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Dspy Evaluation Suite

Checked
omidzamani·vlatest·ai-ml·MIT

The Dspy Evaluation Suite assesses and compares different diffusion models’ performance, streamlining model selection for optimal results.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add omidzamani/dspy-skills --skill dspy-evaluation-suite -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

omidzamani

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Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

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  • Pinned to a commit: passed

    Scanned omidzamani/dspy-skills at f5db3b7b1f.

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Commit
f5db3b7b1f
Manifest
skills/dspy-evaluation-suite/SKILL.md
Scanned
2 files, 290 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

The dspy-evaluation-suite skill provides a framework for evaluating AI agent performance. It allows users to define evaluation datasets, metrics, and scoring functions to systematically assess the quality of agent outputs. This facilitates iterative improvement and comparison between different agents or configurations.

When to use it

  • Benchmarking Agent Performance: Compare the effectiveness of multiple AI agents on a standardized task.
  • Evaluating New Prompts/Agents: Quickly determine if changes to prompts or new agent versions improve overall performance.
  • Identifying Failure Modes: Pinpoint specific areas where an agent consistently struggles, enabling targeted refinement.
  • Automated Regression Testing: Integrate evaluation into your development pipeline to ensure that updates don’t degrade existing capabilities.

Key capabilities

  • Evaluation dataset definition
  • Metric specification
  • Scoring function implementation
  • Agent output assessment
  • Performance reporting

Example prompts

  • "Evaluate the agent's performance on the 'question answering' dataset using the 'exact match' metric."
  • "Run a regression test for the summarization task and report any significant changes in score."
  • "Show me the evaluation results for the latest version of the chatbot, compared to the previous version."

Tips & gotchas

The skill requires familiarity with defining datasets and metrics relevant to your specific use case. Proper configuration of scoring functions is crucial for accurate performance assessment; incorrect setup can lead to misleading results.