Pradaslides logo

Pradaslides

Organization
studio-yebisu
pradaslides

Create, redesign, critique, or plan audience-ready presentations and PowerPoint decks from a short prompt or source material. Use for portfolios, work/result reviews, proposals, sales decks, investor pitches, strategy or decision decks, research and technical talks, lessons, keynotes, reports, product launches, and reusable presentation templates. Infers user intent, audience, desired change, narrative, evidence needs, visual direction, delivery mode, and the best available slide runtime. Uses an adaptive, low-friction workflow: planning contracts may remain internal, visual generation follows the user's preference, and delivery may be PPTX plus HTML presenter view, PPTX only, HTML only, Word, PDF, or another requested format.

Overview

Publisherstudio-yebisu
Repositoryyaoyorozu-ai-toolbox-slides
Skill namepradaslides
Stars
10
Forks
2
Bundled files
42
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 42 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by studio-yebisu on GitHub. Read the source before you install it.

Installation

Install the Pradaslides AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/studio-yebisu/yaoyorozu-ai-toolbox-slides/tree/main/examples/repositories/pradaslides/source/upstream
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/studio-yebisu/yaoyorozu-ai-toolbox-slides.git /tmp/yaoyorozu-ai-toolbox-slides
mkdir -p .claude/skills
cp -r /tmp/yaoyorozu-ai-toolbox-slides/examples/repositories/pradaslides/source/upstream .claude/skills/pradaslides
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pradaslides in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pradaslides on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pradaslides is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PradaSlides

Turn a minimal request into a presentation with a clear communication job, credible content, deliberate audience journey, coherent visual system, and verified delivery artifact. Match the user's language unless they request another.

Operating stance

  • Treat slides as an audience experience, not a decorated document.
  • Optimize for the user's intended change: a decision, belief, action, understanding, trust, or remembered impression.
  • Infer sensible defaults from the material. Ask a question only when the answer would materially change the argument, artifact type, confidentiality, or irreversible work.
  • Keep facts, strategy, design intent, build source, previews, and deliverables logically separated. Do not require separate planning files unless the user requests them or the work needs durable auditability.
  • Use the best slide-production capability already available in the host agent. Do not require a Codex-specific tool.
  • Preserve user files. Work in a new project directory unless the user explicitly requests an in-place edit.

Start here

  1. Classify the task: new, redesign, fill-template, enhance-existing, critique, or plan-only.

  2. Read intent-playbooks.md and select one primary intent plus, when useful, one secondary intent.

  3. Resolve two preferences only when the request has not already answered them:

    • Visual generation: may PradaSlides generate original images/video, or must it use supplied, sourced, and native visuals only?
    • Delivery: PPTX + HTML presenter (recommended), PPTX only, HTML only, Word, PDF, or another named format?

    Ask both in one compact message. Do not turn the start into a questionnaire. If the user does not answer and progress is safe, use no generated media and deliver PPTX + HTML presenter. If the request already answers either preference, do not ask it again.

  4. Build an internal working model of the communication brief, evidence ledger, design system, capability route, topology route, visual-generation decision, and deck plan. Materialize the JSON contracts from artifact-contracts.md only when the user asks for planning artifacts, the project is collaborative or resumable, traceability is high-stakes, or a validator/runtime requires them. For a file-backed workspace, run:

    bash
    python scripts/bootstrap_project.py --output <project-dir> --intent <intent> --contracts full
  5. Scan the runtime when the host's slide capabilities are not already obvious:

    bash
    python scripts/capability_scan.py --json
  6. Read capability-orchestration.md and resolve a conservative execution route. Use files when contracts are materialized:

    bash
    python scripts/resolve_capabilities.py --profile <project-dir>/capability-profile.json --brief <project-dir>/brief.json --scan-local --output <project-dir>/execution-plan.json
  7. Follow the workflow below. Do not start visual slide construction before the underlying brief, evidence, capability route, design system, topology route, generation decision, and deck plan are coherent. Coherence is mandatory; emitting their intermediate files is not.

Workflow

1. Establish the communication contract

Write this sentence before outlining:

By the end, [audience] should [think/feel/do] because [central takeaway].

