Waza

A Go CLI for evaluating AI agent skills — scaffold eval suites, run benchmarks, and compare results across models.

📖 Getting Started / Docs

Installation

Binary Install (recommended)

Download and install the latest pre-built binary with the install script:

curl -fsSL https://raw.githubusercontent.com/microsoft/waza/main/install.sh | bash

The script auto-detects your OS and architecture (linux/darwin/windows, amd64/arm64), downloads the binary, verifies the checksum, and installs to /usr/local/bin (or ~/bin if not writable).

Or download binaries directly from the latest release.

Install from Source

Requires Go 1.26+:

go install github.com/microsoft/waza/cmd/waza@latest

Azure Developer CLI (azd) Extension

Waza is also available as an azd extension:

# Add the waza extension registry
azd ext source add -n waza -t url -l https://raw.githubusercontent.com/microsoft/waza/main/registry.json

# Install the extension
azd ext install microsoft.azd.waza

# Verify it's working
azd waza --help

Once installed, all waza commands are available under azd waza. For example:

azd waza init my-eval --interactive
azd waza run examples/code-explainer/eval.yaml -v

Quick Start

For New Users: Get Started in 5 Minutes

See Getting Started Guide for a complete walkthrough:

# Initialize a new project
waza init my-project && cd my-project

# Create a new skill
waza new my-skill

# Define the skill in skills/my-skill/SKILL.md
# Write evaluation tasks in evals/my-skill/tasks/
# Add test fixtures in evals/my-skill/fixtures/

# Run evaluations
waza run my-skill

# Check skill readiness
waza check my-skill

All Commands

# Build
make build

# Initialize a project workspace
waza init [directory]

# Create a new skill
waza new skill-name

# Check if a skill is ready for submission
waza check skills/my-skill

# Suggest an eval suite from SKILL.md
waza suggest skills/my-skill --dry-run
waza suggest skills/my-skill --apply

# Note: 'generate' is available as an alias for 'new' (see below for new command)

# Run evaluations
waza run examples/code-explainer/eval.yaml --context-dir examples/code-explainer/fixtures -v

# Compare results across models
waza compare results-gpt4.json results-sonnet.json

# Count tokens in skill files
waza tokens count skills/

# Suggest token optimizations
waza tokens suggest skills/

Commands

waza init [directory]

Initialize a waza project workspace with separated skills/ and evals/ directories. Idempotent — creates only missing files.

Flag	Description
--interactive	Project-level setup wizard (reserved for future use)
--no-skill	Skip the first-skill creation prompt

Creates:

• skills/ — Skill definitions directory
• evals/ — Evaluation suites directory
• .github/workflows/eval.yml — CI/CD pipeline for running evals on PR
• .gitignore — Waza-specific exclusions
• README.md — Getting started guide for your project

Example:

waza init my-project
# Optionally creates first skill interactively

waza init my-project --no-skill
# Skip skill creation prompt

waza new <skill-name>

Create a new skill with scaffolded structure and evaluation suite. Detects workspace context and adapts output.

Flag	Short	Description
--interactive	-i	Run guided skill metadata wizard
--template	-t	Template pack (coming soon)

Modes:

Project mode (detects skills/ directory):

project/
├── skills/{skill-name}/SKILL.md
└── evals/{skill-name}/
    ├── eval.yaml
    ├── tasks/*.yaml
    └── fixtures/

Standalone mode (no skills/ detected):

{skill-name}/
├── SKILL.md
├── evals/
│   ├── eval.yaml
│   ├── tasks/*.yaml
│   └── fixtures/
├── .github/workflows/eval.yml
├── .gitignore
└── README.md

Example:

# In project: creates skills/code-explainer/SKILL.md + evals/code-explainer/
waza new code-explainer

# Standalone: creates code-explainer/ self-contained directory
waza new code-explainer

# With wizard
waza new code-explainer --interactive

waza run <eval.yaml>

Run an evaluation benchmark from a spec file.

