Enforcement Action Analysis Amir Fadavi logo

Enforcement Action Analysis Amir Fadavi

Organization
lawve-ai
enforcement-action-analysis-amir-fadavi

Analyze any OFAC or OFSI enforcement action — by URL, pasted text, or uploaded document — and produce a structured root cause analysis as a formatted Excel (.xlsx) spreadsheet. Use this skill whenever a user names, links to, pastes, or uploads an OFAC or OFSI enforcement action and asks for any of the following: root cause analysis, compliance gaps, what went wrong, lessons learned, organizational self-assessment, or remediation planning. Also trigger when a user asks "analyze this enforcement action", "what were the root causes", "turn this into a checklist", or "how do I make sure this doesn't happen to us". Outputs a single-sheet .xlsx table with six columns: Root Cause | What Went Wrong | How It Went Wrong | What Could Have Stopped It | Is my organization immune to this? (Yes/No/Partial) | Notes.

Overview

Publisherlawve-ai
Repositoryawesome-legal-skills
Skill nameenforcement-action-analysis-amir-fadavi
Stars
854
Forks
111
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lawve-ai on GitHub. Read the source before you install it.

Installation

Install the Enforcement Action Analysis Amir Fadavi AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/enforcement-action-analysis-amir-fadavi
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills.git /tmp/awesome-legal-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-legal-skills/skills/enforcement-action-analysis-amir-fadavi .claude/skills/enforcement-action-analysis-amir-fadavi
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Enforcement Action Analysis Amir Fadavi in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Enforcement Action Analysis Amir Fadavi on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Enforcement Action Analysis Amir Fadavi is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Enforcement Action Analysis Skill

Produces a structured root cause analysis of any OFAC or OFSI enforcement action as a formatted Excel spreadsheet. The output is a six-column table designed to be used as a working document by compliance officers, in-house counsel, external counsel, and consultants — at financial institutions and non-financial firms alike.


Input

The user will provide the enforcement action in one of three ways:

  1. URL — fetch and parse the document (PDF or HTML)
  2. Pasted text — use the text directly from the conversation
  3. Uploaded file — read from /mnt/user-data/uploads/

If none is provided, ask the user to supply the enforcement action before proceeding.


Step 1 — Extract the Case Facts

Before identifying root causes, extract the following from the enforcement action:

  • Subject (name of the settling party)
  • Regulator (OFAC or OFSI; include department/division if stated)
  • Date of settlement or enforcement release
  • Settlement amount
  • Sanctions program (e.g., Iran, Russia, Cuba) and specific regulations cited
  • Violation period
  • Number of apparent violations
  • Egregious / non-egregious
  • Voluntarily self-disclosed?

Use this to name the output file and populate the sheet title cell.


Step 2 — Identify Root Causes

Read the full enforcement action — especially the Description of the Apparent Violations, the Aggravating Factors, and the Compliance Considerations sections. These are the primary source material for root causes.

Identify all distinct root causes. A root cause is a discrete compliance failure — a gap in policy, process, training, technology, or judgment — that contributed to the violation. Do not consolidate unrelated failures to keep the table short. Typical enforcement actions yield 2–5 root causes; complex cases (e.g., commodity trading, multi-party evasion schemes) may yield more.

For each root cause, draft three things:

Column: What Went Wrong

One to three sentences describing the specific failure as it occurred in this case. Factual, grounded in the enforcement action text. No generic compliance language.

Column: How It Went Wrong

One to three sentences explaining the underlying compliance failure mechanism — why the organization's program did not catch this. Draw from:

  • Aggravating factors stated by the regulator
  • Compliance Considerations section
  • OFAC's Compliance Framework root cause taxonomy (listed below)
  • Logical inference from the facts

Column: What Could Have Stopped It

Two to four sentences describing concrete controls that would have prevented or detected the violation. Be specific to the facts of the case. Always reflect OFAC's Compliance Considerations section — these are the regulator's own stated expectations and must not be omitted.


OFAC Root Cause Taxonomy (reference)

From OFAC's Compliance Framework appendix. Use as a checklist when identifying root causes:

  • Lack of a formal sanctions compliance program
  • Inadequate policies and procedures (including failure to update for new business lines)
  • Misapplication of OFAC's regulations (including "form over substance" errors)
  • Failure to update or use automated screening tools
  • Screening tool not configured to cover relevant lists (e.g., SSI/non-SDN lists)
  • Failure to identify and escalate red flags
  • Lack of due diligence on customers, intermediaries, or counterparties
  • Decentralized compliance function with inconsistent application
  • Inadequate sanctions compliance training
  • Failure to conduct ongoing monitoring of existing relationships
  • New business line entered without updating compliance program

Step 3 — Build the Spreadsheet

Use openpyxl (Python). Do not use any other library for file creation.

Sheet structure

  • Row 1: Title cell (merged A1:F1) — Root Causes of Apparent Violations — [Subject] ([Regulator], [Date])
  • Row 2: Column headers
  • Rows 3+: One row per root cause

Column layout

ColHeaderWidth (chars)
ARoot Cause22
BWhat Went Wrong38
CHow It Went Wrong42
DWhat Could Have Stopped It46
EIs my organization immune to this?22
FNotes28

Styling

python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter

NAVY   = "1B3A6B"
STEEL  = "A8C4E0"
LIGHT  = "EEF2F9"
WHITE  = "FFFFFF"
INK    = "1A1A2E"
GREY   = "D0D8E4"

thin = Side(style='thin', color=GREY)
border = Border(left=thin, right=thin, top=thin, bottom=thin)
wrap = Alignment(wrap_text=True, vertical='top')
center_wrap = Alignment(wrap_text=True, vertical='center', horizontal='center')

Title row (row 1, merged A1:F1):

