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.
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.
npx skills add anthropics/financial-services --skill ai-readinessgit clone --depth 1 https://github.com/anthropics/financial-servicesWrote 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.
[](https://agentmods.dev/skills/anthropics/financial-services/ai-readiness)<a href="https://agentmods.dev/skills/anthropics/financial-services/ai-readiness"><img src="https://agentmods.dev/badge/skills/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.
<a href="https://agentmods.dev/skills/anthropics/financial-services/ai-readiness"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/ai-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00112 | $0.01380 |
| Opus 5 | $0.00056 | $0.00690 |
| Sonnet 5 | $0.00022 | $0.00276 |
| Haiku 4.5 | $0.00011 | $0.00138 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-readiness — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio AI Readiness
Workflow
Step 1: Connect to Portfolio Data
First, ask the user where the portfolio materials live. Don't assume — offer the options:
- MCP servers — data room, SharePoint, Google Drive, or a portfolio-ops database if one is connected
- Local files — a folder path on disk with quarterly decks, financials, board packs
- File uploads — drag PDFs, PowerPoint, or Excel directly into the conversation
Once connected, pull quarterly updates, board decks, and financials for the portfolio (or a subset). For each company, extract: sector, revenue, headcount by function, tech stack mentioned, and any AI/automation initiatives already in flight.
If the user provides a single company, still run the scan but skip the cross-portfolio ranking.
Ask up front if not obvious from materials:
- Hold period remaining per company (AI payback matters less 12 months from exit)
- Whether any portco has already deployed something that worked
Step 2: Per-Company Scan
For each company, answer three gate questions. All three yes → Go. Any no → Wait with a note on what unblocks it.
- Is the data there? Can they produce a clean input for the use case — customer list, invoice feed, contract repository — without a 6-month data project first?
- Is there an owner? Someone on the management team who will drive this, not a sponsor who will "support" it.
- Can we pilot in 30 days? One team, one workflow, off-the-shelf tooling. If the answer starts with "first we'd need to...", it's not a quick win.
Then identify the top 2-3 leverage points. Look for these patterns in the cost structure and operations:
Back Office (usually fastest to pilot)
- Invoice processing, AP/AR matching, expense categorization
- Contract abstraction — vendor agreements, leases, customer MSAs
- Month-end close: reconciliations, flux commentary, lender reporting first drafts
Revenue / Front Office
- RFP and proposal first drafts — big lever if revenue is project-based
- Sales call summaries and CRM hygiene
- Customer support ticket triage and first-response drafting
- Quoting for configured / complex products
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.
- today First seen · 100 lines · 112 tokens per session scan A b1bed0ae1234
ai-readiness is a skill published in the GitHub repository anthropics/financial-services (34,793 stars, last pushed today), licensed Apache-2.0. It adds 112 tokens to every session and 1,380 once invoked, about $0.0006 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.
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