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 regulatory-fallback-researchgit 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/regulatory-fallback-research)<a href="https://agentmods.dev/skills/hkuds/openspace/regulatory-fallback-research"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/regulatory-fallback-research.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 | $0.00023 | $0.01022 |
| Opus 5 | $0.00012 | $0.00511 |
| Sonnet 5 | $0.00005 | $0.00204 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
regulatory-fallback-research 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 4d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Regulatory/Government Content Research with Fallback Strategy
Overview
When researching regulatory, government, or compliance-related content, primary sources (official websites, PDFs, regulatory databases) often become inaccessible due to tool failures, access restrictions, or technical issues. This skill provides a resilient workflow to complete research tasks despite these obstacles.
When to Use This Skill
Use this workflow when:
- You need to research regulatory/government content (FDA, CMS, DEA, state boards, etc.)
- Primary source tools (read_webpage, search_web) return 'unknown error' or fail repeatedly
- You need to produce compliance documentation, checklists, or regulatory summaries
Step-by-Step Procedure
Step 1: Attempt Primary Source Access
First, try to access official sources directly:
1. Use read_webpage on known regulatory URLs
2. Use search_web for specific regulatory queries
3. Attempt read_file on any available PDFs or documents
Expected outcomes:
- Success: Proceed with content extraction
- 'unknown error' or repeated failures: Document the failure and proceed to Step 2
Step 2: Deploy Shell Agent for Secondary Research
When primary tools fail, use shell_agent for alternative information gathering:
shell_agent task: "Research [topic] compliance requirements using alternative sources.
Search for summaries, guides, or cached versions of regulatory information.
Focus on established compliance frameworks and best practices."
Strategies for shell_agent:
- Request research from multiple angles (state vs federal, industry guides, etc.)
- Ask for aggregated information from secondary sources
- Request compliance checklist templates from industry resources
Step 3: Apply Domain Knowledge
When both primary and secondary research tools fail:
1. Acknowledge the tool limitations explicitly
2. State you will proceed using established domain knowledge
3. Create content based on well-known regulatory frameworks
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.
- 4d ago First seen · 144 lines · 23 tokens per session scan A 833ae0b4b2c3
regulatory-fallback-research is a skill published in the GitHub repository HKUDS/OpenSpace (7,534 stars, last pushed 26d ago), licensed MIT. It adds 23 tokens to every session and 1,022 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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