slide-skills 0.2.22

AI slide generation skills — fill .pptx/SVG templates with AI text, images, themes, and animations

Slide Generator Lab

AI slide generation in Python. Take a template (a Canva/PowerPoint .pptx, or a hand-designed SVG collection), and generate a finished deck whose text, colors, images, and animations are produced by AI to fit a topic — while the layout stays exactly as designed.

Built as composable skills so the whole thing can be wired into a FastAPI app. Published on PyPI as slide-skills.

---

Install

As a library (use in any project):

pip install slide-skills                 # latest release from PyPI
# or, the bleeding edge straight from source:
pip install "git+https://github.com/phatgg221/Slide-generator-lab.git"

For development (edit the code, changes apply instantly):

git clone https://github.com/phatgg221/Slide-generator-lab.git
cd Slide-generator-lab
python3 -m venv .venv && source .venv/bin/activate
pip install -e .                         # editable install

Optional extras:

pip install "slide-skills[svg-convert]"  # PyMuPDF, for .pptx -> SVG conversion
pip install "slide-skills[all]"          # everything optional

Requirements

What	Needed for	Notes
OPENAI_API_KEY	any AI step (text, images, planning)	put in env or a .env file
Python ≥ 3.9	everything
resvg-py (auto-installed)	rendering SVG/web decks	bundled, no system deps
TAVILY_API_KEY + pip install "slide-skills[search]"	live web research	optional; without it, research falls back to OpenAI web-search then model knowledge
LibreOffice	svg_template_maker only (.pptx → SVG)	brew install --cask libreoffice; optional

Environment variables (all optional):

OPENAI_API_KEY=sk-...            # required for AI calls
OPENAI_TEXT_MODEL=gpt-4o         # default
OPENAI_IMAGE_MODEL=gpt-image-1   # skip image-model auto-detection
TAVILY_API_KEY=tvly-...          # enables Tavily web search (pip install "slide-skills[search]")
SLIDE_TEMPLATES_DIR=/path/to/svg/templates   # where your SVG collections/categories live
SLIDE_LIBRARY_DIR=/path/to/pptx/templates    # where your .pptx templates live
SLIDE_GUIDES_DIR=/path/to/guides             # design-taste markdown injected into prompts

The two *_DIR vars are the key to using this as an installed package: set them once and the library finds your templates wherever you keep them — your designs live outside the code package.

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Two paths

The project supports two delivery targets that share most of the same skills:

	PPTX path	Web path (current focus)
Output	Downloadable PowerPoint file	Self-contained animated HTML for your website
Template source	Canva/PowerPoint .pptx	Hand-designed SVG collections (Figma/Inkscape)
Animation	PowerPoint transitions + entrance effects	Native SVG/CSS animation (Canva-like)
Main CLI	examples/build_course_deck.py	examples/web_deck.py

The web path is preferred because SVG keeps text editable, colors remappable, and animations playable in the browser — and it needs no desktop renderer.

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How it works (agent flow)

A deck is produced by a chain of focused agent steps. You can run the whole chain (topic → deck) or jump in at any stage (e.g. supply your own plan).

  topic / brief                          templates on disk (SVG collections
       │                                  or category folders + category.json)
       ▼                                            │
  ┌─────────────┐                                   │
  │ 1. Keywords │  GPT-4o pulls the core topics     │
  └─────────────┘                                   │
       ▼                                             │
  ┌─────────────┐  Tavily → OpenAI web_search →     │
  │ 2. Research │  model-knowledge. Facts, stats,   │
  └─────────────┘  sources (grounded brief)         │
       ▼                                             │
  ┌─────────────┐  GPT-4o decides slide count,      │
  │ 3. Plan     │  categories/types, order, theme ◄─┘ (knows what templates exist)
  └─────────────┘
       ▼
  ┌──────────────────┐  per slide: schema-fit shortlist + GPT-4o
  │ 4. Pick variant  │  tiebreak → best design for the data
  └──────────────────┘  (category library only)
       ▼
  ┌─────────────┐  GPT-4o writes each {{placeholder}} within its
  │ 5. Write    │  character budget (lists for multi-line slots)
  └─────────────┘
       ▼
  ┌─────────────┐  contrast-safe recolor to the chosen palette
  │ 6. Theme    │  (AI-picked, preset, custom, or keep original)
  └─────────────┘
       ▼
  ┌─────────────┐  optional: gpt-image-1 photos OR GPT-4o SVG
  │ 7. Images   │  vector art (~5× cheaper) into image slots
  └─────────────┘
       ▼
  ┌─────────────┐  fill SVGs → self-contained animated HTML deck
  │ 8. Assemble │  (or .pptx). Returns output path + usage (tokens/$)
  └─────────────┘