  • Merge cells A1:F1
  • Font: Arial 14pt bold, color WHITE
  • Fill: NAVY
  • Alignment: left, vertical center
  • Row height: 30

Header row (row 2):

  • Font: Arial 11pt bold, color WHITE
  • Fill: NAVY
  • Alignment: wrap, vertical top
  • Border: all sides thin GREY
  • Row height: 30

Data rows (row 3+):

  • Column A: Font Arial 10pt bold color NAVY, fill LIGHT, border, wrap top-left
  • Columns B–D: Font Arial 10pt color INK, fill WHITE, border, wrap top-left
  • Column E: Font Arial 10pt color INK, fill WHITE, border, center-aligned — value: ☐ Yes / ☐ No / ☐ Partial
  • Column F: Font Arial 10pt color INK, fill WHITE, border, wrap top-left — empty
  • Row height: set to 15 * (estimated line count) — minimum 60, use sheet.row_dimensions[r].height

Column A label format: RC[N]: [Short Title] — e.g., RC1: SDN-Only Screening

Output path

/mnt/user-data/outputs/[SubjectName]_OFAC_RootCause_Analysis.xlsx

Use underscores, no spaces. Sanitize special characters.

Full code template

python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
import math

wb = Workbook()
ws = wb.active
ws.title = "Root Cause Analysis"

NAVY, LIGHT, WHITE, INK, GREY = "1B3A6B", "EEF2F9", "FFFFFF", "1A1A2E", "D0D8E4"
thin   = Side(style='thin', color=GREY)
border = Border(left=thin, right=thin, top=thin, bottom=thin)
wrap   = Alignment(wrap_text=True, vertical='top')
cwrap  = Alignment(wrap_text=True, vertical='center', horizontal='center')

col_widths = [22, 38, 42, 46, 22, 28]
headers    = ["Root Cause", "What Went Wrong", "How It Went Wrong",
              "What Could Have Stopped It", "Is my organization immune to this?", "Notes"]

# Title row
ws.merge_cells("A1:F1")
t = ws["A1"]
t.value     = "Root Causes of Apparent Violations — [Subject] ([Regulator], [Date])"
t.font      = Font(name="Arial", size=14, bold=True, color=WHITE)
t.fill      = PatternFill("solid", fgColor=NAVY)
t.alignment = Alignment(horizontal='left', vertical='center')
ws.row_dimensions[1].height = 30

# Header row
for i, h in enumerate(headers, 1):
    c = ws.cell(row=2, column=i, value=h)
    c.font      = Font(name="Arial", size=11, bold=True, color=WHITE)
    c.fill      = PatternFill("solid", fgColor=NAVY)
    c.alignment = cwrap
    c.border    = border
ws.row_dimensions[2].height = 30

# Column widths
for i, w in enumerate(col_widths, 1):
    ws.column_dimensions[get_column_letter(i)].width = w

# rows = list of (rc_label, what_went_wrong, how_it_went_wrong, what_could_have_stopped)
rows = []  # populated from analysis

for r, (rc, ww, hw, stop) in enumerate(rows, start=3):
    data = [rc, ww, hw, stop, "☐ Yes  /  ☐ No  /  ☐ Partial", ""]
    max_lines = 1
    for i, val in enumerate(data, 1):
        c = ws.cell(row=r, column=i, value=val)
        c.border    = border
        c.font      = Font(name="Arial", size=10, bold=(i == 1),
                           color=NAVY if i == 1 else INK)
        c.fill      = PatternFill("solid", fgColor=LIGHT if i == 1 else WHITE)
        c.alignment = cwrap if i == 5 else wrap
        if val and i < 5:
            lines = math.ceil(len(str(val)) / col_widths[i-1]) + str(val).count('\n')
            max_lines = max(max_lines, lines)
    ws.row_dimensions[r].height = max(60, max_lines * 15)

wb.save("/mnt/user-data/outputs/[Filename].xlsx")
print("Done.")

Step 4 — Present the File

Call present_files with the output path. One line of context is enough (e.g., "Four root causes for the FTI case — ready to download.").


Quality checks before presenting

  • Every root cause row has all four text columns populated (no blanks in B–D)
  • "What Could Have Stopped It" reflects OFAC's Compliance Considerations where applicable
  • Root causes are distinct — no two rows describe the same underlying failure
  • Column A labels follow RC[N]: [Short Title] format
  • Title cell matches: Root Causes of Apparent Violations — [Subject] ([Regulator], [Date])
  • Column E contains the checkbox string in every data row
  • File written to /mnt/user-data/outputs/ and presented via present_files

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Enforcement Action Analysis Amir Fadavi AI skill do?

Analyze any OFAC or OFSI enforcement action — by URL, pasted text, or uploaded document — and produce a structured root cause analysis as a formatted Excel (.xlsx) spreadsheet. Use this skill whenever a user names, links to, pastes, or uploads an OFAC or OFSI enforcement action and asks for any of the following: root cause analysis, compliance gaps, what went wrong, lessons learned, organizational self-assessment, or remediation planning. Also trigger when a user asks "analyze this enforcement action", "what were the root causes", "turn this into a checklist", or "how do I make sure this do...

Why use Enforcement Action Analysis Amir Fadavi on TypingMind?

Because you install it once and use it with any model. Enforcement Action Analysis Amir Fadavi is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Enforcement Action Analysis Amir Fadavi in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/enforcement-action-analysis-amir-fadavi. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Enforcement Action Analysis Amir Fadavi?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Enforcement Action Analysis Amir Fadavi?

As many as you like. As long as a model supports skills, you can use Enforcement Action Analysis Amir Fadavi with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Enforcement Action Analysis Amir Fadavi AI skill free?

It is published on GitHub by lawve-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