# Agentic Engineering Artifacts

This directory contains artifacts from agentic AI-assisted engineering processes applied to the speak project. These are living documents that track how AI agents have contributed to improving the project.

## Contents

### skill-optimization/
**Date:** 2026-01-11  
**Method:** Agent Focus Group + Iterative Refinement Protocol  
**Models:** Claude Haiku 4.5, Sonnet 4.5, Opus 4.5

Systematic optimization of SKILL.md documentation using multi-model consensus testing. The process ran 5 rounds of focus group testing, evolving both the skill document and the evaluation rubric based on agent feedback.

**Key artifacts:**
- `OPTIMIZATION_REPORT.md` — Full report with rubric evolution and improvement summary
- `SKILL_OPTIMIZED.md` — Final optimized skill document
- `iterations/` — All intermediate versions (v0-v5) showing evolution

**Outcome:** 41% content increase with dramatically improved agent comprehension across all tested tasks (voice cloning, PDF audiobooks, multi-voice podcasts).

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## Purpose

These artifacts serve multiple purposes:

1. **Transparency** — Document how AI agents contributed to the project
2. **Reproducibility** — Show the methodology and iterations
3. **Learning** — Capture what worked and what didn't
4. **Evolution** — Track ongoing improvements over time

## Contributing New Artifacts

When adding new agentic engineering work:

1. Create a subdirectory with descriptive name (e.g., `api-redesign/`, `test-generation/`)
2. Include a report summarizing the process and outcomes
3. Preserve intermediate artifacts that show evolution
4. Document the models and methods used