ai-readiness

ai-readiness is a command for Claude Code from anthropics/financial-services. It costs 10 tokens per session (99 once invoked), scanned A, original, Apache-2.0.

An AI-opportunity review for a group of companies, ranking possible uses of AI and deciding which ones are worth pursuing. It compares potential effects on earnings before interest, taxes, depreciation, and amortisation (EBITDA).

In plain words
What is it for?
It reviews selected companies, sets a go/no-go decision for each, ranks quick wins by expected EBITDA effect, and identifies ideas that could work across several companies.
Why use it?
It helps investment teams focus on AI ideas most likely to improve business results instead of reviewing vague possibilities company by company.

Command for Claude Code ✓ vendor

Written for Claude Code: argument-hint in frontmatter.

Part of the private-equity plugin — 7 skills, 10 commands shipped together

Good fit It reviews selected companies, sets a go/no-go decision for each, ranks quick wins by expected EBITDA effect, and identifies ideas that could work across several companies.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/anthropics/financial-services/ai-readiness
About the project

Claude for Financial Services is a collection of agents, skills, commands, plugins, and data connectors for investment banking, equity research, private equity, and wealth-management workflows. Financial professionals use it to draft models, memos, research notes, and reconciliations for review by qualified people. The catalogue contains components from these workflows, including agents, skills, plugins, commands, and instructions.

anthropics/financial-services · 34,793 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.

Clone the repo
git clone --depth 1 https://github.com/anthropics/financial-services

Made for: Claude Code.

Or install private-equity, the plugin that ships this one along with the rest of its 7 skills, 10 commands.

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 ai-readiness

README.md
[![agentmods](https://agentmods.dev/badge/commands/anthropics/financial-services/ai-readiness/github.svg)](https://agentmods.dev/commands/anthropics/financial-services/ai-readiness)
Your own site
<a href="https://agentmods.dev/commands/anthropics/financial-services/ai-readiness"><img src="https://agentmods.dev/badge/commands/anthropics/financial-services/ai-readiness/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 ai-readiness

Your own site · 80×15
<a href="https://agentmods.dev/commands/anthropics/financial-services/ai-readiness"><img src="https://agentmods.dev/badge/commands/anthropics/financial-services/ai-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 99 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 $0.00010 $0.00099
Opus 5 $0.00005 $0.00049
Sonnet 5 $0.00002 $0.00020
Haiku 4.5 $0.00001 $0.00010

Measured today against content hash e105144b73a1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ai-readiness 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 today.

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.

plugins/vertical-plugins/private-equity/commands/ai-readiness.md · 9 lines

What it actually says

Load the ai-readiness skill and scan portfolio companies for AI leverage — per-company go / no-go gate, quick wins ranked by EBITDA impact across the portfolio, and replays that hit multiple companies at once.

If a folder or company list is provided, use it. Otherwise ask which companies to include and for their latest quarterly materials.

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. today First seen · 9 lines · 10 tokens per session scan A e105144b73a1

Subscribe to this mod's changes

ai-readiness is a command published in the GitHub repository anthropics/financial-services (34,793 stars, last pushed today), licensed Apache-2.0. It adds 10 tokens to every session and 99 once invoked, about $0.0001 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 2026-09-12.