Flag	Short	Description
--context-dir <dir>		Fixture directory (default: ./fixtures relative to spec)
--output <file>	-o	Save results to JSON
--verbose	-v	Detailed progress output
--transcript-dir <dir>		Save per-task transcript JSON files
--task <glob>		Filter tasks by name/ID pattern (repeatable)
--parallel		Run tasks concurrently
--workers <n>		Concurrent workers (default: 4, requires --parallel)
--interpret		Print plain-language result interpretation
--format <fmt>		Output format: default or github-comment (default: default)
--cache		Enable result caching to speed up repeated runs
--no-cache		Explicitly disable result caching
--cache-dir <dir>		Cache directory (default: .waza-cache)
--reporter <spec>		Output reporters: json (default), junit:<path> (repeatable)
--baseline		A/B testing mode — runs each task twice (without skill = baseline, with skill = normal) and computes improvement scores
--discover		Auto skill discovery — walks directory tree for SKILL.md + eval.yaml (root/tests/evals)
--strict		Fail if any SKILL.md lacks eval coverage (use with --discover)
--suggest		Generate a Copilot suggestion report based on test outcomes (mock engine emits a deterministic fake report)

Result Caching

Enable caching with --cache to store test results and skip re-execution on repeated runs:

# First run executes all tests and caches results
waza run eval.yaml --cache

# Second run uses cached results (much faster)
waza run eval.yaml --cache

# Clear the cache when needed
waza cache clear

Cached results are automatically invalidated when:

• Spec configuration changes (model, timeout, graders, etc.)
• Task definitions change
• Fixture files change

Note: Caching is automatically disabled for evaluations using non-deterministic graders (behavior, prompt).

Exit Codes

The run command uses exit codes to enable CI/CD integration:

Exit Code	Condition	Description
0	Success	All tests passed
1	Test failure	One or more tests failed validation
2	Configuration error	Invalid spec, missing files, or runtime error

Example CI usage:

# Fail the build if any tests fail
waza run eval.yaml || exit $?

# Capture specific exit codes
waza run eval.yaml
EXIT_CODE=$?
if [ $EXIT_CODE -eq 1 ]; then
  echo "Tests failed - check results"
elif [ $EXIT_CODE -eq 2 ]; then
  echo "Configuration error"
fi

# Post results as PR comment (GitHub Actions)
waza run eval.yaml --format github-comment > comment.md
gh pr comment $PR_NUMBER --body-file comment.md

# Generate JUnit XML for CI test reporting
waza run eval.yaml --reporter junit:results.xml

# Both JSON output and JUnit XML
waza run eval.yaml -o results.json --reporter junit:results.xml

Note: waza generate is an alias for waza new. Both commands support the same functionality with the --output-dir flag for specifying custom output locations.

waza compare <file1> <file2> [files...]

Compare results from multiple evaluation runs side by side — per-task score deltas, pass rate differences, and aggregate statistics.

Flag	Short	Description
--format <fmt>	-f	Output format: table or json (default: table)

waza cache clear

Clear all cached evaluation results to force re-execution on the next run.

Flag	Description
--cache-dir <dir>	Cache directory to clear (default: .waza-cache)

waza dev [skill-path]

Iteratively score and improve skill frontmatter in a SKILL.md file.

Use --copilot for a non-interactive, single-pass markdown report that:

1. Summarizes current skill details and token usage
2. Loads trigger test prompts as examples (when trigger_tests.yaml exists)
3. Requests Copilot suggestions for improving skill selection
4. Prints the report to stdout without applying any changes

When --copilot is set, iterative mode flags (--target, --max-iterations, --auto) are invalid.

Flag	Description
--target <level>	Target adherence level for iterative mode: low, medium, medium-high, high (default: medium-high)
--max-iterations <n>	Maximum improvement iterations for iterative mode (default: 5)
--auto	Apply improvements without prompting in iterative mode
--copilot	Generate a non-interactive markdown report with Copilot suggestions
--model <id>	Model to use with --copilot

waza check [skill-path]

Check if a skill is ready for submission with a comprehensive readiness report.

