# Eval Driven Development

> Building rigorous LLM evaluation pipelines. Use when developing AI features to ensure quality and prevent regressions across different model versions.

- **Type:** Skill
- **Install:** `agentstack add skill-ishandutta2007-awesome-agent-skills-eval-driven-development`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ishandutta2007](https://agentstack.voostack.com/s/ishandutta2007)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ishandutta2007](https://github.com/ishandutta2007)
- **Source:** https://github.com/ishandutta2007/Awesome-Agent-Skills/tree/main/skills/eval-driven-development

## Install

```sh
agentstack add skill-ishandutta2007-awesome-agent-skills-eval-driven-development
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Eval-Driven Development

## Overview
Eval-Driven Development ensures that AI features behave deterministically and predictably by testing them against a golden dataset using automated evaluators.

## When to Use
- Building an AI-powered feature
- Tuning prompts or changing underlying models
- Implementing RAG pipelines

## Process
1. **Curate Golden Dataset**: Create diverse test cases including edge cases.
2. **Define Metrics**: Choose appropriate evaluators (e.g., exact match, semantic similarity, LLM-as-a-judge).
3. **Run Pipeline**: Execute the AI feature over the dataset and collect results.
4. **Analyze Failures**: Inspect low-scoring examples and update prompts or logic.
5. **Establish Baseline**: Set a minimum threshold for CI/CD checks.

## Common Rationalizations

| Rationalization | Why It Is Wrong |
|---|---|
| "Manual spot checks are enough." | Spot checks miss regressions across prompts, model versions, and edge cases. |
| "We can add evals after launch." | Without a baseline, you cannot tell whether a later prompt or model change improved behavior. |
| "The judge model says it is good." | LLM judges need criteria, calibration examples, and failure review before they are trustworthy. |

## Red Flags

- No golden dataset exists
- Metrics are vague or not tied to user-visible quality
- Low-scoring examples are ignored instead of inspected
- The baseline threshold is chosen after seeing the desired result

## Verification

Before finishing, confirm:

- The golden dataset includes normal, edge, and known-failure cases
- Metrics and evaluator prompts are committed or otherwise reproducible
- The current model/prompt has a recorded baseline
- Failure examples have been reviewed and categorized

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ishandutta2007](https://github.com/ishandutta2007)
- **Source:** [ishandutta2007/Awesome-Agent-Skills](https://github.com/ishandutta2007/Awesome-Agent-Skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ishandutta2007-awesome-agent-skills-eval-driven-development
- Seller: https://agentstack.voostack.com/s/ishandutta2007
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.