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 deal-screeninggit 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/deal-screening)<a href="https://agentmods.dev/skills/anthropics/financial-services/deal-screening"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/deal-screening/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/deal-screening"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/deal-screening.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 | $0.00101 | $0.00542 |
| Opus 5 | $0.00051 | $0.00271 |
| Sonnet 5 | $0.00020 | $0.00108 |
| Haiku 4.5 | $0.00010 | $0.00054 |
Grade A, and why
deal-screening 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
3 near-identical copies found in the catalogue:
- deal-screening — 100% identical, 0 lines differ
- deal-screening — 97% identical, 7 lines differ
- deal-screening — 97% identical, 7 lines differ
What it actually says
Deal Screening
Workflow
Step 1: Extract Deal Facts
From the provided CIM, teaser, or description, extract:
- Company: Name, location, sector/subsector
- Description: What they do (1-2 sentences)
- Financials: Revenue, EBITDA, margins, growth rate
- Deal type: Platform, add-on, recap, minority, carve-out
- Asking price / valuation: Multiple, enterprise value if stated
- Seller motivation: Why selling now
- Management: Rolling or exiting
- Key customers: Concentration risk
- Key risks: Obvious red flags
Step 2: Screen Against Criteria
Apply the fund's investment criteria (ask user if not known):
| Criterion | Target | Actual | Pass/Fail |
|---|---|---|---|
| Revenue range | |||
| EBITDA range | |||
| EBITDA margin | |||
| Growth profile | |||
| Sector fit | |||
| Geography | |||
| Deal size / EV | |||
| Valuation (x EBITDA) | |||
| Customer concentration | |||
| Management continuity |
Step 3: Quick Assessment
Provide a 3-part assessment:
- Verdict: Pass / Further Diligence / Hard Pass
- Bull case (2-3 bullets): Why this could be a good deal
- Bear case (2-3 bullets): Key risks and concerns
- Key questions: What you'd need to answer on a first call
Step 4: Output
One-page screening memo suitable for sharing with partners or an IC quick screen.
Important Notes
- Speed matters — screening should take minutes, not hours
- Be direct about red flags. Don't bury concerns
- If financials seem inconsistent or incomplete, flag it explicitly
- Ask for the fund's criteria upfront if this is the first screening
- Save screening criteria in memory for future deals once confirmed
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 · 61 lines · 101 tokens per session scan A 6d3aaf149316
deal-screening is a skill published in the GitHub repository anthropics/financial-services (34,793 stars, last pushed today), licensed Apache-2.0. It adds 101 tokens to every session and 542 once invoked, about $0.0005 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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