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 write-file-fallback-report-enhancedgit 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/write-file-fallback-report-enhanced)<a href="https://agentmods.dev/skills/hkuds/openspace/write-file-fallback-report-enhanced"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/write-file-fallback-report-enhanced.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 216 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00027 | $0.01839 |
| Opus 5 | $0.00014 | $0.00920 |
| Sonnet 5 | $0.00005 | $0.00368 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
fallback-doc-with-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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fallback Document Generation with Delegation
When to Use This Skill
Use this workflow when attempting to generate a document or report, but multiple primary data source tools fail simultaneously:
read_filereturns binary/image data instead of text (common with PDFs)search_webreturns errors or no resultsexecute_code_sandboxfails unexpectedly- Other data retrieval tools are unavailable
Key insight: Rather than getting stuck on failed data retrieval, pivot immediately to generating the document using the most appropriate available method—either shell_agent for complex formats or write_file for simple text.
Step-by-Step Instructions
Step 1: Detect Tool Failure Pattern
Recognize when you're in a fallback scenario:
TOOL_FAILURE_INDICATORS = [
"read_file returns binary or image data",
"search_web returns unknown error or empty results",
"execute_code_sandbox fails repeatedly",
"Multiple consecutive tool failures on data retrieval"
]
Decision point: If 2+ indicators are present, proceed to Step 2.
Step 2: Choose the Appropriate Fallback Method
Critical Decision: Select between shell_agent and write_file based on document complexity:
Use shell_agent when... |
Use write_file when... |
|---|---|
| Generating PDFs or formatted documents | Creating simple markdown/text files |
| Complex layout or styling needed | Plain structured content is sufficient |
| External libraries required (reportlab, pandoc) | No special formatting libraries needed |
| Multi-step generation process | Single-pass content generation |
| Verification/validation needed | Direct file creation is adequate |
Decision Tree:
Is the output a PDF or complex formatted document?
├── YES → Use shell_agent (delegates to skilled agent)
└── NO → Is it markdown or plain text?
├── YES → Use write_file (direct content generation)
└── NO → Use shell_agent (handles uncertainty)
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 · 257 lines · 27 tokens per session scan A 3921bd68af6f
fallback-doc-with-delegation is a skill published in the GitHub repository HKUDS/OpenSpace (7,544 stars, last pushed 26d ago), licensed MIT. It adds 27 tokens to every session and 1,839 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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