Resolve or infer:

  • audience, context, stakes, prior belief, objections, and decision authority;
  • presentation intent and delivery mode: live, leave-behind, asynchronous, or hybrid;
  • desired from → to shift and final audience action;
  • available time, slide-count constraint, aspect ratio, language, brand, and output format;
  • required evidence, source boundaries, confidentiality, and editability.

Keep the result in working memory by default. If planning artifacts are enabled, record it in brief.json and validate it:

bash
python scripts/validate_brief.py <project-dir>/brief.json

Use assumptions for inferred values. Never disguise an assumption as a user instruction.

2. Route the artifact and runtime

Use the resolved execution plan. Never assume that a multimodal model also has slide authoring or rendering, and never assume a text-only model can semantically inspect media. Treat separately connected vision, image-generation, or video-generation models as delegated capabilities with explicit handoff packets, provenance, acceptance checks, and fallbacks.

Choose one artifact route:

Request shapeRoute
New deck from a goal, topic, or sourcesnew
Improve an existing deck with freedom to re-outlineredesign
Keep native slide shells and replace contentfill-template
Keep visible slides stable; add notes, transitions, or delivery supportenhance-existing
Diagnose and recommend without changing filescritique
Produce strategy, outline, copy, and visual specification onlyplan-only

Then choose a production runtime using runtime-and-delivery.md. Prefer:

  1. the host's native presentation tool when it can create and render the required artifact;
  2. native editable PPTX generation for ordinary business delivery;
  3. a render-parity HTML presenter preview built from the final PPTX renders;
  4. native fixed-stage HTML or Slidev only for explicitly interactive, code-heavy, web-native, or HTML-editable talks;
  5. PDF only when editability is unnecessary;
  6. raster-first PPTX only when the user explicitly accepts limited editability and accessibility.

Honor the selected delivery route:

  • PPTX + HTML presenter: editable PPTX is the source of truth; HTML is a render-parity review and presentation companion.
  • PPTX only: deliver the editable deck without a presenter package.
  • HTML only: author a fixed-stage web deck with presenter controls; do not imply native PowerPoint editability.
  • Word: transform the content into a document-native narrative and use document layout conventions rather than slide canvases.
  • PDF or another requested format: use the closest capable runtime and disclose material editability or interaction limits.

If the user gives no preference, use PPTX + HTML presenter.

If an existing deck must preserve wording, order, or appearance, treat those as invariants in the working model and persist them when planning artifacts are enabled.

3. Inspect sources and build the evidence ledger

  • Inventory every supplied file before outlining.
  • Extract text, tables, charts, images, notes, metadata, and page/slide structure with the appropriate host tools.
  • For PDFs or visually meaningful source files, inspect both extracted content and rendered pages.
  • For attached photos, videos, logos, screenshots, charts, or illustrations, read media-intelligence.md, generate asset-manifest.json, and visually inspect every asset or representative video keyframe before locking the outline.
  • If image/video understanding is unavailable, keep semantic review pending and use a human or delegated reviewer. Metadata alone cannot authorize placement. If generation exists without vision, generated media requires external visual approval before final use.
  • Track every claim, metric, quotation, and visual asset with its source location and status. Persist this as source-ledger.json only when planning artifacts are enabled.
  • Mark invented demo values as scenario; display that label on-slide when the distinction matters.
  • Research only factual gaps that matter to the communication job. Prefer primary and authoritative sources.
  • Never fabricate citations, metrics, customer claims, testimonials, market sizes, or implementation results.

When a ledger file exists, validate it before it becomes slide evidence:

bash
python scripts/validate_source_ledger.py <project-dir>/source-ledger.json

Create a technical asset inventory and review contact sheet when media is present:

bash
python scripts/analyze_assets.py <project-dir>/assets --output <project-dir>/asset-manifest.json --contact-sheet <project-dir>/previews/asset-contact-sheet.png --keyframes-dir <project-dir>/previews/video-keyframes

Enrich the generated manifest with semantic roles, subject, message, focal point, text-safe regions, crop tolerance, rights, sensitivity, and intended slide use. Technical metadata cannot determine meaning by itself.

bash
python scripts/validate_asset_manifest.py <project-dir>/asset-manifest.json --require-reviewed

Read content-and-narrative.md before planning evidence-heavy decks.

