agentsclimarketplace

Pdf onepager

Skill FeedbackPulseApp/hr-skills/skills/fp/pdf-onepager

Employee Experience - Human Resources AI Agent Skills

Install
npx -y skills add FeedbackPulseApp/hr-skills --skill pdf-onepager

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when creating a printable PDF one-pager, lead magnet, worksheet, survey template, cheat sheet, or downloadable framework asset for ANY organization. Generates brand-aligned, print-safe, AI-citation-ready TAGGED PDFs (US Letter + A4) from HTML/CSS via WeasyPrint, with a setup script, a render driver, and a QA verifier. Ships a neutral default plus example brand presets. Triggers: "make/build/generate/create a PDF", "downloadable template", "printable worksheet", "PDF lead magnet", "one-pager PDF", "PDF cheat sheet".

SKILL.md

8.0 KB, ~2.1k tokens by cl100k_base, as published. Nobody here has run it

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)

cd <skill-dir>        # 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

cd <skill-dir>

# 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 <stem>.pdf (Letter), <stem>-a4.pdf (A4), and <stem>-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/<your-slug>.html
  2. Set the metadata in <head> — <title>, <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:

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.

Keep looking

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