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 windsor-ai/claude-windsor-ai-plugin --skill business-datagit clone --depth 1 https://github.com/windsor-ai/claude-windsor-ai-pluginWrote 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/windsor-ai/claude-windsor-ai-plugin/business-data)<a href="https://agentmods.dev/skills/windsor-ai/claude-windsor-ai-plugin/business-data"><img src="https://agentmods.dev/badge/skills/windsor-ai/claude-windsor-ai-plugin/business-data/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/windsor-ai/claude-windsor-ai-plugin/business-data"><img src="https://agentmods.dev/badge/skills/windsor-ai/claude-windsor-ai-plugin/business-data.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 | $0.00000 | $0.00964 |
| Opus 5 | $0.00000 | $0.00482 |
| Sonnet 5 | $0.00000 | $0.00193 |
| Haiku 4.5 | $0.00000 | $0.00096 |
Grade A, and why
business-data 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 12d 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.
This is a copy
88% identical to windsor-ai-business-data — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Windsor.ai Business Data Skill
Use this skill whenever the user needs business data — marketing analytics, sales metrics, CRM data, ecommerce transactions, financial data, or any other data available through Windsor.ai's 350+ connectors.
When to Use
- User is building a dashboard, report, or data visualization that needs business data from any source
- User asks about ad spend, campaign performance, ROAS, CTR, CPC, or conversion metrics
- User needs CRM, sales, ecommerce, or financial data in their codebase
- User needs to explore what data sources or fields are available
- User wants to seed a project with real data (e.g. for testing, prototyping, or generating fixtures)
- User is building an integration with any platform supported by Windsor.ai
Available Tools
Windsor.ai provides 4 MCP tools:
get_connectors
Lists all connected platforms and their account IDs. Always call this first if you don't know what accounts are available.
get_options
Returns available fields, date filters, and options for a specific connector. Use this to discover what data can be queried before calling get_data.
Parameters:
connector(required): Platform ID like"google_ads","facebook","tiktok","linkedin","googleanalytics4","hubspot","salesforce","searchconsole","instagram","youtube","google_my_business","shopify","stripe","quickbooks", and 300+ moreaccounts(required): List of account IDs fromget_connectors
get_fields
Returns detailed metadata about specific fields — data types, descriptions, available values. Use this when you need to understand the schema before writing code that processes the data.
Parameters:
connector(required): Platform IDfields(required): List of field IDs like["campaign", "spend", "clicks"]
get_data
Retrieves actual data. This is the main query tool.
Parameters:
connector(required): Platform IDaccounts(required): List of account IDsfields(required): Fields to retrieve, e.g.["campaign", "date", "spend", "clicks", "impressions"]date_from/date_to: Date range as"YYYY-MM-DD"date_preset: Shorthand like"last_7d","last_30d","this_month","last_3m"filters: Conditions like[["spend", "gt", 100], "and", ["campaign", "contains", "Sale"]]options: Connector-specific options like{"attribution_window": "7d_view,1d_click"}
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.
- 12d ago First seen · 77 lines · 0 tokens per session scan A a6a45fa51d38
business-data is a skill published in the GitHub repository windsor-ai/claude-windsor-ai-plugin (0 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 964 tokens. A static security scan graded it A with 0 findings. It is 88% identical to windsor-ai-business-data, differing in 4 lines, and is treated as a copy.
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