Performs five types of checks:

1. Compliance scoring — Validates frontmatter adherence (Low/Medium/Medium-High/High)
2. Token budget — Checks if SKILL.md is within token limits (configurable in .waza.yaml tokens.limits)
3. Evaluation suite — Checks for the presence of eval.yaml
4. Spec compliance — Validates the skill against the agentskills.io spec (frontmatter structure, required fields, naming rules, directory match, description length, compatibility, license, and version)
5. Advisory checks — Detects quality and maintainability issues (reference module count, complexity classification, negative delta risk patterns, procedural content, and over-specificity)

Provides a plain-language summary and actionable next steps to improve the skill.

Example output:

🔍 Skill Readiness Check
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Skill: code-explainer

📋 Compliance Score: High
   ✅ Excellent! Your skill meets all compliance requirements.

📊 Token Budget: 450 / 500 tokens
   ✅ Within budget (50 tokens remaining).

🧪 Evaluation Suite: Found
   ✅ eval.yaml detected. Run 'waza run eval.yaml' to test.

📐 Spec Compliance (agentskills.io)
   ✅ spec-frontmatter    Frontmatter structure valid with required fields
   ✅ spec-allowed-fields All frontmatter fields are spec-allowed
   ✅ spec-name           Name follows spec naming rules
   ✅ spec-dir-match      Directory name matches skill name
   ✅ spec-description    Description is valid
   ✅ spec-license        License field present
   ✅ spec-version        metadata.version present

🔬 Advisory Checks
   ✅ module-count        Found 2 reference modules (2-3 is optimal)
   ✅ complexity          Complexity: detailed (350 tokens, 2 modules)
   ✅ negative-delta-risk No negative delta risk patterns detected
   ✅ procedural-content  Description contains procedural language
   ✅ over-specificity    No over-specificity patterns detected

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📈 Overall Readiness
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✅ Your skill is ready for submission!

🎯 Next Steps
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✨ No action needed! Your skill looks great.

Consider:
  • Running 'waza run eval.yaml' to verify functionality
  • Sharing your skill with the community

Usage:

# Check current directory
waza check

# Check specific skill
waza check skills/my-skill

# Suggested workflow
waza check skills/my-skill     # Check readiness
waza dev skills/my-skill       # Improve compliance if needed
waza check skills/my-skill     # Verify improvements

waza suggest <skill-path>

Use an LLM to analyze SKILL.md and generate suggested evaluation artifacts.

Flag	Description
--model <model>	Model to use for suggestions (default: project default model)
--dry-run	Print suggested output to stdout (default)
--apply	Write files to disk
--output-dir <dir>	Output directory (default: <skill-path>/evals)
--format yaml|json	Output format (default: yaml)

Examples:

# Preview generated eval/task/fixture files as YAML
waza suggest skills/code-explainer --dry-run

# Write generated files to disk
waza suggest skills/code-explainer --apply

# Print JSON-formatted suggestion payload
waza suggest skills/code-explainer --format json

waza tokens count [paths...]

Count tokens in markdown files. Paths may be files or directories (scanned recursively for .md/.mdx).

Flag	Description
--format <fmt>	Output format: table or json (default: table)
--sort <field>	Sort by: tokens, name, or path (default: path)
--min-tokens <n>	Filter files below n tokens
--no-total	Hide total row in table output

waza tokens profile [skill-name | path]

Structural analysis of SKILL.md files — reports token count, section count, code block count, and workflow step detection with a one-line summary and warnings.

Flag	Description
--format <fmt>	Output format: text or json (default: text)
--tokenizer <t>	Tokenizer: bpe or estimate (default: bpe)

Example output:

📊 my-skill: 1,722 tokens (detailed ✓), 8 sections, 4 code blocks
   ⚠️  no workflow steps detected

waza tokens suggest [paths...]

Suggest ways to reduce token usage in markdown files. Paths may be files or directories (scanned recursively for .md/.mdx).

Flag	Description
--format <fmt>	Output format: text or json (default: text)
--min-savings <n>	Minimum estimated token savings for heuristic suggestions
--copilot	Enable Copilot-powered suggestions
--model <id>	Model to use with --copilot

waza serve

Start the waza dashboard server to visualize evaluation results. The HTTP server opens in your browser automatically and scans the specified directory for .json result files.

Optionally, run a JSON-RPC 2.0 server (for IDE integration) instead of the HTTP dashboard using the --tcp flag.

