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 ib-check-deckgit 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/ib-check-deck)<a href="https://agentmods.dev/skills/anthropics/financial-services/ib-check-deck"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/ib-check-deck/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/ib-check-deck"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/ib-check-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00116 | $0.00895 |
| Opus 5 | $0.00058 | $0.00447 |
| Sonnet 5 | $0.00023 | $0.00179 |
| Haiku 4.5 | $0.00012 | $0.00089 |
Grade A, and why
ib-check-deck 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 9d 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
2 near-identical copies found in the catalogue:
- ib-check-deck — 100% identical, 0 lines differ
- ib-check-deck — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IB Deck Checker
Perform comprehensive QC on the presentation across four dimensions. Read every slide, then report findings.
Environment check
This skill works in both the PowerPoint add-in and chat. Identify which you're in before starting:
- Add-in — read from the live open deck.
- Chat — read from the uploaded
.pptxfile.
This is read-and-report only — no edits — so the workflow is identical in both.
Workflow
Read the deck
Pull text from every slide, keeping track of which slide each line came from. You'll need slide-level attribution for every finding ("$500M appears on slides 3 and 8, but slide 15 shows $485M"). A deck with 30 slides is too much to hold in working memory reliably — write the extracted text to a file so the number-checking script can process it.
The script expects markdown-ish input with slide markers. Format as:
## Slide 1
[slide 1 text content]
## Slide 2
[slide 2 text content]
1. Number consistency
Run the extraction script on what you collected:
python scripts/extract_numbers.py /tmp/deck_content.md --check
It normalizes units ($500M vs $500MM vs $500,000,000 → same number), categorizes values (revenue, EBITDA, multiples, margins), and flags when the same metric category shows conflicting values on different slides. This is the part most likely to catch something a human missed on the fifth read-through.
Beyond what the script flags, verify:
- Calculations are correct (totals sum, percentages add up, growth rates match the endpoints)
- Unit style is consistent — the deck should pick one of $M or $MM and stick with it
- Time periods are aligned — FY vs LTM vs quarterly, explicitly labeled
2. Data-narrative alignment
Map claims to the data that's supposed to support them. This is where decks go wrong quietly — someone edits the chart on slide 7 and forgets the narrative on slide 4.
- Trend statements ("declining margins") → does the chart actually go that direction?
- Market position claims ("#1 player") → revenue and share data support it?
- Plausibility — "#1 in a $100B market" with $200M revenue is 0.2% share; that's not #1
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 79 lines · 116 tokens per session scan A ddb30ad93603
ib-check-deck is a skill published in the GitHub repository anthropics/financial-services (34,762 stars, last pushed 14d ago), licensed Apache-2.0. It adds 116 tokens to every session and 895 once invoked, about $0.0006 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.
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