`, `<meta name="author|description| keywords|generator">`, and `<html lang>`. WeasyPrint reads these into the PDF properties; they are how AI engines identify the document. Do not leave blank. 3. **Set the running-footer URL** — the one per-document override: `body { string-set: docsource "yourdomain.com/the-page"; }`. Put the same URL in the masthead link and the `.docfoot` so the citation appears three times. 4. **Use the components in `styles/base.css`**: `.definition` (tinted intro), `.framework`/`.col` (equal columns with rules), `.callout-key` (the single loudest element — make it the memorable hook), `.worksheet`/`.fill` (dotted fill-in lines), `.cols2`, `.checklist`, `.page-2` (back of the one-pager). 5. **Render + verify + LOOK.** If page 1 overflows, adjust margins/font-size in the HTML's `<style>` or base.css; WeasyPrint reflows, so don't fight pixels. 6. **Manual AI test (required, can't be automated):** drag the PDF into **both** ChatGPT and Claude and ask *"What is this document and where can I read more?"* The answer must name the topic and surface your source URL. If not, the metadata or footer URL is wrong — fix and re-render. 7. **Ship to a STABLE URL.** Host at `/assets/downloads/<slug>.pdf` (and `-a4.pdf`), publicly (not behind a form). When you update, **replace the file at the same URL** — never version the path, or backlinks and AI citations break. Optionally add `DigitalDocument` JSON-LD on the page that links it. ## Make it your brand Only colors and fonts are brand-specific; layout is shared. To add a brand: ```bash cp styles/brand-template.css styles/brand-acme.css # then fill in colors/fonts ./.venv/bin/python gen.py templates/starter.html --brand acme ``` `brand-template.css` is fully commented. The one rule that matters is **contrast**: `--accent` is your vivid color for fills/rules; `--accent-deep` is for accent *text on white* and must clear 4.5:1; `--callout-bg`/`--on-callout` is the loudest block and must also clear 4.5:1 (if your brand color is too light for white text, use a navy/near-black there — see `brand-enpstools.css`, where teal fails so the callout is navy). Drop brand `.ttf` files into `fonts/` and wire `@font-face` as shown in the template. ## What makes these good (encoded in base.css) - **One loud element.** `.callout-key` is the highest-contrast block; everything else is calm. White background, no full-bleed color, no gradients/shadows — it must read as a tool, not a flyer. - **Brand via presets, never hardcoded.** Every color/font is a CSS variable in `styles/brand-*.css`; `base.css` consumes them. - **Contrast enforced by variable split** (`--accent` vs `--accent-deep`, `--callout-bg`/`--on-callout`) — see "Make it your brand". - **No em dashes in copy.** They read as an AI tell; write them out (period/comma/colon). The templates are already clean. - **Dual page size from one template.** `base.css` sets margins but not size; `gen.py` injects `@page { size: letter | A4 }` per build. ## Gotchas - **Use the venv's python, every time.** System `python3` lacks WeasyPrint; `./.venv/bin/python` has it. The #1 "module not found" cause. - **Native libs are a real dependency.** If `import weasyprint` fails, you are missing the OS packages — re-run `./setup.sh` and follow its install line. On macOS Homebrew, `DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib` resolves rare dylib-lookup failures. - **Fonts are bundled (OFL) in `fonts/`** and embedded+subsetted into the PDF, so output is identical on every machine. To use your own brand font, add the `.ttf` and wire `@font-face` in your brand CSS. Variable fonts work — declare `font-weight: 100 900` on the `@font-face` (see `brand-feedbackpulse.css`). - **`pdf/ua-1` needs a `<title>` and `<html lang>`.** Both are in the templates; deleting them degrades the tagged-PDF output. - **Keep it under 600 KB.** `optimize_images=True` is on; don't embed large rasters. The asset should be email-attachable and fast to crawl. - **Put the key points as text on the download page too**, so AI has the "answer key" without parsing the PDF. ## References - **Example brand presets**: `styles/brand-feedbackpulse.css` (Product Bridge Blue) and `styles/brand-enpstools.css` (navy/teal, the clearest contrast demo). - **Sample output**: regenerate any time with `gen.py` into `out/`. - **Bundled fonts**: `fonts/` with the OFL license text for each family. - **Deeper PDF/UA validation** (optional): run the file through veraPDF for a formal accessibility conformance claim; `verify.py` checks the structural markers (tags, metadata, selectable text), not full PDF/UA conformance.

