experiment-catalog-demo-data Skill
Generate realistic demo data for the experiment catalog including projects, experiments, permutations, results with metrics, metric definitions, and tags. Published by microsoft in experiment-catalog.
Decision snapshot
Is this a fit?
Deployment, Data analysis, Includes SKILL.md, Reusable instructions
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Copy skill directory
2 months ago · MIT license
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What is experiment-catalog-demo-data Skill?
Generate realistic demo data for the experiment catalog including projects, experiments, permutations, results with metrics, metric definitions, and tags. Published by microsoft in experiment-catalog. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Deployment
- Data analysis
- Deployment use cases
- Data analysis use cases
Declared skill metadata
- Declared license: MIT
- Compatibility: Requires Python 3.10+, requests, and a reachable Experiment Catalog API.
- Source file: .agents/skills/experiment-catalog-demo-data/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use experiment-catalog-demo-data Skill
- Use it for deployment.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/experiment-catalog
- Skill file: .agents/skills/experiment-catalog-demo-data/SKILL.md
What it does
Generate realistic demo data for the experiment catalog including projects, experiments, permutations, results with metrics, metric definitions, and tags.
Skill instructions
Execution Model [!IMPORTANT] Do not execute this skill directly in the main thread. Delegate the data generation task to its own sub-agent and pass this skill path to that sub-agent. Keep demo-data generation separate from deployment or pipeline tasks. Store the created dataset summary in session artifacts instead of replaying bulk logs. Generate Demo Data Use this skill to populate a running Experiment Catalog instance with realistic sample projects, experiments, permutations, results, metric definitions, and tags. Open references/full-guide.md for the long-form walkthrough. This skill uses the catalog API and does not perform Azure ARM control-plane provisioning. Do not apply the ISE asset telemetry ID to demo-data API calls. Prerequisites - Python 3.10+ - requests - a reachable catalog API, usually http://localhost:6010 Fast Path Run with defaults: bash python scripts/generatedemodata.py Override the target: bash python scripts/generatedemodata.py --base-url http://localhost:8080 Ke
Verified compatibility and discovery
Frequently asked questions
What is experiment-catalog-demo-data?
experiment-catalog-demo-data is a open-source AI agent skill with Copy skill directory. Generate realistic demo data for the experiment catalog including projects, experiments, permutations, results with metrics, metric definitions, and tags.
Who is experiment-catalog-demo-data best for?
experiment-catalog-demo-data is best for reusing agent instructions, scripts, and references, deployment workflows, data analysis workflows.
How do I install experiment-catalog-demo-data?
Install or run experiment-catalog-demo-data using Copy skill directory. Check experiment-catalog-demo-data for the latest setup command.
Is experiment-catalog-demo-data actively maintained?
experiment-catalog-demo-data may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
