code-exec-fallback-266cba

A fallback procedure for running code when the normal sandbox has failed repeatedly. It saves the script to a file, runs it through a shell, and checks the resulting output.

In plain words
What is it for?
Executing Python or other scripts outside the failing sandbox, capturing standard output and errors, and diagnosing whether the run succeeded.
Why use it?
It gives coding agents a defined next step after repeated sandbox errors, timeouts, or environment problems instead of retrying the same failing method.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/hkuds/openspace/code-exec-fallback-266cba
Any agent
npx skills add HKUDS/OpenSpace --skill code-exec-fallback-266cba
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 603 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00020 $0.00603
Opus 5 $0.00010 $0.00302
Sonnet 5 $0.00004 $0.00121
Haiku 4.5 $0.00002 $0.00060

Measured yesterday against content hash a1e126879ef1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

benchmarks/gdpval/skills/code-exec-fallback-266cba/SKILL.md · 95 lines

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

  1. Add error handling in your script - use try/except with sys.exit() codes
  2. Set appropriate timeouts - run_shell default is 30s, increase for heavy operations
  3. Clean up temporary files after execution if needed
  4. Log the fallback trigger - document why you switched approaches
  5. Verify Python availability - Most sandboxes have Python 3.x by default

Why This Works

  • write_file is more reliable for file I/O operations
  • run_shell gives you direct control over execution environment
  • Shell execution bypasses sandbox serialization issues
  • Better error visibility through stdout/stderr streams

Read the full file on GitHub · 95 lines

Files

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.

Changes

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.

  1. yesterday First seen · 95 lines · 20 tokens per session scan A a1e126879ef1

Subscribe to this mod's changes

code-exec-fallback-266cba is a skill published in the GitHub repository HKUDS/OpenSpace (7,479 stars, last pushed 19d ago), licensed MIT. It adds 20 tokens to every session and 603 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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