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 account-risk-early-warninggit 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/account-risk-early-warning)<a href="https://agentmods.dev/skills/microsoft/dataverse-business-skills/account-risk-early-warning"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/account-risk-early-warning/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/account-risk-early-warning"><img src="https://agentmods.dev/badge/skills/microsoft/dataverse-business-skills/account-risk-early-warning.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 | $0.04843 |
| Opus 5 | $0.00040 | $0.02422 |
| Sonnet 5 | $0.00016 | $0.00969 |
| Haiku 4.5 | $0.00008 | $0.00484 |
Grade A, and why
account-risk-early-warning 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 — 545 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Risk Early Warning
Retaining existing customers is more cost-effective than acquiring new ones. This skill monitors key accounts for early warning signals that indicate potential churn or relationship issues, enabling proactive intervention before problems escalate.
Instructions
Step 1: Determine Scope
When user asks "Which of my top accounts are showing warning signs?":
1.1 Define Account Set:
Options:
A) All active accounts owned by user
B) Specific account list (e.g., enterprise tier)
C) Accounts with revenue above threshold
D) Accounts up for renewal in next X months
1.2 Query Target Accounts:
SELECT accountid, name, revenue, numberofemployees, industrycode,
createdon, ownerid, customertypecode, territoryid,
openrevenue, openrevenue_date
FROM account
-- Note: openrevenue and openrevenue_date are rollup fields that may not exist in all orgs.
-- If absent, calculate open pipeline value by querying the opportunity table directly.
WHERE statecode = 0
AND ownerid = '[current_user_id]' -- or specific criteria
ORDER BY revenue DESC
Step 2: Analyze Risk Signals for Each Account
Important: Dataverse SQL Limitations Dataverse SQL does NOT support: subqueries, DATEADD(), GETUTCDATE(), HAVING, DISTINCT, UNION, CASE statements. Use separate queries and calculate date filters programmatically (e.g., calculate '2025-09-01' for 6 months ago).
2.1 Activity Trend Analysis Query recent activities for each account (run separately per account):
SELECT activityid, activitytypecode, createdon, statecode
FROM activitypointer
WHERE regardingobjectid = '[accountid]'
AND createdon > '2025-09-01'
ORDER BY createdon DESC
Calculate Activity Metrics:
For each account:
- Total activities (last 6 months)
- Activities per month trend
- Activity types breakdown
- Days since last activity
- Comparison to account's historical average
Activity Risk Indicators:
| Signal | Warning | Critical |
|---|---|---|
| Days Since Last Activity | 30+ days | 60+ days |
| Month-over-Month Decline | 25%+ decline | 50%+ decline |
| No Meetings/Calls | 45+ days | 90+ days |
| Only Automated Emails | 30+ days | 60+ days |
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 · 545 lines · 80 tokens per session scan A 5be6e2357f3f
account-risk-early-warning 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 4,843 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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