4. Design the audience journey

Construct the journey around the audience's mental state, not the source document's chapter order. Use only the phases the intent needs:

attention → orientation → tension → insight → proof → resolution → decision → retention

For each phase, specify:

  • what the audience currently believes or needs;
  • the question the next slide answers;
  • the emotional or cognitive load;
  • the proof required to advance;
  • the transition to the next phase.

Avoid artificial drama. A scientific briefing may emphasize orientation and proof; a portfolio may emphasize identity, selected work, process, outcomes, and fit.

5. Establish the visual system, art direction, and topology registry

Read design-and-visuals.md, art-direction-and-pptx-reconstruction.md, and topology-and-layouts.md. Establish the design system and topology registry internally. Edit design-system.json and use layout-manifest.json when planning artifacts are enabled.

Define:

  • one intent-fit visual direction and a relevant supplied-reference quality floor;
  • canvas, grid, margins, type scale, palette roles, image treatment, and slide furniture;
  • a concrete art_direction: visual thesis, material language, edge language, image roles/treatments/reuse rules, native-versus-raster strategy, and three to eight slide families;
  • deck-level rhythm: topology variety, focal-mass changes, tone shifts, repetition anchors, and density;
  • reusable topology/layout entries with roles, relationships, slots, media capacity, tone support, guardrails, and target-runtime fidelity.

Validate both files when they exist:

bash
python scripts/validate_design_system.py <project-dir>/design-system.json
python scripts/validate_layout_manifest.py <project-dir>/layout-manifest.json

Select layout by the relationship the audience must perceive, not by decoration or template name. Use role → relationship → topology → layout → safe slots → style. Require a layout_rationale for any custom-* layout.

6. Build the deck plan and visual-generation plan

Create a deck plan internally; persist it as deck-plan.json when planning artifacts are enabled. Each content slide must have:

  • one job and one claim;
  • a takeaway title that communicates the claim;
  • evidence and its source IDs;
  • a visual role and chosen visual form;
  • audience journey phase and transition;
  • speaker-note purpose;
  • layout family, registered layout_id, topology, tone, focal emphasis, and density/slot budget.
  • stable asset_ids and a resolved media_plan when media is used; in strict visual mode, multi-asset slides require one complete asset_treatments entry per asset so logos, heroes, screenshots, and video do not inherit the same crop or fallback.

Exceptions: cover, section divider, deliberate pause, and closing identity slides may use non-claim titles.

Apply these tests:

  • So what? The claim matters to this audience.
  • Why true? Evidence supports the claim.
  • Why now/next? The sequence creates momentum.
  • Why this visual? The visual performs a communication task.
  • What should remain? The audience can repeat the central takeaway after the deck.

Lint the persisted plan before building when contract files exist:

bash
python scripts/lint_deck_plan.py <project-dir>/deck-plan.json --brief <project-dir>/brief.json --source-ledger <project-dir>/source-ledger.json --asset-manifest <project-dir>/asset-manifest.json --design-system <project-dir>/design-system.json --layout-manifest <project-dir>/layout-manifest.json --strict-visual

Always make an explicit internal generation decision. If the user declines generation, do not generate media; use supplied, rights-compatible sourced, or native visuals. If the user allows generation and the capability is usable, read generative-visuals.md and perform an opportunity audit. Persist visual-generation-plan.json only when planning artifacts are enabled.

bash
python scripts/validate_visual_generation_plan.py <project-dir>/visual-generation-plan.json --require-resolved

When generation is allowed and available, and the user asks for a polished, distinctive, visual, launch-quality, portfolio-quality, or reference-matched deck, default to at least one original candidate unless supplied media already performs every important hero/concept job or generation creates factual/brand risk. Keep a concrete reason for skip.

If the user provides references, infer transferable principles rather than cloning the composition. If direction is genuinely ambiguous and the choice is consequential, present up to three distinct visual routes with short rationale; otherwise select one and proceed.

