OpenSpace is a skill-management layer for AI agents that stores, retrieves, evaluates, shares, and improves reusable workflows. It is intended for people using multiple coding agents who want skills to be reused and refined based on task outcomes. The catalogue provides 200 skills for use with OpenSpace and the agents it supports.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/hkuds/openspace/direct-reportlab-pdf-generationnpx skills add HKUDS/OpenSpace --skill direct-reportlab-pdf-generationgit clone --depth 1 https://github.com/HKUDS/OpenSpaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/openspace/direct-reportlab-pdf-generation)<a href="https://agentmods.dev/skills/hkuds/openspace/direct-reportlab-pdf-generation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/direct-reportlab-pdf-generation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01493 |
| Opus 5 | $0.00015 | $0.00746 |
| Sonnet 5 | $0.00006 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
direct-reportlab-pdf-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Direct ReportLab PDF Generation via run_shell
When to Use This Skill
Use this pattern when:
- You need to create a complex, multi-page PDF document with structured content
- Delegating PDF generation to
shell_agentfails or produces unreliable results - You need fine-grained control over PDF layout, styling, and pagination
Core Technique
Instead of asking shell_agent to handle PDF creation, write inline Python code using the reportlab library and execute it directly via run_shell. This gives you deterministic control over the document structure.
Step-by-Step Instructions
Step 1: Prepare Your PDF Content
Organize your content into logical sections that will become pages or page groups:
- Title page
- Table of contents (optional)
- Main content sections
- Appendices or references
Step 2: Write the ReportLab Python Script
Create a Python script that uses these key reportlab components:
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak, Image
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
def create_pdf(filename, content_data):
doc = SimpleDocTemplate(filename, pagesize=letter,
rightMargin=72, leftMargin=72,
topMargin=72, bottomMargin=72)
story = []
styles = getSampleStyleSheet()
# Custom styles
title_style = ParagraphStyle(
'CustomTitle',
parent=styles['Heading1'],
fontSize=24,
textColor=colors.HexColor('#1a1a1a'),
spaceAfter=30,
alignment=TA_CENTER
)
heading_style = ParagraphStyle(
'CustomHeading',
parent=styles['Heading2'],
fontSize=16,
textColor=colors.HexColor('#2c3e50'),
spaceBefore=20,
spaceAfter=12
)
body_style = ParagraphStyle(
'CustomBody',
parent=styles['Normal'],
fontSize=11,
leading=16,
alignment=TA_JUSTIFY
)
# Build document content
for section in content_data:
if section['type'] == 'title':
story.append(Paragraph(section['text'], title_style))
elif section['type'] == 'heading':
story.append(Paragraph(section['text'], heading_style))
elif section['type'] == 'paragraph':
story.append(Paragraph(section['text'], body_style))
story.append(Spacer(1, 12))
elif section['type'] == 'table':
table = Table(section['data'], colWidths=section.get('col_widths'))
table.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTSIZE', (0, 0), (-1, 0), 12),
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
('BACKGROUND', (0, 1), (-1, -1), colors.beige),
('GRID', (0, 0), (-1, -1), 1, colors.black),
]))
story.append(table)
story.append(Spacer(1, 20))
elif section['type'] == 'pagebreak':
story.append(PageBreak())
doc.build(story)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 195 lines · 29 tokens per session scan A b185f70c7752
direct-reportlab-pdf-generation is a skill published in the GitHub repository HKUDS/OpenSpace (7,510 stars, last pushed 24d ago), licensed MIT. It adds 29 tokens to every session and 1,493 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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