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 code-exec-fallback-266cbagit 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/code-exec-fallback-266cba)<a href="https://agentmods.dev/skills/hkuds/openspace/code-exec-fallback-266cba"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-exec-fallback-266cba/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/code-exec-fallback-266cba"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/code-exec-fallback-266cba.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.00603 |
| Opus 5.5 | $0.00008 | $0.00241 |
| Sonnet 5.5 | $0.00004 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
code-exec-fallback-266cba 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 1mo 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Execution Fallback Pattern
When to Use This Skill
Apply this pattern when you encounter repeated failures with execute_code_sandbox:
- 2+ consecutive failures with opaque or unknown errors
- Timeout errors that persist across retry attempts
- Environment-related errors that don't resolve with code fixes
The Fallback Workflow
Step 1: Detect Repeated Failures
Track execution failures. After 2 consecutive failures with execute_code_sandbox, switch to the fallback approach.
Step 2: Write Script to File
Use write_file to save your Python script:
write_file(
path="/workspace/script_name.py",
content="# Your Python code here\nimport sys\n..."
)
Step 3: Execute via Shell
Use run_shell to run the script:
run_shell(
command="python /workspace/script_name.py",
timeout=300
)
Step 4: Capture Output
Parse stdout/stderr from run_shell output to verify success or diagnose issues.
Complete Example
# Instead of this (which may fail):
result = execute_code_sandbox(code="import pandas as pd\n...")
# Use this fallback pattern:
script_content = """
import pandas as pd
import sys
try:
# Your logic here
df = pd.DataFrame({'col': [1, 2, 3]})
print(df.to_csv())
sys.exit(0)
except Exception as e:
print(f"ERROR: {e}", file=sys.stderr)
sys.exit(1)
"""
# Write the script
write_file(path="/workspace/my_script.py", content=script_content)
# Execute via shell
result = run_shell(command="python /workspace/my_script.py", timeout=300)
Best Practices
- Add error handling in your script - use try/except with
sys.exit()codes - Set appropriate timeouts -
run_shelldefault is 30s, increase for heavy operations - Clean up temporary files after execution if needed
- Log the fallback trigger - document why you switched approaches
- Verify Python availability - Most sandboxes have Python 3.x by default
Why This Works
write_fileis more reliable for file I/O operationsrun_shellgives you direct control over execution environment- Shell execution bypasses sandbox serialization issues
- Better error visibility through stdout/stderr streams
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.
- 1mo ago First seen · 95 lines · 20 tokens per session scan A a1e126879ef1
code-exec-fallback-266cba is a skill published in the GitHub repository HKUDS/OpenSpace (7,760 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 603 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…