Hard visual rules:

  • Group before styling; use proximity, alignment, repetition, and contrast to expose structure.
  • Use whitespace as organization, not as unused decoration.
  • Use one dominant visual idea per slide.
  • Meet the reference-quality floor: recognizable identity, decisive scale contrast, inspectable proof, meaningful topology variation, and coherent tone rhythm.
  • Treat a visual cluster as a prior, never a typography answer. Resolve the specific product's positioning, audience, personality, proof density, interaction model, language, and brand assets before choosing families, scale, weight, tracking, case, and type color. Two products in the same category should not inherit the same type system without a written rationale.
  • Treat material and edge language as first-class tokens. Define how background, surface, depth, frame, curve, overlap, rule, and shadow behave; do not approximate an art direction with only a palette and rounded cards.
  • Pair every text role with its actual rendered surface, not just the deck palette. Normal text needs at least 4.5:1 contrast; muted/support text needs at least 3:1. Do not place a dark-tone text token on a light surface (or the reverse) after a mask, override, or image treatment changes the surface.
  • A curve, mask, crop, or illustration must clarify hierarchy, reveal a focal subject, carry evidence, or establish a named brand/material motif. If it merely slices a photograph or occupies whitespace, delete it and use a grid, rule, or deliberate crop instead.
  • Treat an image slot as a media opportunity, never a fixed template. Resolve each asset's narrative job, focal subject, crop tolerance, text-safe region, scale, and relationship to copy before selecting full bleed, split crop, editorial window, contact sheet, diptych, detail crop, device stage, panorama, mask, or gallery choreography. Repeat a composition only when repetition is an intentional deck anchor.
  • Select frame geometry from the evidence: preserve a full garment in a vertical window, use an edge-to-edge macro plus an establishing view for craft detail, give process work a sequence or wide evidence field, and reserve an arch/circle/organic mask for a subject or brand reason. Do not apply one rounded portrait frame to every supplied photo.
  • Let presentable type consume the available composition. At a 1600×900 live slide, treat roughly 20–26px as normal body territory and reserve 14–18px for true furniture/captions. If a sparse layout leaves large unused space, enlarge the claim, body, image evidence, or spacing relationship before adding a decorative object.
  • Give each used image an explicit role such as hero, process, context, product, detail, evidence, texture, or cutout. Use a slide-family/rhythm map so page silhouettes vary while type, material, edge, and furniture grammar remain coherent.
  • Keep authoring and audit language off the audience-facing canvas. Reference IDs, transfer principles, avoid rules, asset credits, crop labels, topology names, benchmark scores, and QA markers belong in notes or metadata unless the audience genuinely needs them.
  • Treat photographs, diagrams, tables, and charts as evidence carriers.
  • Preserve visual semantics during redesign. Inventory every source screenshot, product example, device state, chart, process diagram, and annotated artifact; resolve each as retain, sanitize, reconstruct, replace-equivalently, or omit-with-rationale. Do not convert visual evidence into prose by default.
  • For instructional slides, show a concrete artifact or mechanism before explaining it. A clean card grid is not equivalent to a legible screen, product proof, chart, device state, or directional process.
  • Avoid generic business imagery, ornamental dashboards, arbitrary blobs, and repetitive card grids.
  • Do not let CSS gradients, empty device shells, avatar silhouettes, or abstract SVG objects impersonate photography, product evidence, or a finished hero. When a supplied reference derives its quality from real media, use relevant supplied or rights-compatible sourced media, or record a blocking media gap.
  • Do not ask an image model to render core slide copy, charts, or exact labels.
  • When editable PPTX is requested, rebuild the visual grammar with native text, rules, shapes, annotations, charts, tables, and page furniture. Rasterize only selected photos, texture, approved complex art, or non-reconstructable visual detail; never use a full generated slide as the default editable deck page.
  • Maintain projection-safe type. Default minimums: title 35 pt, subhead 24 pt, body 16 pt; use larger text for live speaking decks.
  • Treat the outer text-safe area as a hard geometry contract, not a visual preference. Unless the design system declares a stricter value, keep audience-facing text at least 3% of slide width and 4% of slide height from every edge; full-bleed media and intentional decoration may cross that boundary, but readable copy may not.
  • Anchor independent text and evidence regions to shared grid lines or normal layout flow. Do not position two absolutely placed regions from guessed top values when either region can wrap. Measure the rendered boxes after fonts load and require positive separation.
  • Treat figures as protected visual regions: sibling text must not enter their rendered bounds. Mark other independently positioned diagrams, process rows, charts, and card groups with data-no-text-overlap; reserve data-text-overlap="allow" for deliberate, contrast-checked text-on-media compositions.
  • Keep slide-edge safety margins and account for renderer differences. A slide is not finished until rendered bounding-box QA reports no text-safe-area violation, text collision, clipping, or out-of-bounds content.

