Dataverse Business Skills is a collection of natural-language instructions that teach AI agents how to follow business processes, policies, and domain knowledge for Microsoft Dataverse. Organizations use the skills with Dataverse environments connected to products such as Power Apps, Dynamics 365, or Power Platform. The catalogue entries are skills from this collection.
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 microsoft/dataverse-business-skills --skill cross-sell-target-identifiergit clone --depth 1 https://github.com/microsoft/dataverse-business-skillsWrote 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/microsoft/dataverse-business-skills/cross-sell-target-identifier)<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/cross-sell-target-identifier"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/cross-sell-target-identifier/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/microsoft/dataverse-business-skills/cross-sell-target-identifier"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/cross-sell-target-identifier.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 | $0.00080 | $0.03924 |
| Opus 5 | $0.00040 | $0.01962 |
| Sonnet 5 | $0.00016 | $0.00785 |
| Haiku 4.5 | $0.00008 | $0.00392 |
Grade A, and why
cross-sell-target-identifier 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.
How it starts
The opening of the file, as written. The whole thing — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Sell Target Identifier
When launching a new product or looking to expand product adoption, sales teams need to identify which existing customers are most likely to purchase. This skill analyzes the characteristics of successful customers for a given product, finds similar customers who don't own it yet, and provides prioritized recommendations with justification.
Instructions
Step 1: Identify the Target Product
When user asks "Which customers should I pitch Product X to?":
- Identify the Product:
SELECT productid, name, description, producttypecode, productstructure
FROM product
WHERE name LIKE '%[product name]%'
AND statecode = 0
- Confirm with user if multiple matches
Step 2: Analyze Successful Product X Customers
2.1 Find Customers Who Own Product X Query won opportunities that included the target product:
SELECT op.opportunityid, op.customerid, op.accountid, op.actualvalue,
op.actualclosedate, op.salesstage
FROM opportunity op
JOIN opportunityproduct opp ON op.opportunityid = opp.opportunityid
WHERE opp.productid = '[target_product_id]'
AND op.statecode = 1
Note: A product may appear in multiple opportunityproduct rows per opportunity. Deduplicate opportunityids programmatically after fetching results.
2.2 Build Success Profile from Winning Accounts For each winning account, gather firmographic data:
SELECT accountid, name, industrycode, numberofemployees, revenue,
customertypecode, address1_stateorprovince, address1_country,
ownershipcode, createdon
FROM account
WHERE accountid IN ([list of winning account ids])
2.3 Analyze Success Patterns
Firmographic Analysis:
Calculate distribution across successful customers:
- Industry breakdown (industrycode): Which industries buy most?
- Company size (numberofemployees): What's the typical range?
- Revenue range: What's the typical revenue bracket?
- Geography (address1_stateorprovince/country): Regional concentrations?
- Customer type (customertypecode): Are they customers, partners, etc.?
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 · 451 lines · 80 tokens per session scan A de140164108b
cross-sell-target-identifier is a skill published in the GitHub repository microsoft/dataverse-business-skills (50 stars, last pushed 6mo ago), licensed MIT. It adds 80 tokens to every session and 3,924 once invoked, about $0.0004 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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