waza
**WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.
Works with
---
name: waza
description: **WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.
license: MIT
---
# Waza
> "The way of technique — measure, refine, master."
A Go CLI tool for evaluating AI agent skills through structured benchmarks. Define test cases in YAML, run them against agent engines, and validate results with pluggable scoring validators.
## Help
When user says "waza help" or asks how to use waza:
```
╔══════════════════════════════════════════════════════════════════╗
║ WAZA - CLI Tool for Evaluating Agent Skills ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ COMMANDS: ║
║ waza run <eval.yaml> # Run an evaluation benchmark ║
║ waza init [directory] # Initialize a new eval suite ║
║ waza generate <SKILL.md> # Generate eval from SKILL.md ║
║ waza compare <r1> <r2> ... # Compare result files ║
║ waza dev [skill-path] # Improve SKILL.md compliance ║
║ ║
║ RUN FLAGS: ║
║ --context-dir, -c Fixtures directory (default: ./fixtures) ║
║ --output, -o Save results JSON to file ║
║ --verbose, -v Verbose output ║
║ --task, -t Filter tasks by name (repeatable) ║
║ --parallel, -p Run tasks in parallel ║
║ --workers, -w Number of parallel workers ║
║ --transcript-dir Save per-task transcripts ║
║ ║
║ COMPARE FLAGS: ║
║ --format, -f Output format: table or json ║
║ ║
║ GENERATE FLAGS: ║
║ --output-dir, -d Output directory for generated files ║
║ ║
║ DEV FLAGS: ║
║ --target Adherence level: low|medium|high ║
║ --max-iterations Max improvement iterations (default: 5) ║
║ --auto Auto-apply without prompting ║
║ ║
║ WORKFLOW: ║
║ 1. waza init my-eval # Scaffold eval suite ║
║ 2. Edit eval.yaml + tasks # Define test cases ║
║ 3. waza run eval.yaml -v # Execute benchmark ║
║ 4. waza compare a.json b.json # Cross-model comparison ║
║ ║
║ FIXTURE ISOLATION: ║
║ Each task gets a fresh temp workspace with fixtures copied ║
║ in. Original fixtures are never modified. ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
```
## Commands
### `waza run`
Run an evaluation benchmark from a YAML spec file.
```bash
# Run with default mock engine
waza run path/to/eval.yaml --context-dir path/to/fixtures
# Verbose output with results saved
waza run eval.yaml -c ./fixtures -v -o results.json
# Filter to specific tasks
waza run eval.yaml -t "task-name-1" -t "task-name-2"
# Parallel execution
waza run eval.yaml --parallel --workers 4
# Save per-task transcripts
waza run eval.yaml --transcript-dir ./transcripts
```
### `waza init`
Initialize a new evaluation suite with a compliant directory structure.
```bash
# Initialize in current directory
waza init
# Initialize in a named directory
waza init my-eval-suite
```
Creates: `eval.yaml`, `tasks/` with example task, `fixtures/` with example fixture.
### `waza generate`
Generate an eval suite from an existing SKILL.md file.
```bash
# Generate eval from SKILL.md
waza generate path/to/SKILL.md
# Specify output directory
waza generate SKILL.md --output-dir ./my-eval
```
Parses YAML frontmatter (name, description) and creates eval.yaml, starter tasks, and fixtures.
### `waza compare`
Compare results from multiple evaluation runs side by side.
```bash
# Compare two result files
waza compare run1.json run2.json
# Compare three or more
waza compare gpt4.json claude.json gemini.json
# JSON output
waza compare run1.json run2.json --format json
```
Shows per-task score deltas, pass rate differences, and aggregate statistics.
### `waza dev`
Iteratively improve SKILL.md frontmatter compliance with automated scoring.
```bash
# Score current skill and suggest improvements
waza dev skills/my-skill
# Target high compliance level
waza dev skills/my-skill --target high
# Auto-apply improvements without prompts
waza dev skills/my-skill --target medium --auto --max-iterations 3
```
**Compliance Levels:**
- **Low** (< 150 chars or no triggers) — Minimal description
- **Medium** (150+ chars, has triggers) — Basic trigger coverage
- **Medium-High** (+ anti-triggers) — Routing clarity improved
- **High** (+ routing markers like INVOKES/FOR SINGLE OPERATIONS) — Full compliance
**Scoring Checks:**
- Description length (150+ chars required, 1024 max)
- Trigger phrases (USE FOR: patterns)
- Anti-trigger phrases (DO NOT USE FOR: patterns)
- Routing clarity markers (**WORKFLOW SKILL**, INVOKES:, etc.)
- Token budget (500 soft limit, 5000 hard limit)
**Coming Soon:** Trigger accuracy tests (#36), `--skip-integration` (#37), `--fast` (#38), improvement suggestions engine (#34).
## Evaluation Spec Format
```yaml
name: my-eval
skill: my-skill
version: "1.0"
executor: mock # or copilot-sdk
tasks:
- id: task-1
name: "Describe the task"
prompt: "Your prompt to the agent"
expected: "Expected behavior"
validators:
- type: code
config:
language: go
- type: text
config:
pattern: "expected pattern"
```
## Engines
| Engine | Use | Description |
|--------|-----|-------------|
| `mock` | Testing | Returns canned responses for validator development |
| `copilot-sdk` | Production | Executes via Copilot CLI SDK |
## Validators
| Validator | What it checks |
|-----------|---------------|
| `code` | Code compiles / passes syntax check |
| `regex` | Output matches regex pattern |
## Configuration
| Setting | Flag | Default |
|---------|------|---------|
| Fixtures dir | `--context-dir` | `./fixtures` |
| Output file | `--output` | (none) |
| Verbose | `--verbose` | `false` |
| Parallel | `--parallel` | `false` |
| Workers | `--workers` | CPU count |
| Transcript dir | `--transcript-dir` | (none) |
## Scoring Quick Reference
Each task produces an `EvaluationOutcome` with:
| Field | Description |
|-------|-------------|
| `score` | 0.0–1.0 normalized score |
| `pass` | Boolean pass/fail |
| `validator_results` | Per-validator details |
| `duration` | Execution time |More Testing skills
tdd
mattpocock/skills
Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
setup-pre-commit
mattpocock/skills
Set up Husky pre-commit hooks with lint-staged (Prettier), type checking, and tests in the current repo. Use when user wants to add pre-commit hooks, set up Husky, configure lint-staged, or add commit-time formatting/typechecking/testing.
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

