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 agentmods add skills/hkuds/openspace/python-debug-execution-911f17npx skills add HKUDS/OpenSpace --skill python-debug-execution-911f17git 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/python-debug-execution-911f17)<a href="https://agentmods.dev/skills/hkuds/openspace/python-debug-execution-911f17"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/python-debug-execution-911f17.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 | $0.00023 | $0.00824 |
| Opus 5 | $0.00012 | $0.00412 |
| Sonnet 5 | $0.00005 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
python-debug-execution-911f17 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 2d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Debug Execution
This skill provides a pattern for debugging Python script execution failures. It ensures you capture actual tracebacks instead of opaque errors and verifies the working directory before file operations.
Core Pattern
1. Execute with Full Traceback Capture
Always run Python scripts with stderr redirected and exit code reported:
python3 script.py 2>&1 ; echo Exit code: $?
This pattern:
2>&1- Redirects stderr to stdout so all output (including tracebacks) is captured togetherecho Exit code: $?- Reports the exit code to distinguish between successful runs and failures
Why this matters: Opaque errors without tracebacks make it impossible to identify the root cause. The exit code tells you if the script succeeded (0) or failed (non-zero).
2. Verify Working Directory Before File Operations
Before any file read/write operations in your Python script, add:
import os
print(f"Current working directory: {os.getcwd()}")
Or for debugging, add at the start of your script:
import os
import sys
# Debug: show execution context
print(f"Script path: {__file__}")
print(f"Working directory: {os.getcwd()}")
print(f"Python version: {sys.version}")
Why this matters: File operations often fail due to incorrect assumptions about the current working directory. Verifying os.getcwd() helps diagnose path-related errors.
Usage Examples
Example 1: Running a Script with Debug Output
# Instead of:
python3 analyze.py
# Use:
python3 analyze.py 2>&1 ; echo Exit code: $?
Example 2: Script with Directory Verification
#!/usr/bin/env python3
import os
import pandas as pd
# Verify execution context
print(f"Working directory: {os.getcwd()}")
# Now safe to do file operations
data_path = "data/input.csv"
print(f"Attempting to read: {data_path}")
# Check if file exists before reading
if os.path.exists(data_path):
df = pd.read_csv(data_path)
print(f"Successfully loaded {len(df)} rows")
else:
print(f"ERROR: File not found at {os.path.abspath(data_path)}")
print(f"Directory contents: {os.listdir('.')}")
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.
- 2d ago First seen · 129 lines · 23 tokens per session scan A 61612d9de0cb
python-debug-execution-911f17 is a skill published in the GitHub repository HKUDS/OpenSpace (7,510 stars, last pushed 24d ago), licensed MIT. It adds 23 tokens to every session and 824 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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