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 duplicate-detectivegit 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/duplicate-detective)<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/duplicate-detective"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/duplicate-detective/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/duplicate-detective"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/duplicate-detective.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 | $0.00078 | $0.03544 |
| Opus 5.5 | $0.00031 | $0.01418 |
| Sonnet 5.5 | $0.00016 | $0.00709 |
| Haiku 4.5 | $0.00008 | $0.00354 |
Grade A, and why
duplicate-detective 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 2mo 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 — 383 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Duplicate Detective
Sales and marketing teams often accumulate duplicate records due to data entry variations, imports from multiple sources, and inconsistent naming conventions. Standard Dataverse duplicate detection rules are limited (max 5 rules per table) and miss subtle duplicates. This skill provides intelligent duplicate detection to maintain data quality and prevent wasted effort on duplicate outreach.
Instructions
Step 1: Determine Scope of Duplicate Check
Ask the user or infer from context:
- Target Table: Account, Contact, or Lead (or all three)
- Scope:
- Full scan of all records
- Specific subset (e.g., records created in last 30 days)
- Check against a specific record (e.g., "Find duplicates of Contoso")
- Matching Threshold: Strict (high confidence only) or Relaxed (include possible matches)
Step 2: Query Source Records
Based on scope, retrieve records to analyze:
For Accounts:
SELECT accountid, name, telephone1, telephone2, emailaddress1,
address1_line1, address1_city, address1_stateorprovince, address1_postalcode,
websiteurl, numberofemployees, industrycode
FROM account
WHERE statecode = 0
For Contacts:
SELECT contactid, firstname, lastname, fullname, emailaddress1, emailaddress2,
telephone1, mobilephone, jobtitle, accountid,
address1_line1, address1_city, address1_stateorprovince, address1_postalcode
FROM contact
WHERE statecode = 0
For Leads:
SELECT leadid, firstname, lastname, fullname, companyname, emailaddress1,
telephone1, mobilephone, jobtitle, address1_line1, address1_city,
address1_stateorprovince, address1_postalcode, websiteurl
FROM lead
WHERE statecode = 0
Step 3: Apply Fuzzy Matching Algorithms
3.1 Company/Account Name Matching Detect duplicates even with variations:
- Abbreviation Handling: "International Business Machines" ↔ "IBM"
- Legal Suffix Normalization: "Contoso Inc." ↔ "Contoso Corporation" ↔ "Contoso LLC"
- Common Abbreviations: "Mfg" = "Manufacturing", "Intl" = "International"
- Word Order Variations: "Microsoft Corporation" ↔ "Corporation Microsoft"
- Punctuation & Spacing: "A.B.C. Company" ↔ "ABC Company"
- The/A Prefix Handling: "The Contoso Group" ↔ "Contoso Group"
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.
- 2mo ago First seen · 383 lines · 78 tokens per session scan A fc78df759e20
duplicate-detective is a skill published in the GitHub repository microsoft/dataverse-business-skills (52 stars, last pushed 7mo ago), licensed MIT. It adds 78 tokens to every session and 3,544 once invoked, about $0.0003 per session on Opus 5.5. 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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