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 earnings-previewgit 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/earnings-preview)<a href="https://agentmods.dev/skills/anthropics/financial-services/earnings-preview"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/earnings-preview/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/earnings-preview"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/earnings-preview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector 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 | $0.00090 | $0.00621 |
| Opus 5 | $0.00045 | $0.00311 |
| Sonnet 5 | $0.00018 | $0.00124 |
| Haiku 4.5 | $0.00009 | $0.00062 |
Grade A, and why
earnings-preview 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 10d ago.
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:
- earnings-preview — 100% identical, 0 lines differ
- earnings-preview — 95% identical, 7 lines differ
- earnings-preview — 95% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Preview
Workflow
Step 1: Gather Context
- Identify the company and reporting quarter
- Pull consensus estimates via web search (revenue, EPS, key segment metrics)
- Find the earnings date and time (pre-market vs. after-hours)
- Review the company's prior quarter earnings call for any guidance or commentary
Step 2: Key Metrics Framework
Build a "what to watch" framework specific to the company:
Financial Metrics:
- Revenue vs. consensus (total and by segment)
- EPS vs. consensus
- Margins (gross, operating, net) — expanding or contracting?
- Free cash flow
- Forward guidance vs. consensus
Operational Metrics (sector-specific):
- Tech/SaaS: ARR, net retention, RPO, customer count
- Retail: Same-store sales, traffic, basket size
- Industrials: Backlog, book-to-bill, price vs. volume
- Financials: NIM, credit quality, loan growth, fee income
- Healthcare: Scripts, patient volumes, pipeline updates
Step 3: Scenario Analysis
Build 3 scenarios with stock price implications:
| Scenario | Revenue | EPS | Key Driver | Stock Reaction |
|---|---|---|---|---|
| Bull | ||||
| Base | ||||
| Bear |
For each scenario:
- What would need to happen operationally
- What management commentary would signal this
- Historical context — how has the stock moved on similar prints?
Step 4: Catalyst Checklist
Identify the 3-5 things that will determine the stock's reaction:
- [Metric] vs. [consensus/whisper number] — why it matters
- [Guidance item] — what the buy-side expects to hear
- [Narrative shift] — any strategic changes, M&A, restructuring
Step 5: Output
One-page earnings preview with:
- Company, quarter, earnings date
- Consensus estimates table
- Key metrics to watch (ranked by importance)
- Bull/base/bear scenario table
- Catalyst checklist
- Trading setup: recent stock performance, implied move from options
Important Notes
- Consensus estimates change — always note the source and date of estimates
- "Whisper numbers" from buy-side surveys are often more relevant than published consensus
- Historical earnings reactions help calibrate expectations (search for "[company] earnings reaction history")
- Options-implied move tells you what the market expects — compare to your scenarios
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.
- 10d ago First seen · 74 lines · 90 tokens per session scan A a30d383a6f1c
earnings-preview is a skill published in the GitHub repository anthropics/financial-services (34,762 stars, last pushed 15d ago), licensed Apache-2.0. It adds 90 tokens to every session and 621 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 2026-08-30.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.