AI PPT Remix

Use this skill when the user wants a source deck reworked into a new AI-visual deck without losing the original argument. This skill is for semantic slide remixes, not for generic "make me a new PPT" requests.

Also use the existing PowerPoint skill for rendering and rebuilding .pptx files, and the existing imagegen skill for per-slide generation.

Transcript-first rule

When a real talk transcript, narration script, or spoken notes exist, they are the authority for semantic fidelity.

• The transcript is not background context; it is a required source.
• Do not simplify away transcript-specific claims just because the original slide only hinted at them.
• Numbers, dates, platforms, market comparisons, causal claims, and caveats mentioned in the transcript must be captured slide by slide as explicit constraints before image generation.
• A visually strong slide is still a failure if the transcript-specific point disappears into generic "marketing" visuals.
• If a slide's factual density is too high for a single clean AI image, stop and either switch to a hybrid rebuild flow or ask the user how much factual compression is acceptable.

Evidence-fusion rule

When the user asks for AI-remixed slide images, source screenshots are evidence, not delivery tiles.

• Do not paste old slides, reference boards, or screenshot grids into the final image.
• Do not remove the evidence either. Rebuild it as large, recognizable visual evidence inside a new composition: charts become redrawn evidence charts, screenshots become stylized but identifiable artifacts, case studies keep their names, and spoken numbers become readable labels.
• Use "AI-fused evidence" when the user wants a new AI image but still needs key screenshots and facts to be visible. The final slide should feel newly generated while letting the speaker point to the same proof.
• A generated slide is not acceptable if the must-keep facts only appear as tiny background texture, unlabeled icons, or vague themed imagery.
• If the model cannot keep both visual quality and factual evidence in one pass, regenerate from a stricter manifest before changing the deck.

When To Use

• The user has an existing pptx plus a matching script, narration, or detailed notes.
• One new slide should combine one or more old slides into a single semantic beat.
• Important screenshots, charts, labels, examples, or case-study evidence must survive the redesign.
• The result should be a visually consistent AI-generated deck, not a screenshot collage.

Do not use this skill for net-new decks with no source deck, or for simple edits that only need normal PowerPoint changes.

Workflow

1. Prepare source materials.

• Render the source deck to preview PNGs with the PowerPoint skill.
• If there is already a target deck with a good visual direction, render that too and use it as the style-reference deck.
• Mark unchanged slides early. Reuse them instead of regenerating everything.

2. Group source slides by script meaning.

• One output slide equals one spoken idea, not one source slide.
• Group one or more source slides when they support the same script beat.
• Before generating, extract a fact checklist from the transcript for each group:
  • exact visible text that must stay readable
  • must-keep evidence screenshots or case-study visuals
  • must-keep transcript facts such as numbers, dates, platforms, comparisons, and causal links
  • any caveats or nuance that would be lost if the slide became generic
• Write a group manifest before generating. See references/manifest-schema.md.
• Treat this manifest as a contract. Do not generate first and backfill facts afterward.

3. Build one reference board per generated slide.

• Use scripts/build_reference_boards.py.
• Each board should show:
  • one style reference slide
  • all source slides in the semantic group
  • the relevant script excerpt
  • the visible text that must remain readable
  • the must-keep evidence
  • the must-keep transcript facts
  • any semantic remapping rules
• The reference board is only a prompt and review artifact. It must not be copied, tiled, or shrunk into the final slide.

4. Write prompts from meaning, not from layout.

• Use references/prompting.md.
• Ask for a brand-new 16:9 slide image.
• Require exact visible text where needed.
• List the must-keep screenshots, examples, labels, numbers, or diagrams explicitly.
• List transcript-derived facts explicitly, especially when they are easy for the model to blur away:
  • budgets, counts, dates, platforms, geography, ratios, and comparisons
  • who or what a number refers to
  • whether a point is an example, a benchmark, a warning, or a caveat
• Convert high-risk spoken facts into visible slide labels or callouts. If a fact matters to the talk, it should not depend on speaker memory alone.
• Crucial rule: place evidence according to what the script means, not according to the original pixel position.

5. Generate consistently across the deck.

• Generate one anchor slide first to lock palette, texture, density, and composition language.
• Reuse the accepted anchor slide as a style reference for later generations when possible.
• For high-risk pages, show both the reference board and the most relevant original slide.

6. Adopt approved images and rebuild the deck.

• Use scripts/adopt_generated_slide.py to crop and copy the chosen generated image into the working slide-image path.
• Rebuild the final deck with the PowerPoint skill.
• Preserve speaker notes and any unchanged slides.

7. Review semantically, not just visually.

• Reject any slide that becomes generic while dropping the source argument.
• Reject placeholder frames, fake browser chrome, or empty mockup boxes.
• Reject old-slide sticker layouts: a new background plus pasted source slide thumbnails is not an AI remix.
• Reject any slide that swaps a specific case study for a generic substitute.
• Reject any slide that loses transcript-specific facts or softens them into vague summary language.
• Reject any slide that keeps the title but drops the spoken point, such as the specific benchmark, spend comparison, or risk chain described in the transcript.
• Use a yes/no acceptance checklist per slide before adoption:
  • Is the spoken claim still visible?
  • Are the must-keep facts still present?
  • Are the must-keep screenshots or case studies still recognizable?
  • Would the speaker still be able to say the original lines naturally while this slide is on screen?
• For partial-deck repairs, hash or otherwise compare unchanged slide images before delivery to prove non-target slides were not modified.

Non-Negotiables

• Do not shrink old slides and paste them into a new slide as tiny thumbnails for delivery.
• Do not let the model replace specific screenshots, examples, charts, or text evidence with generic filler.
• Do not preserve the original physical layout if the new slide structure changes the meaning.
• Do preserve the original semantic role of each example.
• Do not let transcript facts disappear just because they were spoken instead of typed on the source slide.
• Do not accept a beautiful slide that fails as speaker support for the actual talk.

Example:

• If the old slide used a different axis direction than the new slide, remap the examples to the correct new quadrants by label meaning, not by old screen position.

Resources

• references/manifest-schema.md: group manifest fields and example JSON.
• references/prompting.md: prompt template, consistency rules, and semantic-fidelity checks.
• scripts/build_reference_boards.py: builds style-plus-source reference boards with script and must-keep notes.
• scripts/adopt_generated_slide.py: copies the selected generated image into the working slide image path with cover-crop resizing.