 1 | 1 | 0% | 2,127 | 2,508 | +18% | 0 | 0 | — |
case-20 | fail→pass | 7,766 | 8,733 | +12% | 1 | 1 | 0% | 1,223 | 2,854 | +133% | 0 | 0 | — |
case-21 | fail→fail | 14,604 | 8,670 | -41% | 1 | 1 | 0% | 2,187 | 2,663 | +22% | 0 | 0 | — |
case-22 | pass→pass | 6,194 | 6,817 | +10% | 1 | 1 | 0% | 997 | 2,448 | +146% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.