feat: add promptfoo eval pipeline for skill quality testing

- Add eval/ directory with 10 pilot skill eval configs
- Add GitHub Action (skill-eval.yml) for automated eval on PR
- Add generate-eval-config.py script for bootstrapping new evals
- Add reusable assertion helpers (skill-quality.js)
- Add eval README with setup and usage docs

Skills covered: copywriting, cto-advisor, seo-audit, content-strategy,
aws-solution-architect, agile-product-owner, senior-frontend,
senior-security, mcp-server-builder, launch-strategy

CI integration:
- Triggers on PR to dev when SKILL.md files change
- Detects which skills changed and runs only those evals
- Posts results as PR comments (non-blocking)
- Uploads full results as artifacts

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# Skill Evaluation Pipeline
Automated quality evaluation for skills using [promptfoo](https://promptfoo.dev).
## Quick Start
```bash
# Run a single skill eval
npx promptfoo@latest eval -c eval/skills/copywriting.yaml
# View results in browser
npx promptfoo@latest view
# Run all pilot skill evals
for config in eval/skills/*.yaml; do
npx promptfoo@latest eval -c "$config" --no-cache
done
```
## Requirements
- Node.js 18+
- `ANTHROPIC_API_KEY` environment variable set
- No additional dependencies (promptfoo runs via npx)
## How It Works
Each skill has an eval config in `eval/skills/<skill-name>.yaml` that:
1. Loads the skill's `SKILL.md` content as context
2. Sends realistic task prompts to an LLM with the skill loaded
3. Evaluates outputs against quality assertions (LLM rubrics + programmatic checks)
4. Reports pass/fail per assertion
### CI/CD Integration
The GitHub Action (`.github/workflows/skill-eval.yml`) runs automatically when:
- A PR to `dev` changes any `SKILL.md` file
- The changed skill has an eval config in `eval/skills/`
- Results are posted as PR comments
Currently **non-blocking** — evals are informational, not gates.
## Adding Evals for a New Skill
### Option 1: Auto-generate
```bash
python eval/scripts/generate-eval-config.py marketing-skill/my-new-skill
```
This creates a boilerplate config with default prompts and assertions. **Always customize** the generated config with domain-specific test cases.
### Option 2: Manual
Copy an existing config and modify:
```bash
cp eval/skills/copywriting.yaml eval/skills/my-skill.yaml
```
### Eval Config Structure
```yaml
description: "What this eval tests"
prompts:
- |
You are an expert AI assistant with this skill:
---BEGIN SKILL---
{{skill_content}}
---END SKILL---
Task: {{task}}
providers:
- id: anthropic:messages:claude-sonnet-4-6
config:
max_tokens: 4096
tests:
- vars:
skill_content: file://../../path/to/SKILL.md
task: "A realistic user request"
assert:
- type: llm-rubric
value: "What good output looks like"
- type: javascript
value: "output.length > 200"
```
### Assertion Types
| Type | Use For | Example |
|------|---------|---------|
| `llm-rubric` | Qualitative checks (expertise, relevance) | `"Response includes actionable next steps"` |
| `contains` | Required terms | `"React"` |
| `javascript` | Programmatic checks | `"output.length > 500"` |
| `similar` | Semantic similarity | Compare against reference output |
## Reading Results
```bash
# Terminal output (after eval)
npx promptfoo@latest eval -c eval/skills/copywriting.yaml
# Web UI (interactive)
npx promptfoo@latest view
# JSON output (for scripting)
npx promptfoo@latest eval -c eval/skills/copywriting.yaml --output results.json
```
## File Structure
```
eval/
├── promptfooconfig.yaml # Master config (reference)
├── skills/ # Per-skill eval configs
│ ├── copywriting.yaml # ← 10 pilot skills
│ ├── cto-advisor.yaml
│ └── ...
├── assertions/
│ └── skill-quality.js # Reusable assertion helpers
├── scripts/
│ └── generate-eval-config.py # Config generator
└── README.md # This file
```
## Running Locally vs CI
| | Local | CI |
|---|---|---|
| **Command** | `npx promptfoo@latest eval -c eval/skills/X.yaml` | Automatic on PR |
| **Results** | Terminal + web viewer | PR comment + artifact |
| **Caching** | Enabled (faster iteration) | Disabled (`--no-cache`) |
| **Cost** | Your API key | Repo secret `ANTHROPIC_API_KEY` |
## Cost Estimate
Each skill eval runs 2-3 test cases × ~4K tokens output = ~12K tokens per skill.
At Sonnet pricing (~$3/M input, $15/M output): **~$0.05-0.10 per skill eval**.
Full 10-skill pilot batch: **~$0.50-1.00 per run**.