Step	Agent / skill	Model / backend	Optional?
1 Keywords	extract_keywords	GPT-4o	no
2 Research	web_research	Tavily › OpenAI web_search › model	yes (research)
3 Plan	plan_deck	GPT-4o	skip if you pass a plan
4 Pick variant	select_and_fill_slide	schema-fit + GPT-4o	category libraries only
5 Write	generate_deck_content	GPT-4o	no
6 Theme	theme.py	pure code (+ optional GPT-4o palette)	yes
7 Images	generate_image / generate_svg_image	gpt-image-1 / GPT-4o	yes
8 Assemble	html_deck / svg_slide_renderer	pure code	no

Two ready-made entry points bundle these steps:

• generate_web_deck(collection, brief, …, research=True) — the full flow over one collection: (1·2 with research=True) ·3·5·6·(7)·8.
• generate_deck_from_plan(plan, library_dir, …) — steps 4·5·6·8 where your plan already names a category per slide and the agent picks the variant.

Every entry point returns a usage block (tokens + estimated USD) for the run.

Per-agent docs: each step has an "agent card" in agents/ — role, input/output, model/backend, and decision logic (e.g. how the researcher searches the web, how the category→variant mapper picks a design).

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Use as a Python library

Once installed, import the skills from anywhere:

from slide_skills import generate_web_deck

# Topic -> animated HTML deck (writes the file, returns a summary dict)
result = generate_web_deck(
    collection="starter",                 # folder under SLIDE_TEMPLATES_DIR
    brief="Khóa học nhập môn Machine Learning",
    output_path="out/deck.html",
    palette="teal",                       # "auto" | preset name | (primary, secondary, accent) | None
    language="Vietnamese",
    animation="rise",                     # rise | fade | scale | none
)
print(result["output_path"], result["slides"])
print(result["usage"]["report"])          # tokens + estimated USD for this call

Every generate_* call returns a usage block with the total cost of that run:

result["usage"]   # {input_tokens, output_tokens, total_tokens, requests,
                  #  estimated_cost_usd, report}

Category library + variant-selecting agent (your plan names categories; the agent picks the best design variant per slide):

from slide_skills import generate_deck_from_plan, scan_template_library

lib = scan_template_library("templates")     # discover categories + variants
print(lib.category_map())                     # registry for a UI / planner

plan = {"title": "ML 101", "slides": [
    {"category": "Title Slide", "topic": "Intro to Machine Learning"},
    {"category": "KPI & Big Numbers", "talking_points": ["78%", "3x", "12M"]},
    {"category": "Conclusion & Summary", "talking_points": ["Recap", "Next steps"]},
]}
generate_deck_from_plan(plan, "templates", "out/deck.html", palette="auto", language="Vietnamese")

From an editor document (ProseMirror/TipTap JSON) — parse → infer a category per section → optionally research → theme → variant-fill → deck:

from slide_skills import generate_deck_from_document

result = generate_deck_from_document(
    editor_doc,                 # {"type":"doc","content":[heading, paragraph, bulletList, ...]}
    "templates",                # your category library
    "out/deck.html",
    palette="auto",             # agent picks the theme from the content
    research=True,              # enrich sections with web facts first
    images=True,                # fill <image> slots; image_source "svg" (cheap) or "ai"
    language="Vietnamese",
)
print(result["plan"])           # the inferred category-per-slide plan
print(result["usage"]["report"])

Add a collection at runtime (e.g. a user uploads a Figma export):

from slide_skills import import_collection
import_collection("/path/to/figma_export_folder", "my_style")   # copies + validates

Track cost of any run:

from slide_skills import usage_tracker
before = usage_tracker.snapshot()
# ... generate ...
print((usage_tracker.snapshot() - before).report())

Per-user API keys (multi-tenant service — each user supplies their own OpenAI/Tavily key): wrap calls in use_keys. It's async-task-local, so keys never leak across concurrent requests. Without it, keys come from the env.

from slide_skills import use_keys, generate_web_deck

with use_keys(openai_key=user_openai_key, tavily_key=user_tavily_key):
    generate_web_deck("starter", topic, "out/deck.html")

In a FastAPI app, wrap blocking calls in a thread and scope keys per request:

import asyncio
from fastapi import FastAPI
from fastapi.responses import FileResponse
from slide_skills import generate_web_deck, use_keys

app = FastAPI()

def _run(topic, collection, openai_key):
    with use_keys(openai_key=openai_key):       # set inside the worker thread
        return generate_web_deck(collection, topic, "out/deck.html")

@app.post("/generate")
async def generate(topic: str, openai_key: str, collection: str = "starter"):
    await asyncio.to_thread(_run, topic, collection, openai_key)
    return FileResponse("out/deck.html", media_type="text/html")

(Set SLIDE_TEMPLATES_DIR so the app finds your collections. Set keys inside the thread/worker that runs the generation, not only on the request coroutine.)