7. Produce content and visuals together

  • Write audience-facing copy, not production notes.
  • Prefer concise claim titles and concrete language.
  • Separate slide copy from speaker notes and leave-behind detail.
  • Match density to delivery mode: speaking, hybrid, or reading.
  • Use native text, charts, tables, and shapes when editability matters.
  • Apply the declared native/raster strategy to every slide. A raster asset may support art direction, but it must not contain the only version of critical copy, chart values, source labels, or accessibility meaning.
  • Use generated or searched imagery only when it adds specific explanatory or emotional value.
  • Honor an explicit preference for sourced photography over generation. Search rights-compatible libraries, select by subject and crop fitness, download the chosen asset locally, record its source page and license, and never depend on a remote hotlink during delivery.
  • For usable and user-approved generation capability, create only assets approved by the resolved generation decision, inspect every result, and preserve prompt/provenance. Add selected outputs to asset-manifest.json when planning artifacts are enabled.
  • Give each generated visual a unique slide job. Use one hero once by default; generate a different asset for a different subject, crop, or narrative job.
  • Keep actual logos, people, product UI, data, charts, tables, diagrams, and exact labels sourced or native. Generated art is not factual proof.
  • Reference attached media by stable asset_ids; specify placement intent, crop mode, focal anchor, alt text, and fallback behavior in the deck plan.
  • Preserve per-asset invariants: logos and assets with crop_tolerance: none use only contain or none; use native zoom/callout treatments instead of destructive cropping.
  • Use a table for exact lookup, a graph for patterns, and a diagram for relationships or process.
  • Select charts by the question being answered, not by visual novelty.
  • Give sources near the claim or in notes according to the delivery contract.

8. Build through an owned intermediate representation

Keep the internal planning model and actual authoring source aligned. When contract files are enabled, brief.json, source-ledger.json, and deck-plan.json are durable sources of truth. Never use rendered PNGs as the editable source.

When generating PPTX:

  • create native editable elements where practical;
  • preserve theme, master, or placeholder constraints when required;
  • include speaker notes when the runtime supports them;
  • avoid hidden dependencies on remote services;
  • make output deterministic enough to repair individual slides.

When generating native web slides:

  • use a fixed stage, not responsive page flow, for slide composition;
  • read html-presenter-console.md and scaffold the dependency-free console when a presentation-product experience is useful;
  • provide thumbnails, keyboard navigation, chrome-free presentation mode, speaker notes, safe display controls, and honest export actions;
  • center the transformed fixed stage explicitly with absolute 50%/50% positioning plus translate(-50%, -50%) scale(...); do not rely on grid centering for an oversized unscaled canvas;
  • keep console chrome outside the slide canvas and outside PDF/PPTX exports;
  • bind authored media to the plan: use data-asset-id on rendered assets, data-poster-asset-id for a separately planned video poster, and background.assetId for runtime-created background media;
  • test export separately from browser display.
bash
python scripts/scaffold_presenter.py --output <project-dir>/presenter --deck-plan <project-dir>/deck-plan.json --design-system <project-dir>/design-system.json

The scaffold is an authoring start, not a deliverable. Replace each slide's body, clear every authoring.incomplete flag, and run visual QA.

When PPTX + HTML presenter is selected, do not duplicate the slide layout in HTML. Render the final PPTX to ordered slide images, then build the presentation-console preview:

bash
python scripts/build_pptx_preview.py --output <project-dir>/presenter --slides-dir <project-dir>/renders/slides --deck-plan <project-dir>/deck-plan.json --title "<deck title>" --force

This render-parity preview must use the final post-repair PPTX renders. Rebuild it after every PPTX change. Use native HTML authoring only when the user explicitly needs web-native interaction, editable HTML slide content, or runtime-specific behavior.

