Regulatory Compliance GuideSAFE

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: regulatory-compliance-guide
description: "Regulatory text mining, compliance research, and policy analysis tools"
metadata:
  openclaw:
    emoji: "📜"
    category: "domains"
    subcategory: "law"
    keywords: ["regulation", "compliance", "policy-analysis", "text-mining", "federal-register", "rulemaking"]
    source: "wentor"
---

# Regulatory Compliance Guide

A skill for mining regulatory texts, tracking regulatory changes, and conducting compliance research. Covers accessing regulatory databases, parsing regulatory language, change detection in regulations, compliance gap analysis, and computational policy analysis.

## Regulatory Data Sources

### US Federal Regulatory Data

| Source | Content | Format | Access |
|--------|---------|--------|--------|
| Federal Register API | Proposed and final rules | JSON API | Free (federalregister.gov) |
| eCFR (Electronic CFR) | Current Code of Federal Regulations | XML + API | Free (ecfr.gov) |
| Regulations.gov | Public comments on rulemakings | JSON API | Free |
| Congress.gov | Bills and legislative history | API + bulk | Free |
| SEC EDGAR | Securities filings and no-action letters | Full-text search + API | Free |

### Accessing Federal Register Data

```python
import requests
from datetime import date, timedelta

class FederalRegisterClient:
    """Client for the Federal Register API."""

    BASE_URL = "https://www.federalregister.gov/api/v1"

    def search_rules(self, query: str, agency: str = None,
                     date_from: str = None, per_page: int = 20) -> dict:
        """
        Search for rules and proposed rules in the Federal Register.
        """
        params = {
            "conditions[term]": query,
            "conditions[type][]": ["RULE", "PRORULE"],
            "per_page": per_page,
            "order": "newest",
        }
        if agency:
            params["conditions[agencies][]"] = agency
        if date_from:
            params["conditions[publication_date][gte]"] = date_from

        resp = requests.get(f"{self.BASE_URL}/documents", params=params)
        data = resp.json()
        return {
            "count": data.get("count", 0),
            "results": [
                {
                    "title": r["title"],
                    "document_number": r["document_number"],
                    "publication_date": r["publication_date"],
                    "agency_names": r.get("agency_names", []),
                    "type": r["type"],
                    "abstract": r.get("abstract", ""),
                    "html_url": r["html_url"],
                }
                for r in data.get("results", [])
            ],
        }

    def get_document(self, document_number: str) -> dict:
        """Retrieve full document details by document number."""
        resp = requests.get(
            f"{self.BASE_URL}/documents/{document_number}.json"
        )
        return resp.json()
```

## Regulatory Text Parsing

### Identifying Regulatory Obligations

Regulatory language follows predictable patterns that indicate obligation strength:

```python
import re
from enum import Enum

class ObligationLevel(Enum):
    MANDATORY = "mandatory"       # shall, must, required
    PROHIBITIVE = "prohibitive"   # shall not, must not, prohibited
    PERMISSIVE = "permissive"     # may, is permitted
    RECOMMENDED = "recommended"   # should, is recommended
    INFORMATIVE = "informative"   # for information, note

OBLIGATION_PATTERNS = {
    ObligationLevel.MANDATORY: [
        r"\bshall\b(?!\s+not)", r"\bmust\b(?!\s+not)",
        r"\bis required to\b", r"\bare required to\b",
    ],
    ObligationLevel.PROHIBITIVE: [
        r"\bshall not\b", r"\bmust not\b",
        r"\bis prohibited\b", r"\bmay not\b",
    ],
    ObligationLevel.PERMISSIVE: [
        r"\bmay\b(?!\s+not)", r"\bis permitted\b",
        r"\bis authorized\b",
    ],
    ObligationLevel.RECOMMENDED: [
        r"\bshould\b(?!\s+not)", r"\bis recommended\b",
        r"\bit is advisable\b",
    ],
}

def classify_obligations(text: str) -> list[dict]:
    """
    Extract and classify regulatory obligations from text.
    Returns sentences tagged with their obligation level.
    """
    sentences = re.split(r'(?<=[.!?])\s+', text)
    results = []
    for sent in sentences:
        level = ObligationLevel.INFORMATIVE
        for obl_level, patterns in OBLIGATION_PATTERNS.items():
            if any(re.search(p, sent, re.IGNORECASE) for p in patterns):
                level = obl_level
                break
        results.append({"sentence": sent.strip(), "obligation": level.value})
    return results
```

### CFR Section Parsing

```python
def parse_cfr_section(xml_text: str) -> dict:
    """
    Parse an eCFR XML section into structured components.
    Extracts the section number, heading, paragraphs, and cross-references.
    """
    root = ET.fromstring(xml_text)
    section = {
        "number": root.findtext(".//SECTNO", ""),
        "heading": root.findtext(".//SUBJECT", ""),
        "paragraphs": [],
        "cross_references": [],
    }

    for para in root.iter("P"):
        text = "".join(para.itertext()).strip()
        if text:
            section["paragraphs"].append(text)
            # Extract cross-references to other CFR sections
            xrefs = re.findall(r"\d+\s+CFR\s+[\d.]+(?:\([a-z]\))?", text)
            section["cross_references"].extend(xrefs)

    return section
```

## Regulatory Change Detection

### Tracking Amendments Over Time

```python
from difflib import SequenceMatcher, unified_diff

def compare_regulation_versions(old_text: str, new_text: str,
                                  section_id: str) -> dict:
    """
    Compare two versions of a regulation section to identify changes.
    Returns a structured diff with change classification.
    """
    old_lines = old_text.splitlines(keepends=True)
    new_lines = new_text.splitlines(keepends=True)

    diff = list(unified_diff(old_lines, new_lines,
                 

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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none-observed

Network

none-observed

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none-observed

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pinned

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Gates applied: no_behavioural_pass.