daenet-scientific

name

daenet-scientific

description

Creates a professional daenet TechTalk-branded two-column scientific article PDF from a Word document (.docx) and a cover image. Use this skill whenever the user wants to produce a scientific or technical magazine-style PDF in daenet style, convert a DOCX to a formatted daenet article, create a two-column article PDF with a cover page, or generate a professional publication from a Word document. Trigger even when the user says things like "make it look like a daenet article", "format this as a TechTalk PDF", "create a professional PDF from my doc", or "produce a magazine-style article". The output is always a polished PDF saved to the user's connected folder.

daenet Scientific Article PDF

Turn a Word document into a professional daenet TechTalk two-column scientific article PDF -- cover page, running headers/footers, code boxes, and full daenet branding -- in one workflow.

What you produce

• Page 1: Full-bleed cover image with a translucent dark-blue title band (article title, subtitle, author, year, "daenet -- an ACP Digital company" strip)
• Page 2+: daenet-branded two-column layout
  • Full-width header zone: article title, subtitle, author/affiliation, abstract, keywords
  • Two-column body: numbered sections, subsections, body text, styled code boxes
  • Running header (journal title left / article title right) and footer (page number, copyright)
• Final section: Contributions block (if provided)

Workflow

Step 1 -- Gather inputs

You need four things. Ask for any that are missing:

1. DOCX file -- the article text. Read it with python-docx.
2. Cover image -- The official daenet TechTalk cover is bundled at assets/techtalk-cover.jpg. Use it as the default cover unless the user explicitly provides a different image.
3. Article metadata -- title, subtitle (optional), author name(s), affiliation line, year (default 2026).
4. Output path -- where to save the PDF (default: user's connected folder).

To find the skill's own directory (so you can reference assets/techtalk-cover.jpg), locate the SKILL.md file:

find /sessions -name "SKILL.md" -path "*daenet-scientific*" 2>/dev/null | head -1

The cover image is at <skill-dir>/assets/techtalk-cover.jpg.

If the user provides a custom cover image inline in the conversation, it will not be saved to disk automatically. Extract it from the conversation JSONL:

import json, base64
# find with: find /sessions -name "*.jsonl" -path "*projects*" | head -1
images = []
with open(jsonl_path) as f:
    for line in f:
        try: obj = json.loads(line)
        except: continue
        def collect(o, depth=0):
            if depth > 15: return
            if isinstance(o, dict):
                if o.get("type") == "image" and o.get("source", {}).get("type") == "base64":
                    images.append(o["source"])
                for v in o.values(): collect(v, depth+1)
            elif isinstance(o, list):
                for v in o: collect(v, depth+1)
        collect(obj)
best = max(images, key=lambda s: len(s.get("data", "")))
with open("cover_custom.jpg", "wb") as f:
    f.write(base64.b64decode(best["data"]))

Step 2 -- Extract article content from the DOCX

Install python-docx if needed (pip install python-docx --break-system-packages -q), then read every non-blank paragraph. Map paragraphs to content types:

Heuristic	Type
Starts with a digit and a period or space, OR all-caps short line	"section"
Starts with a digit.digit	"subsection"
Paragraph style contains "Heading"	"section" or "subsection"
Otherwise	"body"

Look for an "abstract" paragraph (often a short italic paragraph near the top, or labelled "Abstract"). Look for a "keywords" line. Identify any code-like blocks (monospace runs, or lines with Assert., {, }, //).

Build a content.json list:

[
  {"type": "abstract",  "text": "..."},
  {"type": "keywords",  "text": "Keywords: ..."},
  {"type": "section",   "text": "1.  Introduction"},
  {"type": "body",      "text": "..."},
  {"type": "codebox",   "lines": ["line1", "line2"], "caption": "Listing 1 - ..."},
  {"type": "contributions", "text": "Authors. Affiliation note."}
]

Important: Use only ASCII characters in codebox lines. Replace:

• Unicode symbols like theta, middle-dot, double-bar, minus-sign, arrow with plain ASCII equivalents: a, ., ||, -, -> Courier (the codebox font) does not support Unicode and will render black boxes.

Step 3 -- Run the builder script

The bundled script at scripts/build_pdf.py does all the ReportLab work. Install reportlab first if needed (pip install reportlab --break-system-packages -q).

python scripts/build_pdf.py \
  --title   "Full Article Title" \
  --subtitle "Optional subtitle line" \
  --author  "First Last" \
  --affiliation "Organisation - City - email@domain" \
  --cover   <skill-dir>/assets/techtalk-cover.jpg \
  --content /path/to/content.json \
  --output  /path/to/output.pdf \
  --year    2026

Step 4 -- Present the PDF

Save the PDF to the user's connected folder and use present_files to share it.

Layout constants (for reference / debugging)

• A4: 595 x 842 pt
• Side margins: 18 mm; top: 22 mm; bottom: 20 mm
• Column gap: 6 mm -> each column ~246 pt wide
• Header zone height (first content page): 104 mm
• daenet blue: #003DA5; accent: #00AEEF; code bg: #EEF3FA

Common issues

Symptom	Fix
Black boxes in code boxes	Non-ASCII chars in lines -- replace with ASCII
Header zone content overflows	Reduce abstract length or shrink HDR_H in the script
Cover image not found	Check assets/techtalk-cover.jpg relative to SKILL.md
Text crosses column boundary lines	Header/footer rules drawn by on_content()