When Chromium and Node.js are available, run the portable browser check:

bash
node scripts/qa_html_presenter.mjs --entry <project-dir>/presenter/index.html --deck-plan <project-dir>/deck-plan.json --count <slide-count> --output <project-dir>/qa/html-presenter.json --render-dir <project-dir>/renders/slides --montage <project-dir>/renders/slide-montage.png --console-shot <project-dir>/renders/console/presenter-console.png

It checks fixed-stage geometry, presenter-viewport stage fit, plan-to-DOM asset identity, image/video contracts, placeholders, out-of-bounds elements, clipped content, outer text-safe-area violations, independent text-block collisions, text entering protected media/layout regions, title lines, body text size, suspicious text encoding, thumbnails, console and interactions, deep links, print geometry, draft status, and inspector-toggle geometry. Exhaustive reference coverage additionally activates topology/layout/tone/density diversity and reference-to-DOM trace gates. It does not replace visual review.

9. Render, inspect, repair

Read qa.md. Rendering is mandatory for created or edited decks.

  1. Run structural validation and resolve all blocking errors.
  2. Render every slide/page to an image.
  3. Inspect the full montage for rhythm, repetition, pacing, and outliers.
  4. Inspect every slide at full size for overlap, clipping, wrapping, contrast, alignment, image crop, chart semantics, citation fit, and empty placeholders.
  5. Repair the authoring source, re-export, and re-render affected slides.
  6. Repeat until no blocking issue remains. Do not declare success from code inspection alone.

For reference-led work, also read reference-benchmarking.md. Populate reference-benchmark.json for HTML candidates and apply the same scorecard to PPTX renders: image staging, material/edge language, type-role contrast, slide-family rhythm, native fidelity, and anti-copy integrity. PNG renders are QA evidence; they are never the editable source.

Before changing an existing benchmark candidate, read iteration-versioning.md and freeze the current candidate. Never overwrite the only inspectable evidence for an earlier tuning round. Label retrospective or different-input artifacts honestly; only same-prompt, same-content, same-capability runs qualify for direct V-to-V scoring.

Distinguish representative cluster sampling from proof against every supplied reference. If the user explicitly requires the baseline to account for every reference, declare coverage.mode as mapped-all-references or build a dedicated one-slide-per-reference stress fixture. Every reference then needs a unique, inspectable mapping to an original HTML response with an in-range slide ID, individually specific response text, and passing status. Bind referenceId or referenceIds in the slide data so browser QA can trace the mapping to rendered DOM. Do not report cluster sampling as exhaustive coverage, and do not embed the source reference merely to satisfy the mapping.

bash
python scripts/validate_reference_benchmark.py <project-dir>/reference-benchmark.json --require-final

Do not let structural completeness or an impressive cover conceal weak visible craft. A final HTML benchmark needs every universal criterion at or above its declared floor and no unresolved blocker.

Once the benchmark contract is populated, rerun the complete HTML loop with:

bash
python scripts/run_html_benchmark.py --project <project-dir> --entry presenter/index.html --count <slide-count>

If rendering or image understanding is unavailable, do not counterfeit this loop. Record an external review gate and deliver a draft/plan rather than claiming final visual QA.

For PPTX package inspection:

bash
python scripts/inspect_pptx.py <deck.pptx> --json <project-dir>/qa/pptx-structure.json

Record results in qa-report.json. A maximum repair count is a scheduling aid, not permission to ship a broken deck.

10. Deliver clearly

The user-facing handoff contains only:

  • the selected primary deliverable;
  • the companion presenter index.html when that option was selected.

Keep QA reports, optional contracts, ledgers, manifests, render folders, source files, and benchmark evidence internal unless the user asks for them or a material limitation requires disclosure. Open with the selected deliverable(s) and avoid process narration. Add at most one short limitation sentence when necessary.

