Claude for Financial Services is a repository of reference AI agents, skills, and data connectors built by Anthropic for the financial-services workflows it sees most: investment banking, equity research, private equity, and wealth management. It is aimed at the people who already do that work and at the engineers who support them — anyone who wants a working starting point for analyst tasks rather than a finished, locked-down product. It is open source under Apache-2.0, with Python as its main language.
What it does
The repository is organised in two layers. The first is a set of named, end-to-end workflow agents, each one titled after the job it runs: a Pitch Agent for coverage and advisory work, a Market Researcher, a GL Reconciler, and others in the same table. Each agent plugin is self-contained — it bundles the skills it needs, so installing the agent is the whole installation step.
The second layer is the vertical plugins underneath: the skills, slash commands, and data connectors themselves, bundled by financial-services vertical. If you do not want a full agent and only want the pieces, you can install these on their own and get commands like /comps, /dcf, and /earnings plus the connectors that feed them.
The video walks the Pitch Agent through a single chain: a sector prompt becomes a comparable-companies table, then a precedent transactions table, then a full LBO model, and the agent compiles all three into one branded pitch deck. The GL Reconciler works the same way on the accounting side, flagging ledger breaks instead of building models.
One thing the README is emphatic about, and it belongs in any honest description: nothing here is investment, legal, tax, or accounting advice. These agents draft analyst work product — models, memos, research notes, reconciliations — for a qualified professional to review. They do not make recommendations, execute transactions, bind risk, post to a ledger, or approve onboarding. Every output is staged for human sign-off, and the firm running them is responsible for verifying what comes out.
How it works
Everything in the repository is available two ways from one source. You can install a component as a Claude Cowork plugin, so it shows up in the product with its slash commands attached. Or you can deploy the same thing as a Claude Managed Agent template through the Managed Agents API, calling /v1/agents from behind your own workflow engine. The system prompt and the skills are identical across both paths; the only choice is where the agent runs. The managed-agent templates live in their own managed-agent-cookbooks directory in the repo.
That two-way packaging is the design decision worth noticing. A single definition covers both the desk analyst who wants a plugin in front of them and the platform team that needs the same behaviour running server-side inside an existing pipeline, without maintaining two divergent copies of the prompt.
Getting started
The path depends on what you want:
- A whole workflow. Find the agent in the README's table that matches the job, and install it as a Cowork plugin. Because each agent bundles its own skills, that is the only step.
- Just the tools. Install the vertical plugin for your area instead, and use the slash commands and connectors directly.
- Inside your own system. Take the matching Managed Agent template and deploy it through
/v1/agents, with your workflow engine in front.
In all three cases the README frames what ships as a starting point, not a final configuration: install what matches your work, then tune the prompts, skills, and connectors to how your firm actually does it. The Apache-2.0 license leaves that adaptation open.
When to use it / when not
This fits when the output is a draft that a person reviews — a model to be checked, a memo to be edited, a reconciliation to be signed off. It fits when you want the shape of the workflow handed to you and expect to spend your time on the firm-specific parts: data sources, conventions, house formatting.
It does not fit where an output is binding. The repository itself rules out investment recommendations, trade execution, risk decisions, ledger postings, and onboarding approvals, and a team that quietly relaxes that boundary is taking on the resulting compliance exposure alone. It also is not a turnkey product. If nobody on your side is going to tune the prompts and wire the connectors to your own systems, the agents will stay generic.
Analysts and associates who spend their weeks on comps, precedent tables, IC memos, and tie-outs should look at this closely, and so should the platform engineers who would be asked to put it into production — the second layer of skills and connectors is reusable even if none of the named agents matches your desk exactly. Treat it as scaffolding from the model vendor rather than a finished analyst, keep the human sign-off the README insists on, and it is a credible place to start.