KYC Onboarding Review

KYC Onboarding Review is a skill for Claude Code, Codex from zgbrenner/agentcounsel. It costs 51 tokens per session (2,628 once invoked), scanned A, original, MIT.

A first-pass review of a customer or investor onboarding packet for KYC, meaning “know your customer,” and AML, meaning anti-money-laundering, checks. It organizes identity, ownership, control, funds, screening results, gaps, and escalation items for compliance and legal review.

In plain words
What is it for?
It is for extracting due-diligence fields, applying the firm's rules, organizing sanctions, politically exposed person, and adverse-media results, and preparing a draft risk rating.
Why use it?
It brings the required onboarding evidence into one structured file and highlights missing information before a compliance officer decides whether to accept or rate the customer.

Skill for Claude CodeCodex

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

Good fit It is for extracting due-diligence fields, applying the firm's rules, organizing sanctions, politically exposed person, and adverse-media results, and preparing a draft risk rating.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zgbrenner/agentcounsel/kyc-onboarding-review
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 zgbrenner/agentcounsel --skill kyc-onboarding-review
Clone the repo
git clone --depth 1 https://github.com/zgbrenner/agentcounsel

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 KYC Onboarding Review

README.md
[![agentmods](https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/kyc-onboarding-review/github.svg)](https://agentmods.dev/skills/zgbrenner/agentcounsel/kyc-onboarding-review)
Your own site
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/kyc-onboarding-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/kyc-onboarding-review/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 KYC Onboarding Review

Your own site · 80×15
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/kyc-onboarding-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/kyc-onboarding-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,628 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.
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 $0.00051 $0.02628
Opus 5 $0.00026 $0.01314
Sonnet 5 $0.00010 $0.00526
Haiku 4.5 $0.00005 $0.00263

Measured 9d ago against content hash 9a5a7a9a49e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

KYC Onboarding Review 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 9d 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.

skills/financial-crime/kyc-onboarding-review/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

KYC Onboarding Review

Purpose

Produce a structured, review-ready draft KYC (Know Your Customer) onboarding file. The skill inventories an onboarding document packet, extracts the customer due diligence fields, applies the firm's KYC/AML rules grid, organizes any sanctions / PEP / adverse-media screening results, proposes a customer risk rating, and packages gaps and escalation items.

This skill provides workflow discipline and analytical structure. It produces draft work product for review by the firm's compliance function and a supervising attorney. This is not legal advice and not a customer-acceptance decision. The skill recommends; the compliance officer and counsel decide.

Use When

  • A user says "run KYC on this new client," "review this onboarding packet," or "screen this investor for onboarding."
  • A new client or investor is being onboarded, or a periodic KYC refresh is due.
  • A firm needs a structured first-pass file before a compliance officer makes a customer-acceptance or risk-rating decision.
  • An onboarding analyst needs to organize identity, ownership, control, and source-of-funds information against the firm's rules grid.

Required Inputs

  • Onboarding document packet: the actual documents — uploaded or pasted. This typically includes identity documents, entity formation documents, ownership and control documents (UBO declarations, org charts, registers, resolutions), address proof, source-of-funds or source-of-wealth evidence, and tax forms. If no packet is provided, stop and request it.
  • The firm's KYC/AML rules grid or CDD policy: the actual firm document setting out the due diligence rules, required documents by customer type and risk level, and the risk-rating methodology. If not provided, stop and request it. Do not construct rules or document requirements from model background knowledge.
  • High-risk jurisdiction list and risk-rating methodology (if maintained separately from the rules grid).
  • Screening results (optional): sanctions, PEP, and adverse-media results for each named party, if a screening run has been completed. The skill does not perform live screening; it organizes and reviews results that are provided. If no screening has been run, note that screening is pending.
  • Customer context: applicant type (individual, entity, trust) and the nature of the intended business relationship.

Read the full file on GitHub · 146 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. 9d ago First seen · 146 lines · 51 tokens per session scan A 9a5a7a9a49e8

Subscribe to this mod's changes

KYC Onboarding Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 2,628 once invoked, about $0.0003 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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