Agent skill

Enforcement Action Analysis Amir Fadavi

by lawve-ai in lawve-ai/awesome-legal-skills

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.

MITAuto-check passedDocuments & Office

Install Enforcement Action Analysis Amir Fadavi

skills CLI
$ npx skills add lawve-ai/awesome-legal-skills --skill enforcement-action-analysis-amir-fadavi -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lawve-ai/awesome-legal-skills enforcement-action-analysis-amir-fadavi --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/enforcement-action-analysis-amir-fadavi .claude/skills/enforcement-action-analysis-amir-fadavi && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
enforcement-action-analysis-amir-fadavi
GitHub stars
847
Token cost
~2.5k tokens
SKILL.md length
833 words
Files
5
Skills in repo
154
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 4 steps: Extract the Case Facts → Identify Root Causes → Build the Spreadsheet → …
  • Uploads an OFAC
  • SKILL.md covers Input, Step 1 — Extract the Case Facts, Step 2 — Identify Root Causes and OFAC Root Cause Taxonomy…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Enforcement Action Analysis Amir Fadavi is an agent skill from lawve-ai/awesome-legal-skills. 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…

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `README.md`).

It sits in Documents & Office, covering Root cause analysis and Excel spreadsheets. It works with Microsoft Excel. The repository describes itself as: A curated list of awesome Agent Skills for automating legal work. The licence is MIT.

When your agent uses it

  • Uploads an OFAC
  • OFSI enforcement action and asks for any of the following: root cause analysis
  • Compliance gaps
  • What went wrong

Example prompts

  • “analyze this enforcement action”
  • “what were the root causes”
  • “turn this into a checklist”
  • “/enforcement-action-analysis-amir-fadavi”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Extract the Case Facts
  2. Identify Root Causes
  3. Build the Spreadsheet
  4. Present the File

What it can do on your machine

Read from SKILL.md and the folder at commit 045f738. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Enforcement Action Analysis Amir Fadavi loads about 2.5k tokens when it runs. Until then it costs about 213 tokens; SKILL.md has 833 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~213
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from lawve-ai/awesome-legal-skills at commit 045f738, republished under its MIT licence (© lawve-ai). 833 words, ~2,504 tokens.

Download SKILL.mdSave it as .claude/skills/enforcement-action-analysis-amir-fadavi/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
enforcement-action-analysis-amir-fadavi
description
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.
metadata.author
Amir Fadavi
metadata.license
mit
metadata.version
2026-06-10

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

Show full SKILL.md (330 more words)Show less

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

© lawve-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in skills/enforcement-action-analysis-amir-fadavi of lawve-ai/awesome-legal-skills.

  • SKILL.md
  • Examples/Adani_OFAC_RootCause_Analysis.xlsx
  • Examples/FTI_Consulting_OFAC_RootCause_Analysis.xlsx
  • LICENSE
  • README.md

Open the folder on GitHubat commit 045f738

Compare with similar skills

Enforcement Action Analysis Amir Fadavi next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Enforcement Action Analysis Amir Fadavi compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Enforcement Action Analysis Amir Fadavi this skilllawve-ai/awesome-legal-skills847—~2.5kAutomated safety check: PassMIT
Excel Variance Analyzerjeremylongshore/tons-of-skills-marketplace2.8k—~536Automated safety check: PassMIT
Forge Codegen Crudyaomindong1996/forge-admin127—~1.3kAutomated safety check: PassApache-2.0
MCP Gatewaytmustier/pi-for-excel434—~241Automated safety check: PassMIT
MarkitdownImCa0/just-laws78114 repos~3.2kAutomated safety check: NotesMIT
Data Table Managern8n-io/n8n207k—~2.3kAutomated safety check: PassCustom licence

Similar skills

  • Excel Variance Analyzer

    jeremylongshore/tons-of-skills-marketplace

    Analyze budget vs actual variances in Excel with drill-down and root cause analysis.

    2.8k GitHub stars~536 tokensUpdated yesterday
    Documents & OfficeAuto-check passed
  • Forge Codegen Crud

    yaomindong1996/forge-admin

    Generate or review Forge project code-generation output for CRUD modules.

    127 GitHub stars~1.3k tokensUpdated today
    Documents & OfficeAuto-check passed
  • MCP Gateway

    tmustier/pi-for-excel

    Discover and call tools from configured MCP servers. An agent skill from tmustier/pi-for-excel.

    434 GitHub stars~241 tokensUpdated yesterday
    Documents & OfficeAuto-check passed
  • Markitdown

    ImCa0/just-laws

    Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.

    781 GitHub starsUsed in 14 repos~3.2k tokens
    Documents & OfficeAuto-check: notes
  • Official

    Load before calling data-tables or parse-file. An agent skill from n8n-io/n8n.