---

Quick start (CLI) — Web deck from an SVG collection

# 1. See available collections
.venv/bin/python examples/web_deck.py list

# 2. Validate a collection — what placeholders did it find? (free, offline)
.venv/bin/python examples/web_deck.py check starter

# 3. Visual preview with stub text (free, offline)
.venv/bin/python examples/web_deck.py demo starter
open out/starter_demo.html

# 4. Generate a real deck from a topic (~$0.02)
.venv/bin/python examples/web_deck.py generate starter \
    "Khóa học nhập môn Machine Learning" -o out/ml_deck.html --language Vietnamese
open out/ml_deck.html

In the browser deck: →/← to navigate, f for fullscreen, elements animate in as each slide appears.

Options: --palette teal (force a theme), --animation rise|fade|scale|none, --research (run keyword extraction + web search first — the full flow), --language Vietnamese.

---

Designing your own SVG collections

You design collections once in Figma/Inkscape; every generated deck reuses them. A collection is a folder of slide-type SVGs sharing one visual style:

svg_templates/<collection>/
  collection.json     # optional: description, palette, fonts, tags
  title.svg           # filename = slide type the planner picks from
  statistic.svg
  comparison.svg ...

Rules (see svg_templates/README.md for the full guide):

• Export SVG with "Outline Text" UNCHECKED — text must stay live <text>.
• Placeholders are the text content: {{title}}, {{quote|120}} (120-char budget), {{body.1}} / {{body.2}} (multi-line).
• One uniform style per placeholder (don't bold half a word — it splits the text).
• Name files by function (title, statistic, quote…); use common fonts.

Then web_deck.py check / demo your folder before spending on generation.

---

Tuning the AI's taste (design guides)

The agents' design sense lives in editable markdown, injected into the prompts at runtime — change the deck's aesthetic without touching code:

guides/
  color_theme.md   # palette principles, contrast rules, topic→palette table
  style.md         # deck structure, layout choice, writing style, what to avoid

• color_theme.md → injected into propose_palette and plan_deck
• style.md → injected into plan_deck, the SVG content writer, and the variant-selecting agent

Edit a guide and the next generation follows the new rules — no rebuild. Guides are optional (a missing file is simply ignored). Point SLIDE_GUIDES_DIR at a different folder to swap in a per-brand guide set. Keep them crisp — every line costs prompt tokens on each call.

---

Category library + variant-selecting agent

For richer decks, organize templates into categories, each holding several variant designs for the same layout function. The user's plan names a category per slide; an agent picks the best-fitting variant for the data.

templates/
  TITLE_SLIDE/
    category.json        # optional: descriptions guiding variant choice
    centered.svg         # variants — multiple designs, same purpose
    left_aligned.svg
  KPI_BIG_NUMBERS/
    category.json
    three_stats.svg
    list_style.svg

category.json (optional but recommended — it's the "map" the agent reads):

{
  "description": "Highlight key metrics with large, scannable numbers.",
  "variants": {
    "three_stats": "Three stat callouts — use for 2-3 key numbers.",
    "list_style":  "A vertical list — use for 4+ numbers or rankings."
  }
}

How a variant gets chosen, per slide:

1. schema-fit shortlist (code) — keep variants whose slot count fits the data
2. AI tiebreak (GPT-4o) — read each finalist's description + slots and the slide content, pick the best, and write its text

Adding designs is pure data:

• New variant → drop a .svg in the category folder (instantly usable; add a category.json line so the agent knows when to choose it).
• New category → make a new folder; it appears in category_map() automatically.

Category names match plan labels ignoring case/spaces/&/-/_ ("KPI & Big Numbers" → KPI_BIG_NUMBERS), but not plurals — name folders to match your plan labels.

Optional per-variant schema (richer field specs)

By default the AI receives each slot's name + budget, inferring meaning from the name. To make generation more accurate, drop a <variant>.schema.json (or <variant>_schema.json) next to the SVG describing each field:

{
  "fields": {
    "heading": {"type": "title",  "desc": "Section heading"},
    "stat_1":  {"type": "number", "desc": "First key metric, e.g. 78%"},
    "label_1": {"type": "text",   "desc": "What stat_1 measures"}
  }
}

The AI then knows a field is a number vs a label (fixing empty/wrong stat cards). Budgets (max_chars/lines) still come from the SVG, so you never hand-maintain them. Entirely optional — without it, the spec is derived from the SVG as before. The AI reads this small spec, never the SVG markup.

