bi-validation
Validate BI dataset, chart, and dashboard changes after publish by checking object creation, refresh success, and key metric consistency against expected tolerances
Best for BI deployment pipelinesWorks with GitHub
⌘source
- author
@Datus-ai- language
- Python
★1.2kstars
179forks
Apr 22, 2026
✦overview.md
Key Features
- ·Validates BI dataset, chart, and dashboard changes after publish
- ·Checks object creation and refresh success
- ·Compares key metrics against expected tolerances
- ·Confirms dashboard wiring and configuration
- ·Blocks rollout when outputs drift materially
Use Cases
- →Verifying a dataset or chart was created successfully after publishing
- →Confirming dashboard updates are wired correctly post-deployment
- →Comparing refreshed BI metrics against expected values or tolerances
- →Blocking rollout when BI outputs drift beyond acceptable limits
Best for
- ✓BI deployment pipelines
- ✓Dashboard release validation
- ✓Data quality monitoring
Not ideal for
- !One-off BI analysis
- !BI platform setup
- !Real-time data monitoring
FAQs
name
bi-validation
description
Validate BI dataset, chart, and dashboard changes after publish by checking object creation, refresh success, and key metric consistency against expected tolerances
tags:["bi","dashboard","publish","validation","metrics"]
version:1.0.0
user_invocable:false
disable_model_invocation:false
allowed_agents:["gen_dashboard"]
BI Validation
Use this skill after publishing BI changes. It assumes BI platform tools exist and can create or inspect datasets, charts, and dashboards.
When to use this skill
Activate when you need to:
- verify a dataset or chart was created successfully
- verify a dashboard update is wired correctly
- compare refreshed BI metrics against expected values or tolerances
- block rollout when BI outputs drift materially
Core workflow
- Identify the target dashboard by ID.
- Call
get_dashboardto confirm it exists and retrieve the full chart list. - For EVERY chart on the dashboard, call
get_chartand inspect its configuration. Do not validate only a subset. - Verify
chart_type,metrics,x_axis,dimensions, anddataset_idagainst the intended design for each chart. - When
get_chart_datais supported on the active platform, call it for every chart to confirm the chart returns data without backend errors. - Compare key numeric values when expected results or tolerances are available. If exact expectations are not available, still treat successful
get_chart_dataexecution as a required runtime check.
$install
1-click copynpx skills add Datus-ai/Datus-agent --skill bi-validationSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
3/ 5
mediumPartially actionable with several concrete steps, but still missing important details.