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 agentmods add commands/anthropics/financial-services/setupgit clone --depth 1 https://github.com/anthropics/financial-servicesWhat 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 | $0.00021 | $0.04398 |
| Opus 5 | $0.00010 | $0.02199 |
| Sonnet 5 | $0.00004 | $0.00880 |
| Haiku 4.5 | $0.00002 | $0.00440 |
Grade A, and why
setup scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Pick the curl that matches `gateway_api_format`. (Windows: swap `/dev/null` How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude in Office — Direct Cloud Setup
You are walking an enterprise admin through configuring the Claude Office add-in
to call their own cloud instead of Anthropic's API. The output is a customized
manifest.xml they deploy via M365 Admin Center.
Before anything else: the setup log lives at
~/Desktop/claude-for-msft-365-install-setup.md (resolve ~ for their platform). If it
exists, read it first — you may be resuming a prior run and can skip completed
steps. Start a new ## Run — <timestamp> section and append each command and
its captured output (IDs, URLs) as you go.
Check for Node.js — Steps 4 and 6 shell out to node and npx. Run
node --version. If it's missing, ask before installing — it's their
machine. If they say yes, brew install node (mac) / winget install OpenJS.NodeJS
(win) / whatever their package manager is. If no, stop here.
When capturing values from the admin (IDs, URLs, secrets pasted back from a console) — don't use AskUserQuestion. That's a choice picker; they're holding a string. Just say "paste the Client ID when you have it" and read it from their next message. Use AskUserQuestion only for the actual branch points (gateway vs vertex, per-user vs org-wide).
Step 1 — How does the add-in reach Claude?
Ask this first, because it's the thing admins get wrong: do you already run an LLM gateway (LiteLLM, Portkey, Kong, etc.)?
- Yes →
gateway. Even if the gateway routes to Vertex or Bedrock under the hood — the add-in talks to your gateway, not to Google or AWS. You just need the gateway URL. - No →
vertexorbedrock. The add-in authenticates directly to the cloud provider. Pick where your infra lives.
| Path | What it means | Provisioning | Manifest keys |
|---|---|---|---|
gateway |
Add-in → your gateway → (whatever) | None | gateway_url (+ gateway_api_format if not /v1/messages) |
vertex |
Add-in → Google Vertex AI, directly | Google OAuth client | gcp_project_id, gcp_region, google_client_id, google_client_secret |
bedrock |
Add-in → AWS Bedrock, directly | IAM OIDC provider + role | aws_role_arn, aws_region |
foundry |
Add-in → Azure AI Foundry, directly | Foundry resource + API key | azure_resource_name, azure_api_key |
foundry (keyless) |
Add-in → Azure AI Foundry, per-user Entra sign-in | Foundry resource + your Entra app | azure_resource_name, entra_sso=1, graph_client_id, entra_scope=https://cognitiveservices.azure.com/.default, gateway_auth_source=entra |
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.
- yesterday First seen · 391 lines · 21 tokens per session scan A 4afae1c4f349
setup is a command published in the GitHub repository anthropics/financial-services (34,594 stars, last pushed 6d ago), licensed Apache-2.0. It adds 21 tokens to every session and 4,398 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.