Reference router

NeedRead
Select intent, arc, density, and closing actionintent-playbooks.md
Research, evidence, claims, story, titles, notescontent-and-narrative.md
Grids, typography, color, imagery, charts, diagramsdesign-and-visuals.md
Turn visual references into material, edge, image, rhythm, and native-PPTX reconstruction decisionsart-direction-and-pptx-reconstruction.md
Select product-specific typography, brand voice, scale, roles, and fallbackstypography-and-voice.md
Choreograph photos and product media by intent without falling into repeated slotsmedia-composition.md
Select topology, layout roles, safe slots, variation, and visual-quality floortopology-and-layouts.md
Analyze and place attached photos, video, logos, screenshots, and illustrationsmedia-intelligence.md
Activate image/video generation, uniqueness, prompt packets, provenance, and reviewgenerative-visuals.md
Negotiate LLM, vision, generation, delegated-model, authoring, and QA capabilitiescapability-orchestration.md
Build a fixed-stage HTML slide product with thumbnails, controls, notes, Present, and exporthtml-presenter-console.md
Benchmark rendered HTML against supplied visual references without cloningreference-benchmarking.md
Freeze tuning iterations and build honest V-to-V comparisonsiteration-versioning.md
Select runtime, preserve editability, export and packageruntime-and-delivery.md
Define or inspect JSON work productsartifact-contracts.md
Run content, visual, technical, and accessibility QAqa.md

Non-negotiable acceptance criteria

  • The deck's communication job is explicit.
  • The primary intent and audience shift are visible in the structure.
  • Every substantive slide has one job, one claim, and adequate proof.
  • Titles communicate takeaways except for documented slide-role exceptions.
  • Facts are sourced or clearly labeled as assumptions/scenarios.
  • The visual system is coherent without making every page identical.
  • The design system includes a concrete visual thesis, material language, edge language, image system, slide-family rhythm map, and native/raster reconstruction strategy that fit this product and audience.
  • Typography expresses the specific product and audience rather than merely its broad category; family, scale, weight, tracking, case, color, and fallback choices have an explicit rationale.
  • Media placement expresses the slide job and product character; crops, masks, scale, sequencing, and image-to-copy relationships do not collapse into a repeated photo slot.
  • Audience-facing slides contain no internal reference, audit, source-credit, crop, topology, or generation labels unless explicitly required for the audience.
  • Every slide uses an explicit relationship, topology, registered/custom layout, safe slot budget, tone, and focal emphasis.
  • The deck meets its intent-relative supplied-reference quality floor and passes thumbnail/squint tests.
  • A reference-led PPTX is judged from rendered pages, not code alone: silhouette, focal mass, type-role contrast, material depth, image staging, card discipline, rhythm, native editability, and truth boundaries all pass review.
  • A reference-led HTML project has a final reference-benchmark.json; every universal criterion includes render evidence, meets its floor, and has no blocker. When exhaustive reference coverage was requested, every supplied reference also has a validated passing coverage mapping in the declared mode.
  • Every modified tuning candidate has an immutable predecessor snapshot, an iteration manifest, and an explicit comparability status.
  • Image/video generation availability produced a resolved use, skip, unavailable, or delegated decision; every generated asset has a unique job, provenance, and visual review.
  • Every used attachment has reviewed semantics, rights, placement, crop, focal anchor, safe region, fallback, and alt text; strict multi-asset slides resolve these per asset.
  • Copy fits the delivery mode and remains readable.
  • Core content remains editable unless the user accepted another trade-off.
  • Every slide was rendered and visually inspected.
  • Capability-dependent work was performed only by verified or explicitly delegated capabilities; fallbacks and external review gates are disclosed.
  • The final artifact opens, contains no placeholders, and matches the requested format.
  • Delivery matches the user's selected route. If no route was provided, the fallback is an editable PPTX plus a render-parity HTML presenter preview built from that final PPTX.
  • Brief, evidence, design-system, topology, and visual-generation logic were completed even when their JSON/Markdown artifacts were not emitted.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pradaslides AI skill do?

Create, redesign, critique, or plan audience-ready presentations and PowerPoint decks from a short prompt or source material. Use for portfolios, work/result reviews, proposals, sales decks, investor pitches, strategy or decision decks, research and technical talks, lessons, keynotes, reports, product launches, and reusable presentation templates. Infers user intent, audience, desired change, narrative, evidence needs, visual direction, delivery mode, and the best available slide runtime. Uses an adaptive, low-friction workflow: planning contracts may remain internal, visual generation foll...

Why use Pradaslides on TypingMind?

Because you install it once and use it with any model. Pradaslides is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pradaslides in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/studio-yebisu/yaoyorozu-ai-toolbox-slides/tree/main/examples/repositories/pradaslides/source/upstream. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pradaslides?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pradaslides?

As many as you like. As long as a model supports skills, you can use Pradaslides with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pradaslides AI skill free?

It is published on GitHub by studio-yebisu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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