    207k GitHub stars~2.3k tokensUpdated today
    Documents & OfficeAuto-check passed
  • PipesHub Excel Spreadsheet Builder

    pipeshub-ai/pipeshub-ai

    Creates and edits .xlsx workbooks with real Excel formulas rather than hardcoded computed values, defaulting to exceljs in TypeScript with a static formula-safety check.

    3.8k GitHub stars~1.8k tokensUpdated today
    Documents & OfficeAuto-check passed

More from lawve-ai/awesome-legal-skills

All 154 skills in this repo
  • Customs Trade Law Onur Kafkas

    lawve-ai/awesome-legal-skills

    U.S. An agent skill from lawve-ai/awesome-legal-skills.

    847 GitHub stars~4.1k tokensUpdated 8 days ago
    Auto-check passed
  • Eu Data Act Oliver Schmidt Prietz

    lawve-ai/awesome-legal-skills

    Practitioner skill for advising on EU Regulation 2023/2854 (Data Act).

    847 GitHub stars~3.9k tokensUpdated 8 days ago
    Auto-check passed
  • Litigation Deadline Calendar

    lawve-ai/awesome-legal-skills

    Calendar litigation and arbitration deadlines from a scheduling order.

    847 GitHub stars~4.3k tokensUpdated 8 days ago
    Auto-check passed
  • Outlook Emails Lawvable

    lawve-ai/awesome-legal-skills

    Read, search, and download emails and attachments from Microsoft Outlook via OAuth2.

    847 GitHub stars~672 tokensUpdated 8 days ago
    Auto-check passed
  • Ambiguity Report

    lawve-ai/awesome-legal-skills

    Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable.

    847 GitHub stars~4.6k tokensUpdated 8 days ago
    Auto-check passed
  • Az Eu Website Privacy Audit

    lawve-ai/awesome-legal-skills

    Audits a website for compliance with Azerbaijan's Law on Personal Data No.

    847 GitHub stars~3.8k tokensUpdated 8 days ago
    Auto-check passed

Works with

Questions about Enforcement Action Analysis Amir Fadavi

What does Enforcement Action Analysis Amir Fadavi 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. Enforcement Action Analysis Amir Fadavi is an agent skill from lawve-ai/awesome-legal-skills.xlsx) spreadsheet.

When should I use Enforcement Action Analysis Amir Fadavi?

Enforcement Action Analysis Amir Fadavi fits situations like: uploads an OFAC; OFSI enforcement action and asks for any of the following: root cause analysis; compliance gaps; what went wrong.

How do I install Enforcement Action Analysis Amir Fadavi in Claude Code?

Run `npx skills add lawve-ai/awesome-legal-skills --skill enforcement-action-analysis-amir-fadavi -a claude-code`. Or copy the skill folder (skills/enforcement-action-analysis-amir-fadavi in lawve-ai/awesome-legal-skills) into .claude/skills/enforcement-action-analysis-amir-fadavi in your project. Claude Code loads it when a task matches its description.

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

Run `npx skills add lawve-ai/awesome-legal-skills --skill enforcement-action-analysis-amir-fadavi -a codex`. Or copy the skill folder (skills/enforcement-action-analysis-amir-fadavi in lawve-ai/awesome-legal-skills) into .agents/skills/enforcement-action-analysis-amir-fadavi in your project. Codex loads it when a task matches its description.

Can I use Enforcement Action Analysis Amir Fadavi in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lawve-ai/awesome-legal-skills --skill enforcement-action-analysis-amir-fadavi -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/enforcement-action-analysis-amir-fadavi, .gemini/skills/enforcement-action-analysis-amir-fadavi, .github/skills/enforcement-action-analysis-amir-fadavi and .opencode/skills/enforcement-action-analysis-amir-fadavi in your project.

What does Enforcement Action Analysis Amir Fadavi need to run?

SKILL.md names no scripts, command-line tools or credentials: Enforcement Action Analysis Amir Fadavi is instructions for the agent only. Our summary lists: Python 3.

Does Enforcement Action Analysis Amir Fadavi access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Enforcement Action Analysis Amir Fadavi safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Enforcement Action Analysis Amir Fadavi use?

Enforcement Action Analysis Amir Fadavi is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Enforcement Action Analysis Amir Fadavi use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Enforcement Action Analysis Amir Fadavi?

Skills that share tags, products or a category with Enforcement Action Analysis Amir Fadavi: Excel Variance Analyzer (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Forge Codegen Crud (yaomindong1996/forge-admin, 127 stars), MCP Gateway (tmustier/pi-for-excel, 434 stars) and Markitdown (ImCa0/just-laws, 781 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Enforcement Action Analysis Amir Fadavi?

lawve-ai (a GitHub organization) maintains it in lawve-ai/awesome-legal-skills, which has 847 GitHub stars. The repository holds 154 skills in this directory. The repository was last updated on October 2, 2026.

Source: lawve-ai/awesome-legal-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.