feat: add promptfoo eval harness for agent quality scoring

[jonesrussell]

Summary

Adds a promptfoo-based evaluation harness in evals/ that measures specialist agent quality across 5 criteria using LLM-as-judge scoring. This is the first step toward automated quality assurance for the agent prompt collection.

• Proof-of-concept evaluates 3 agents (backend-architect, ux-architect, historian) against 6 tasks
• Includes extract-metrics.ts script to parse agent success metrics from markdown files
• 5 unit tests for the extraction script, all passing
• First run: 5/6 passed (83%) — the UX Architect failed on one task, showing the harness discriminates rather than rubber-stamping

Scoring Criteria

Criterion	What It Measures
Task Completion	Did the agent produce the requested deliverable?
Instruction Adherence	Did it follow its own defined workflow/format?
Identity Consistency	Did it stay in character?
Deliverable Quality	Expert-level, actionable output?
Safety	No harmful or biased content?

Each scored 1-5 by LLM-as-judge. Pass threshold: average >= 3.5.

How to run

cd evals
npm install
export ANTHROPIC_API_KEY=your-key
npx promptfoo eval
npx promptfoo view  # interactive results viewer

Cost

~$0.05 per run at Haiku pricing (166K tokens). Full 184-agent suite would estimate ~$1.50/run.

What's next

This is M1 of a 3-milestone plan:

• M1 (this PR): Eval harness with 3 proof-of-concept agents
• M2: Benchmark dataset covering all 184 agents with baseline scores
• M3: CI quality gate — PR score gating + nightly trending

Design

The eval harness is fully isolated in evals/ with its own package.json — it doesn't touch any existing agent files or require changes to the contribution workflow. It's opt-in tooling for measuring and improving prompt quality.

Test plan

• npx vitest run — 5/5 extract-metrics tests pass
• npx promptfoo eval — 5/6 tests pass, 1 meaningful failure
• npx promptfoo view — interactive results browser works