# PDF Onepager > pdf-onepager - Skill: `feedbackpulseapp/pdf-onepager` (Agent Skill, multi-file: 20 files) - Install (CLI): `npx skillmds@latest add feedbackpulseapp/pdf-onepager` - Raw SKILL.md: https://api.skillmd.com/api/skills/feedbackpulseapp/pdf-onepager/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: FeedbackPulseApp (https://skillmd.com/u/feedbackpulseapp) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/feedbackpulseapp/pdf-onepager --- # pdf-onepager Produces high-craft, **print-safe, AI-citation-ready** one/two-page PDFs from **HTML + print CSS rendered by WeasyPrint** — not a design tool, not a flattened image. You edit a content HTML file; `gen.py` renders it to a tagged PDF in both US Letter and A4 plus a PNG preview; `verify.py` gates the citation/utility checklist before you ship. Why this approach: the asset's whole value is that its **text is selectable, its metadata is set, and its source URL is live in the document** — the citation infrastructure that makes ChatGPT, Perplexity, and Google AI Overview cite it. WeasyPrint emits tagged PDF/UA from clean HTML, and CSS handles the layout math (centering, columns, page breaks) for you. It works for any brand out of the box: a neutral **`default`** preset plus two worked examples (`feedbackpulse`, `enpstools`). Make your own by copying one file. Paths below are relative to the skill directory (where this file lives). ## Setup (one-time) ```bash cd # the folder containing this SKILL.md ./setup.sh # installs native deps hint, builds .venv, smoke-renders ``` `setup.sh` is idempotent. It needs system libraries for WeasyPrint; if they are missing it prints the exact install line for your OS: - **macOS:** `brew install pango cairo gdk-pixbuf libffi poppler` - **Debian/Ubuntu:** `sudo apt-get install -y libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf-2.0-0 libffi-dev poppler-utils` If the skill is installed read-only via the plugin marketplace, copy `gen.py`, `verify.py`, `setup.sh`, `requirements.txt`, `styles/`, `templates/`, `fonts/` into your project (or clone the repo) and run there, so the `.venv` and `out/` have a writable home. ## Run (agent path) — render + verify ```bash cd # Neutral default brand (Atkinson Hyperlegible + indigo), Letter + A4 + preview: ./.venv/bin/python gen.py templates/starter.html # A real example, branded, one flag to swap brand: ./.venv/bin/python gen.py templates/example-sbi.html --brand feedbackpulse # QA against the citation checklist (exit 0 = ship-able): ./.venv/bin/python verify.py out/starter.pdf --source yourdomain.com/your-source-page ``` `gen.py` writes `.pdf` (Letter), `-a4.pdf` (A4), and `-preview.png` (page-1 render) into `--out` (default `out/`). **Open the preview and actually look at it** — `verify.py` checks structure, not whether the layout looks good. `verify.py` FAILs (blocking) on: missing Title/Author/Subject/Keywords/Creator, no `/Lang`, untagged PDF (no StructTreeRoot), any page without selectable text, or the `--source` URL missing from the text. WARNs on file size over 600 KB. ## Authoring a NEW asset 1. **Copy the starter**, keep the structure, replace every `[bracketed]` placeholder: `cp templates/starter.html templates/.html` 2. **Set the metadata in ``** — ``, `<meta name="author|description| keywords|generator">`, and `<html lang>`. WeasyPrint reads these into the PDF properties; they are how AI engines identify the document. Do not leave blank. 3. **Set the running-footer URL** — the one per-document override: `body { string-set: docsource "yourdomain.com/the-page"; }`. Put the same URL in the masthead link and the `.docfoot` so the citation appears three times. 4. **Use the components in `styles/base.css`**: `.definition` (tinted intro), `.framework`/`.col` (equal columns with rules), `.callout-key` (the single loudest element — make it the memorable hook), `.worksheet`/`.fill` (dotted fill-in lines), `.cols2`, `.checklist`, `.page-2` (back of the one-pager). 