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 create-an-assetgit 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/create-an-asset)<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/create-an-asset"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/create-an-asset/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/create-an-asset"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/create-an-asset.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 | $0.00081 | $0.03608 |
| Opus 5 | $0.00041 | $0.01804 |
| Sonnet 5 | $0.00016 | $0.00722 |
| Haiku 4.5 | $0.00008 | $0.00361 |
Grade A, and why
create-an-asset 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.
How it starts
The opening of the file, as written. The whole thing — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create an Asset
Reps waste time reformatting generic templates for each prospect. This skill pulls real Dataverse data — deal context, pain points, products, account details, and relationship history — to generate tailored sales documents that feel specific to the buyer. Assets are saved back to Dataverse as annotations so the full team has access.
Instructions
Step 1: Identify Asset Type and Target
Accept input from the user:
- Asset type: One-pager, proposal outline, executive summary, ROI summary, mutual action plan (MAP), competitive battlecard, or meeting follow-up summary
- Target: Opportunity name, account name, or contact name
- Audience: Economic buyer, technical evaluator, end user, executive sponsor
Step 2: Retrieve Opportunity Context
SELECT opportunityid, name, estimatedvalue, estimatedclosedate, salesstage,
closeprobability, budgetstatus, need, purchasetimeframe, purchaseprocess,
decisionmaker, description, currentsituation, customerneed,
customerpainpoints, msdyn_forecastcategory, ownerid, customerid, accountid
FROM opportunity
WHERE opportunityid = '[opportunityid]'
If searching by name:
SELECT opportunityid, name, estimatedvalue, estimatedclosedate, salesstage,
customerid, ownerid
FROM opportunity
WHERE statecode = 0
AND name LIKE '%[search_term]%'
Step 3: Retrieve Account Details
SELECT accountid, name, industrycode, revenue, numberofemployees, description,
address1_city, address1_stateorprovince, address1_country,
customertypecode, accountcategorycode, websiteurl
FROM account
WHERE accountid = '[accountid]'
Step 4: Retrieve Key Contacts and Stakeholders
SELECT contactid, fullname, jobtitle, emailaddress1, description
FROM contact
WHERE accountid = '[accountid]'
ORDER BY createdon DESC
Identify stakeholder roles from jobtitle field (Economic Buyer, Technical Evaluator, Champion, End User) for use in asset personalization.
Step 5: Retrieve Products Under Consideration
SELECT opportunityproduct.productid, product.name, product.description,
opportunityproduct.quantity, opportunityproduct.priceperunit,
opportunityproduct.tax, opportunityproduct.baseamount
FROM opportunityproduct
JOIN product ON opportunityproduct.productid = product.productid
WHERE opportunityproduct.opportunityid = '[opportunityid]'
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 · 500 lines · 81 tokens per session scan A b75ba5c1cbdc
create-an-asset is a skill published in the GitHub repository microsoft/dataverse-business-skills (50 stars, last pushed 6mo ago), licensed MIT. It adds 81 tokens to every session and 3,608 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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