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 sandbox-file-discovery-and-validationgit 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/sandbox-file-discovery-and-validation)<a href="https://agentmods.dev/skills/hkuds/openspace/sandbox-file-discovery-and-validation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-file-discovery-and-validation/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/sandbox-file-discovery-and-validation"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/sandbox-file-discovery-and-validation.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 | $0.00036 | $0.01524 |
| Opus 5 | $0.00018 | $0.00762 |
| Sonnet 5 | $0.00007 | $0.00305 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
sandbox-file-discovery-and-validation 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sandbox File Discovery and Validation
Use this skill when working in constrained or sandboxed shell environments where common folders like ~/Desktop, ~/Documents, or ~/Downloads may not exist, may be inaccessible, or may not be where task files are stored.
The goal is to:
- discover files from actual accessible roots,
- avoid brittle path assumptions,
- generate outputs in known locations, and
- explicitly validate that outputs were created and match expectations.
Core principles
- Do not assume GUI-era user folders exist.
- Start from the current working directory and other confirmed workspace roots.
- Use recursive search from known roots, not from
/unless necessary. - Prefer deterministic output paths you control.
- After creating an artifact, verify it exists, is readable, and matches the requested form.
When to use this
Use this pattern when:
- you need to locate input files in an unfamiliar sandbox,
- the environment may be ephemeral or nonstandard,
- you are producing files for the user,
- success depends on the actual contents or structure of the output.
Recommended workflow
1) Establish your real working roots
Begin by identifying where you are and what directories are actually available.
Example:
pwd
ls -la
find . -maxdepth 2 -type d | sort
If needed, inspect nearby likely roots:
ls -la /tmp
ls -la /workspace 2>/dev/null || true
ls -la /workspaces 2>/dev/null || true
ls -la /mnt/data 2>/dev/null || true
Treat only confirmed, readable directories as search roots.
2) Avoid assumed folders
Do not begin with paths like:
~/Desktop~/Documents~/Downloads
unless you have already confirmed they exist and are relevant.
Bad:
find ~/Desktop -name "*.xlsx"
Better:
find . -type f -name "*.xlsx"
or, if a root is confirmed:
find /workspace -type f -name "*.xlsx" 2>/dev/null
3) Search from known roots with bounded, focused queries
Prefer targeted searches over broad filesystem scans.
Useful patterns:
find . -type f | sort
find . -type f -iname "*report*"
find . -type f \( -iname "*.csv" -o -iname "*.xlsx" -o -iname "*.json" \)
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 · 223 lines · 36 tokens per session scan A 089e00c5ede6
sandbox-file-discovery-and-validation is a skill published in the GitHub repository HKUDS/OpenSpace (7,552 stars, last pushed 28d ago), licensed MIT. It adds 36 tokens to every session and 1,524 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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