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 shell-agent-delegationgit 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/shell-agent-delegation)<a href="https://agentmods.dev/skills/hkuds/openspace/shell-agent-delegation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/shell-agent-delegation.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 | $0.00025 | $0.00840 |
| Opus 5 | $0.00013 | $0.00420 |
| Sonnet 5 | $0.00005 | $0.00168 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
shell-agent-delegation 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 5d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shell Agent Delegation for Resilient Workflow Execution
When to Use This Skill
Apply this pattern when:
- Direct tool execution (execute_code_sandbox, read_webpage, search_web) fails with 'unknown error'
- Multiple tool attempts have failed in sequence
- The task requires complex document generation or data processing
- You need a tool that can autonomously select libraries and handle multi-step workflows
Why This Works
The shell_agent tool differs from direct execution tools in key ways:
- Autonomous tool selection: Decides whether to use Python or Bash based on the task
- Built-in error recovery: Automatically retries and fixes errors (up to several rounds)
- Iterative execution: Writes code, executes, inspects output, and adapts
- Full workflow ownership: Handles the entire task end-to-end without manual intervention
Step-by-Step Instructions
Step 1: Recognize the Failure Pattern
Identify when to pivot to shell_agent:
- execute_code_sandbox returned 'unknown error'
- read_webpage/search_web failed multiple times
- Direct approaches are struggling with the task complexity
Step 2: Formulate the Delegation Task
Create a clear, self-contained task description for shell_agent:
Good task description:
Create a 1-page SBAR Template PDF document. Include sections for:
- Situation: Brief description of the current situation
- Background: Relevant context and history
- Assessment: Current assessment and analysis
- Recommendation: Proposed actions and next steps
Use a professional layout with clear headings and adequate whitespace.
Key elements to include:
- The end goal (what should be produced)
- Required sections/components
- Format requirements (PDF, DOCX, etc.)
- Any style or layout preferences
Step 3: Execute the Delegation
Call shell_agent with your task description:
# Conceptual example
shell_agent(task="Create a professional SBAR Template PDF with Situation, Background, Assessment, and Recommendation sections. Include clear headings and professional formatting.")
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.
- 5d ago First seen · 108 lines · 25 tokens per session scan A 3bdf8ae24c83
shell-agent-delegation is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 26d ago), licensed MIT. It adds 25 tokens to every session and 840 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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