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.
git 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/commands/anthropics/financial-services/comps)<a href="https://agentmods.dev/commands/anthropics/financial-services/comps"><img src="https://agentmods.dev/badge/commands/anthropics/financial-services/comps/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/commands/anthropics/financial-services/comps"><img src="https://agentmods.dev/badge/commands/anthropics/financial-services/comps.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00008 | $0.01118 |
| Opus 5 | $0.00004 | $0.00559 |
| Sonnet 5 | $0.00002 | $0.00224 |
| Haiku 4.5 | $0.00001 | $0.00112 |
Grade A, and why
comps 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 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.
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:
- comps — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparable Company Analysis Command
Build an institutional-grade comparable company analysis with operating metrics, valuation multiples, and statistical benchmarking.
Workflow
Step 1: Gather Company Information
If a company name or ticker is provided, use it. Otherwise ask:
- "What company would you like to analyze?"
Step 2: Load Comps Analysis Skill
Use skill: "comps-analysis" to build the analysis:
-
Clarify the analysis purpose:
- "What's the key question?" (valuation, efficiency, growth comparison)
- "Who is the audience?" (IC, board, quick reference)
- "Do you have a preferred format or template?"
-
Identify peer group (4-6 comparable companies):
- Similar business model
- Similar scale/market cap range
- Same industry/sector
- Geographic comparability
-
Gather data (prioritize MCP sources if available):
- Operating metrics: Revenue, Growth, Gross Margin, EBITDA, EBITDA Margin
- Valuation: Market Cap, Enterprise Value, EV/Revenue, EV/EBITDA, P/E
- Additional metrics based on industry (Rule of 40 for SaaS, etc.)
-
Build the analysis:
- Operating Statistics section with company data + statistics (Max, 75th, Median, 25th, Min)
- Valuation Multiples section with same statistical summary
- Notes & Methodology documentation
Step 3: Create Excel Output
Generate Excel file with:
- Header block (analysis title, companies, date, units)
- Operating Statistics & Financial Metrics section
- Valuation Multiples section
- Statistical summary for each metric
- Notes section documenting sources and methodology
Step 4: Deliver Output
Provide:
- Excel file (.xlsx) - the comps analysis
- Summary highlighting:
- Peer group selection rationale
- Key insights (who trades at premium/discount)
- Median multiples for reference
Output Format Reference
┌─────────────────────────────────────────────────────────────────┐
│ [SECTOR] - COMPARABLE COMPANY ANALYSIS │
│ [Company 1] • [Company 2] • [Company 3] • [Company 4] │
│ As of [Date] | All figures in USD Millions │
├─────────────────────────────────────────────────────────────────┤
│ OPERATING STATISTICS & FINANCIAL METRICS │
├──────────┬─────────┬─────────┬──────────┬─────────┬────────────┤
│ Company │ Revenue │ Growth │ Gross │ EBITDA │ EBITDA │
│ │ (LTM) │ (YoY) │ Margin │ (LTM) │ Margin │
├──────────┼─────────┼─────────┼──────────┼─────────┼────────────┤
│ [Data rows for each company] │
│ │
│ Maximum │ =MAX │ =MAX │ =MAX │ =MAX │ =MAX │
│ 75th % │ =QUART │ =QUART │ =QUART │ =QUART │ =QUART │
│ Median │ =MEDIAN │ =MEDIAN │ =MEDIAN │ =MEDIAN │ =MEDIAN │
│ 25th % │ =QUART │ =QUART │ =QUART │ =QUART │ =QUART │
│ Minimum │ =MIN │ =MIN │ =MIN │ =MIN │ =MIN │
├─────────────────────────────────────────────────────────────────┤
│ VALUATION MULTIPLES │
├──────────┬──────────┬──────────┬──────────┬───────────┬────────┤
│ Company │ Mkt Cap │ EV │ EV/Rev │ EV/EBITDA │ P/E │
├──────────┼──────────┼──────────┼──────────┼───────────┼────────┤
│ [Data rows + statistics] │
└─────────────────────────────────────────────────────────────────┘
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 · 110 lines · 8 tokens per session scan A 88a71a5c60a7
comps is a command published in the GitHub repository anthropics/financial-services (34,808 stars, last pushed yesterday), licensed Apache-2.0. It adds 8 tokens to every session and 1,118 once invoked, about $0.0000 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.
Other commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.
finops-status
Orientation — say where an opportunity or assignment sits in the five-step FinOps lifecycle and what unlocks next. Useful when a record has no active stage: an opportunity while its assignments do the work, an assignment whose plan has not been approved yet, or a rejected or archived assignment. Read-only; mutates…