5. **Render + verify + LOOK.** If page 1 overflows, adjust margins/font-size in the HTML's `<style>` or base.css; WeasyPrint reflows, so don't fight pixels. 6. **Manual AI test (required, can't be automated):** drag the PDF into **both** ChatGPT and Claude and ask *"What is this document and where can I read more?"* The answer must name the topic and surface your source URL. If not, the metadata or footer URL is wrong — fix and re-render. 7. **Ship to a STABLE URL.** Host at `/assets/downloads/<slug>.pdf` (and `-a4.pdf`), publicly (not behind a form). When you update, **replace the file at the same URL** — never version the path, or backlinks and AI citations break. Optionally add `DigitalDocument` JSON-LD on the page that links it. ## Make it your brand Only colors and fonts are brand-specific; layout is shared. To add a brand: ```bash cp styles/brand-template.css styles/brand-acme.css # then fill in colors/fonts ./.venv/bin/python gen.py templates/starter.html --brand acme ``` `brand-template.css` is fully commented. The one rule that matters is **contrast**: `--accent` is your vivid color for fills/rules; `--accent-deep` is for accent *text on white* and must clear 4.5:1; `--callout-bg`/`--on-callout` is the loudest block and must also clear 4.5:1 (if your brand color is too light for white text, use a navy/near-black there — see `brand-enpstools.css`, where teal fails so the callout is navy). Drop brand `.ttf` files into `fonts/` and wire `@font-face` as shown in the template. ## What makes these good (encoded in base.css) - **One loud element.** `.callout-key` is the highest-contrast block; everything else is calm. White background, no full-bleed color, no gradients/shadows — it must read as a tool, not a flyer. - **Brand via presets, never hardcoded.** Every color/font is a CSS variable in `styles/brand-*.css`; `base.css` consumes them. - **Contrast enforced by variable split** (`--accent` vs `--accent-deep`, `--callout-bg`/`--on-callout`) — see "Make it your brand". - **No em dashes in copy.** They read as an AI tell; write them out (period/comma/colon). The templates are already clean. - **Dual page size from one template.** `base.css` sets margins but not size; `gen.py` injects `@page { size: letter | A4 }` per build. ## Gotchas - **Use the venv's python, every time.** System `python3` lacks WeasyPrint; `./.venv/bin/python` has it. The #1 "module not found" cause. - **Native libs are a real dependency.** If `import weasyprint` fails, you are missing the OS packages — re-run `./setup.sh` and follow its install line. On macOS Homebrew, `DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib` resolves rare dylib-lookup failures. - **Fonts are bundled (OFL) in `fonts/`** and embedded+subsetted into the PDF, so output is identical on every machine. To use your own brand font, add the `.ttf` and wire `@font-face` in your brand CSS. Variable fonts work — declare `font-weight: 100 900` on the `@font-face` (see `brand-feedbackpulse.css`). - **`pdf/ua-1` needs a `<title>` and `<html lang>`.** Both are in the templates; deleting them degrades the tagged-PDF output. - **Keep it under 600 KB.** `optimize_images=True` is on; don't embed large rasters. The asset should be email-attachable and fast to crawl. - **Put the key points as text on the download page too**, so AI has the "answer key" without parsing the PDF. ## References - **Example brand presets**: `styles/brand-feedbackpulse.css` (Product Bridge Blue) and `styles/brand-enpstools.css` (navy/teal, the clearest contrast demo). - **Sample output**: regenerate any time with `gen.py` into `out/`. - **Bundled fonts**: `fonts/` with the OFL license text for each family. - **Deeper PDF/UA validation** (optional): run the file through veraPDF for a formal accessibility conformance claim; `verify.py` checks the structural markers (tags, metadata, selectable text), not full PDF/UA conformance.