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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HKUDS/OpenSpacenpx agentmods add skills/hkuds/openspace/pdf-checklist-workflowWrote 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/pdf-checklist-workflow)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-checklist-workflow"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-checklist-workflow.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 | $0.00025 | $0.02432 |
| Opus 5 | $0.00013 | $0.01216 |
| Sonnet 5 | $0.00005 | $0.00486 |
| Haiku 4.5 | $0.00003 | $0.00243 |
Grade A, and why
pdf-checklist-workflow 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 3d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Checklist/Report Generation Workflow
This skill provides a reusable pattern for creating structured PDF documents such as checklists, reports, or assessments using Python in a sandboxed environment.
Overview
Use this workflow when you need to:
- Generate PDF checklists, reports, or assessments
- Create structured documents with tables, sections, and headers
- Include scoring criteria or evaluation frameworks
- Produce downloadable artifacts for users
Primary method: Use execute_code_sandbox for Python PDF generation
Fallback method: If execute_code_sandbox fails, create and run Python scripts via shell commands
Step-by-Step Instructions
Step 1: Choose a PDF Library
Select a Python library based on your needs:
| Library | Best For | Complexity |
|---|---|---|
reportlab |
Professional reports, complex layouts | Medium |
fpdf / fpdf2 |
Simple documents, quick generation | Low |
matplotlib |
Charts, graphs, visual elements | Medium |
Step 2: Write PDF Generation Code in execute_code_sandbox
Use the execute_code_sandbox tool with Python code that:
- Imports the chosen PDF library
- Defines document structure (title, sections, tables)
- Adds content (text, tables, scores, criteria)
- Saves to a file path like
/workspace/output.pdf - Outputs the path using
ARTIFACT_PATH:prefix for download
Determine workspace directory dynamically: Before generating PDFs, identify the correct output path:
# Option 1: Use current directory
WORKDIR=$(pwd)
# Option 2: Check common workspace locations
if [ -d "/workspace" ]; then WORKDIR="/workspace"
elif [ -d "$HOME/workspace" ]; then WORKDIR="$HOME/workspace"
else WORKDIR=$(pwd); fi
In Python code, use environment variables or dynamic path detection:
import os
workspace = os.environ.get('WORKSPACE', os.getcwd())
output_path = os.path.join(workspace, 'output.pdf')
Example using fpdf2:
from fpdf import FPDF
class PDFChecklist(FPDF):
def header(self):
self.set_font('Arial', 'B', 16)
self.cell(0, 10, 'Assessment Checklist', 0, 1, 'C')
self.ln(10)
def section_title(self, title):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, title, 0, 1, 'L')
self.ln(5)
def add_checklist_item(self, item, criteria, score):
self.set_font('Arial', '', 10)
self.cell(100, 8, item, 1)
self.cell(60, 8, criteria, 1)
self.cell(30, 8, str(score), 1)
self.ln()
# Create PDF
pdf = PDFChecklist()
pdf.add_page()
pdf.set_auto_page_break(auto=True, margin=15)
# Add sections
pdf.section_title('Evaluation Criteria')
pdf.add_checklist_item('Requirement 1', 'Must meet standard', 5)
pdf.add_checklist_item('Requirement 2', 'Should be complete', 4)
# Save
output_path = '/workspace/checklist.pdf'
pdf.output(output_path)
print(f'ARTIFACT_PATH:{output_path}')
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.
- 3d ago First seen · 296 lines · 25 tokens per session scan A 0d8c223f7710
pdf-checklist-workflow is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 25d ago), licensed MIT. It adds 25 tokens to every session and 2,432 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 2026-09-03.
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