benchmark-qed-setup Skill
Initialize and configure benchmark-qed workspaces for RAG benchmarking. Use when: setting up a new benchmarking project, initializing config files for question generation or evaluation, downloading sample datasets, or modifying benchmark-qed settings.yaml configuration. Also use when the user mentions "benchmark-qed co Published by microsoft in benchmark-qed.
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Is this a fit?
Developers using benchmark-qed, Includes SKILL.md, Reusable instructions
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Copy skill directory
2 months ago · MIT license
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What is benchmark-qed-setup Skill?
Initialize and configure benchmark-qed workspaces for RAG benchmarking. Use when: setting up a new benchmarking project, initializing config files for question generation or evaluation, downloading sample datasets, or modifying benchmark-qed settings.yaml configuration. Also use when the user mentions "benchmark-qed co Published by microsoft in benchmark-qed. 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
- Developers using benchmark-qed
Declared skill metadata
- Source file: .apm/skills/benchmark-qed-setup/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 benchmark-qed-setup Skill
- Use it for developers using benchmark-qed.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/benchmark-qed
- Skill file: .apm/skills/benchmark-qed-setup/SKILL.md
What it does
Initialize and configure benchmark-qed workspaces for RAG benchmarking. Use when: setting up a new benchmarking project, initializing config files for question generation or evaluation, downloading sample datasets, or modifying benchmark-qed settings.yaml configuration. Also use when the user mentions "benchmark-qed co
Skill instructions
Benchmark-QED Workspace Setup Initialize workspaces, generate configuration files, download datasets, and manage settings for the benchmark-qed RAG benchmarking tool. Prerequisites benchmark-qed requires Python 3.11+ and uv. Run commands with uvx to avoid installing globally: bash uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed <command Pin a specific version for reproducibility: bash uvx --from "git+https://github.com/microsoft/benchmark-qed@v1.2.3" benchmark-qed <command If uvx is unavailable, install uv first: bash pip install uv && uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed <command Procedure Step 1 — Initialize a Workspace Option A (Recommended): Interactive wizard The interactive wizard guides you through configuration with sensible defaults: bash uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed init <rootdirectory This walks through: - Config type selection (autoq, autoepairwise, autoereference, au
Verified compatibility and discovery
Frequently asked questions
What is benchmark-qed-setup?
benchmark-qed-setup is a open-source AI agent skill with Copy skill directory. Initialize and configure benchmark-qed workspaces for RAG benchmarking. Use when: setting up a new benchmarking project, initializing config files for question generation or evaluation, downloading.
Who is benchmark-qed-setup best for?
benchmark-qed-setup is best for reusing agent instructions, scripts, and references.
How do I install benchmark-qed-setup?
Install or run benchmark-qed-setup using Copy skill directory. Check benchmark-qed-setup for the latest setup command.
Is benchmark-qed-setup actively maintained?
benchmark-qed-setup may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
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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.
