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 openpyxl-sandbox-workaroundgit 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/openpyxl-sandbox-workaround)<a href="https://agentmods.dev/skills/hkuds/openspace/openpyxl-sandbox-workaround"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/openpyxl-sandbox-workaround/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/openspace/openpyxl-sandbox-workaround"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/openpyxl-sandbox-workaround.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 68 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00031 | $0.00786 |
| Opus 5 | $0.00015 | $0.00393 |
| Sonnet 5 | $0.00006 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
openpyxl-sandbox-workaround 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Openpyxl Sandbox Workaround
When to Use
Use this pattern when execute_code_sandbox repeatedly fails for openpyxl or spreadsheet manipulation tasks. The sandbox environment may have compatibility issues with certain openpyxl operations, but running Python directly via run_shell often succeeds.
Common Failure Indicators
execute_code_sandboxreturns errors related to openpyxl imports or operations- Multiple retry attempts fail with similar errors
- Errors mention workbook creation, cell writing, or file saving issues
The Workaround Pattern
Instead of using execute_code_sandbox, use run_shell with an inline Python script:
python3 << 'EOF'
from openpyxl import Workbook
from openpyxl.utils import get_column_letter
# Your openpyxl code here
wb = Workbook()
ws = wb.active
ws.title = "Sheet1"
# Add data
ws['A1'] = 'Header'
ws['B1'] = 'Value'
# Save file
wb.save('output.xlsx')
print('File created successfully')
EOF
Multi-Step Spreadsheet Tasks
For complex operations involving multiple files or data processing:
python3 << 'EOF'
from openpyxl import Workbook, load_workbook
import os
# Load existing workbook if needed
if os.path.exists('input.xlsx'):
wb = load_workbook('input.xlsx')
ws = wb.active
# Process data...
# Create new workbook
wb = Workbook()
ws = wb.active
# Add data with proper formatting
for row_idx, row_data in enumerate(data, start=1):
for col_idx, value in enumerate(row_data, start=1):
ws.cell(row=row_idx, column=col_idx, value=value)
# Auto-adjust column widths
for column in ws.columns:
max_length = 0
column_letter = get_column_letter(column[0].column)
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except:
pass
adjusted_width = min(max_length + 2, 50)
ws.column_dimensions[column_letter].width = adjusted_width
wb.save('output.xlsx')
EOF
Best Practices
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 · 121 lines · 31 tokens per session scan A a2bf520fc155
openpyxl-sandbox-workaround is a skill published in the GitHub repository HKUDS/OpenSpace (7,552 stars, last pushed 28d ago), licensed MIT. It adds 31 tokens to every session and 786 once invoked, about $0.0002 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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