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Quick start — PowerPoint deck from a .pptx template

# Ingest any .pptx into the reusable template library (cleans junk, classifies slides)
.venv/bin/python examples/prepare_template.py "~/Downloads/My Design.pptx" my_template

# See what's editable (free, offline)
.venv/bin/python examples/test_template.py my_template

# Full pipeline: research -> plan -> write -> images -> theme -> animate
.venv/bin/python examples/build_course_deck.py library/my_template.pptx \
    "your topic" --transition fade --animate fade -o out/deck.pptx

Cost controls: --no-research, --no-images, --svg-images (cheap vector illustrations instead of AI photos).

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Skills reference (slide_skills/)

Foundation

• config.py — OpenAI client + model config from .env
• usage.py — token & cost tracking across all AI calls (usage_tracker)
• template_parser.py — parse a .pptx into a fill-spec; classify text roles, char budgets; skip tip-bubbles & navigation buttons

Research → Plan → Write

• research.py — extract_keywords, web_research
• planner.py — plan_deck: AI picks slide count, types, order, theme
• content_generator.py — generate_content: AI writes budget-aware text
• agent.py — SlideGeneratorAgent: fill one template from a brief
• pipeline.py — CourseDeckPipeline: the full chained pipeline

Images

• image_generator.py — generate_image: AI photos, auto-detects account's model
• svg_image_generator.py — generate_svg_image: GPT-4o vector art (~5× cheaper)

Filling & assembly

• slide_filler.py — write text keeping formatting, auto-shrink overflow, swap images
• assembler.py — build a deck by picking/reordering/repeating library slides

Templating

• template_maker.py — prepare_template: ingest + clean + AI-classify a .pptx
• merge_template.py — {{placeholder}} form + schema; AI fills; render
• svg_template_maker.py — .pptx → folder of live-text SVGs (needs LibreOffice/PowerPoint)

Theme & motion

• theme.py — contrast-safe recoloring, 8 presets, propose_palette
• transitions.py — PowerPoint slide transitions
• animations.py — PowerPoint element entrance animations

Web decks

• svg_collections.py — scan collections, fill placeholders, retheme, import_collection, generate_web_deck
• svg_categories.py — category library + variant-selecting agent: scan_template_library, select_and_fill_slide, generate_deck_from_plan
• html_deck.py — build a self-contained animated HTML presentation
• svg_slide_renderer.py — filled SVGs → PNG → .pptx export

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Command-line tools (examples/)

Command	Purpose
web_deck.py	SVG collections → animated web deck (list/check/demo/generate)
build_course_deck.py	Full pipeline → .pptx
generate_deck.py	Fill one template from a brief
prepare_template.py	Ingest a .pptx into the template library
test_template.py	Dry-run marker fill — see what's editable (free)
recolor_deck.py	Re-theme an existing deck's colors
merge_deck.py	{{placeholder}} workflow (make/render/generate)

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What can be customized per deck

• Words — every {{placeholder}} is AI-written, any language
• Colors — preset, AI-picked, or custom; always contrast-safe
• Animation — rise / fade / scale / none (web) or PowerPoint effects (pptx)
• Slides — the planner chooses which template types to use, and their order

Fixed by design: your layout, and (for now) fonts.

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Tests (offline, no API key)

.venv/bin/python tests/test_offline_pipeline.py   # parse / fill / image swap
.venv/bin/python tests/test_assembler.py          # library assembly

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Known limits

• Canva exports lose animation and live text. .pptx from Canva is static; Canva SVG outlines all text. Design real SVG templates in Figma/Inkscape.
• svg_template_maker.py needs a renderer. Install LibreOffice (brew install --cask libreoffice) for headless, server-ready conversion; the desktop-PowerPoint fallback is fragile.
• Charts aren't data-driven yet. Chart-style slides render as designed art, not recomputed from numbers.
• FastAPI app is a later milestone; all skills are import-ready for it.

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Releasing a new version (maintainers)

# 1. bump version in BOTH pyproject.toml and slide_skills/__init__.py
# 2. rebuild fresh and publish
rm -rf dist && python -m build
twine upload dist/*          # username: __token__   password: your pypi-... token
# 3. tag it
git tag v0.2.22 && git push origin main --tags

PyPI versions are permanent — never reuse a number; bump to the next one.