regulatory-research-fallback

regulatory-research-fallback is a skill for Claude Code, Codex from HKUDS/OpenSpace. It costs 20 tokens per session (795 once invoked), scanned A, original, MIT.

A fallback workflow for researching regulations when web tools cannot read government websites or PDFs. It gathers information from alternative compliance guides and regulatory summaries.

In plain words
What is it for?
Researching legal or regulatory requirements and turning them into actionable compliance documentation.
Why use it?
It helps produce a compliance checklist when primary regulatory sources are temporarily inaccessible.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Researching legal or regulatory requirements and turning them into actionable compliance documentation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/openspace/regulatory-research-fallback
About the project

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.

HKUDS/OpenSpace · 7,760 stars · on GitHub

Install

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.

Any agent
npx skills add HKUDS/OpenSpace --skill regulatory-research-fallback
Clone the repo
git clone --depth 1 https://github.com/HKUDS/OpenSpace

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for regulatory-research-fallback

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/openspace/regulatory-research-fallback/github.svg)](https://agentmods.dev/skills/hkuds/openspace/regulatory-research-fallback)
Your own site
<a href="https://agentmods.dev/skills/hkuds/openspace/regulatory-research-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/regulatory-research-fallback/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.

agentmods 80×15 button for regulatory-research-fallback

Your own site · 80×15
<a href="https://agentmods.dev/skills/hkuds/openspace/regulatory-research-fallback"><img src="https://agentmods.dev/badge/skills/hkuds/openspace/regulatory-research-fallback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 795 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00020 $0.00795
Opus 5.5 $0.00008 $0.00318
Sonnet 5.5 $0.00004 $0.00159
Haiku 4.5 $0.00002 $0.00080

Measured 1mo ago against content hash 5f3733983e91, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

regulatory-research-fallback 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 1mo 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.

benchmarks/gdpval/skills/regulatory-research-fallback/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Regulatory Research Fallback Workflow

This skill provides a systematic approach to gathering regulatory/compliance information when primary web extraction tools fail on government or regulatory website URLs.

When to Use This Skill

  • read_webpage returns "unknown error" on .gov or regulatory PDF URLs
  • search_web fails to retrieve current regulatory information
  • You need to create compliance checklists or regulatory documentation
  • Primary sources are inaccessible but the task must be completed

Step-by-Step Procedure

Step 1: Attempt Primary Source Extraction

First, try standard web extraction tools:

1. Use read_webpage on the regulatory URL
2. Use search_web for supplementary regulatory information
3. Document which tools failed and with what errors

Step 2: Activate Fallback - Shell Agent Research

When primary tools fail, delegate to shell_agent for secondary research:

Task: "Research [TOPIC] compliance requirements using available system tools. 
Gather information from alternative sources including:
- State/federal regulatory summaries
- Industry compliance guides
- Professional association resources
Focus on actionable checklist items for [SPECIFIC REQUIREMENT]"

Example shell_agent task for pharmacy compliance:

"Research pharmacy compliance requirements for [state]. Gather information 
about licensing, storage, dispensing, and documentation requirements from 
available sources. Create structured notes on mandatory compliance areas."

Step 3: Apply Domain Knowledge

When sources remain inaccessible, use established domain knowledge:

For pharmacy compliance, standard areas include:
- Licensing and registration requirements
- Controlled substance handling
- Prescription record-keeping
- Storage and security protocols
- Patient counseling requirements
- Continuing education obligations

Step 4: Create Documentation with Confidence Levels

Structure output with clear sourcing transparency:

## [Requirement Area]

**Status**: Based on standard regulatory framework (primary sources inaccessible)

**Key Requirements**:
- [Item 1] - Standard industry requirement
- [Item 2] - Common regulatory expectation
- [Item 3] - Best practice guideline

**Note**: Verify with [specific agency] for jurisdiction-specific requirements

Read the full file on GitHub · 134 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 134 lines · 20 tokens per session scan A 5f3733983e91

Subscribe to this mod's changes

regulatory-research-fallback is a skill published in the GitHub repository HKUDS/OpenSpace (7,760 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 795 once invoked, about $0.0001 per session on Opus 5.5. 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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