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 skills add HKUDS/OpenSpace --skill pdf-gen-fallback-501bccgit 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/pdf-gen-fallback-501bcc)<a href="https://agentmods.dev/skills/hkuds/openspace/pdf-gen-fallback-501bcc"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/pdf-gen-fallback-501bcc.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.01042 |
| Opus 5 | $0.00012 | $0.00521 |
| Sonnet 5 | $0.00005 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
pdf-gen-fallback-501bcc 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 4d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Generation Fallback Pattern
When to Use
Apply this pattern when execute_code_sandbox returns opaque or unhelpful errors during PDF, DOCX, or other document generation tasks. This fallback uses run_shell with direct python -c execution for more reliable document generation.
Step-by-Step Procedure
1. Detect Sandbox Failure
If execute_code_sandbox fails during document generation with errors like:
- Opaque runtime errors
- Missing library errors that don't resolve with import statements
- Silent failures with no output
Proceed to the fallback approach.
2. Check Library Availability
Before generating documents, verify the required Python libraries are available in the shell environment:
python -c "import reportlab; print('OK')" 2>&1 || echo "reportlab missing"
python -c "import fpdf; print('OK')" 2>&1 || echo "fpdf missing"
python -c "import pypdf; print('OK')" 2>&1 || echo "pypdf missing"
python -c "import docx; print('OK')" 2>&1 || echo "python-docx missing"
3. Install Missing Libraries (If Needed)
If a library is missing, install it via pip:
pip install reportlab fpdf2 pypdf python-docx
4. Execute Document Generation via run_shell
Use run_shell with python -c and multi-line strings for document generation:
python -c "
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas('output.pdf', pagesize=letter)
c.drawString(100, 750, 'Document Title')
c.drawString(100, 700, 'Content here')
c.save()
print('SUCCESS: PDF created')
"
For DOCX files:
python -c "
from docx import Document
doc = Document()
doc.add_heading('Document Title', 0)
doc.add_paragraph('Content here')
doc.save('output.docx')
print('SUCCESS: DOCX created')
"
5. Include Explicit Error Handling
Always wrap generation code with try/except for clear error reporting:
python -c "
try:
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas('output.pdf', pagesize=letter)
c.drawString(100, 750, 'Content')
c.save()
print('SUCCESS')
except ImportError as e:
print(f'MISSING_LIBRARY: {e}')
exit(1)
except Exception as e:
print(f'GENERATION_ERROR: {e}')
exit(1)
"
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.
- 4d ago First seen · 148 lines · 23 tokens per session scan A 263f1bd45962
pdf-gen-fallback-501bcc is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 26d ago), licensed MIT. It adds 23 tokens to every session and 1,042 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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