Flag	Default	Description
--port <port>	3000	HTTP server port
--no-browser	false	Don't auto-open the browser
--results-dir <dir>	.	Directory to scan for result files
--tcp <addr>	(off)	TCP address for JSON-RPC (e.g., :9000); defaults to loopback for security
--tcp-allow-remote	false	Allow TCP binding to non-loopback addresses (⚠️ no authentication)

Examples:

Start the HTTP dashboard on port 3000:

waza serve

Start the HTTP dashboard on a custom port and scan a results directory:

waza serve --port 8080 --results-dir ./results

Start the dashboard without auto-opening the browser:

waza serve --no-browser

Start a JSON-RPC server for IDE integration:

waza serve --tcp :9000

Dashboard Views:

The dashboard displays evaluation results with:

• Task-level pass/fail status
• Score distributions across trials
• Model comparisons
• Aggregated metrics and trends

For detailed documentation on the dashboard and result visualization, see docs/GUIDE.md.

waza results

Manage evaluation results stored in cloud or local storage.

waza results list

List all evaluation runs from configured cloud storage or local results directory.

Flag	Description
--limit <n>	Maximum results to display (default: 20)
--format <fmt>	Output format: table or json (default: table)

# List recent results
waza results list

# List with custom limit
waza results list --limit 20

# Output as JSON
waza results list --format json

waza results compare <id1> <id2>

Compare two evaluation runs side by side. Displays per-task score deltas, pass rate differences, and key metrics.

Flag	Description
--format <fmt>	Output format: table or json (default: table)

# Compare two runs
waza results compare run-20250226-001 run-20250226-002

# Output as JSON for further processing
waza results compare run-20250226-001 run-20250226-002 --format json

Cloud Storage

Waza can automatically upload evaluation results to Azure Blob Storage for team collaboration and historical tracking.

Configuration

Add a storage: section to your .waza.yaml:

storage:
  provider: azure-blob
  accountName: "myteamwaza"
  containerName: "waza-results"
  enabled: true

Field	Description	Required
provider	Cloud provider (azure-blob currently supported)	Yes
accountName	Azure Storage account name	Yes
containerName	Blob container name (default: waza-results)	No
enabled	Enable/disable uploads (default: true when configured)	No

Authentication

Waza uses DefaultAzureCredential — it automatically detects and uses available credentials in this order:

1. Environment variables (AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, AZURE_TENANT_ID)
2. Managed Identity (on Azure services)
3. Azure CLI (az login)
4. Visual Studio Code (if signed in)
5. Azure PowerShell (if signed in)

In most cases, running az login is all you need:

az login
waza run eval.yaml  # Results auto-upload to Azure Storage

How It Works

1. Auto-upload on run: When storage: is configured, waza run automatically uploads results to Azure Blob Storage
2. Organized by skill: Results are stored as {skill-name}/{run-id}.json
3. Local copy kept: Results are also saved locally (via -o flag)
4. List remote results: Use waza results list to browse uploaded runs
5. Compare runs: Use waza results compare to diff two remote results

Example Workflow

# Configure once (edit .waza.yaml)
cat > .waza.yaml <<EOF
storage:
  provider: azure-blob
  accountName: "myteamwaza"
  containerName: "waza-results"
  enabled: true
EOF

# Authenticate
az login

# Run evaluations — results auto-upload
waza run evals/my-skill/eval.yaml -v

# Browse uploaded results
waza results list

# Compare two runs
waza results compare run-id-1 run-id-2

For step-by-step setup and troubleshooting, see Getting Started with Azure Storage guide.

Building

make build          # Compile binary to ./waza
make test           # Run tests with coverage
make lint           # Run golangci-lint
make fmt            # Format code and tidy modules
make install        # Install to GOPATH

Project Structure

cmd/waza/              CLI entrypoint and command definitions
  tokens/              Token counting subcommand
internal/
  config/              Configuration with functional options
  execution/           AgentEngine interface (mock, copilot)
  graders/             Validator registry and built-in graders
  metrics/             Scoring metrics
  models/              Data structures (BenchmarkSpec, TestCase, EvaluationOutcome)
  orchestration/       TestRunner for coordinating execution
  reporting/           Result formatting and output
  transcript/          Per-task transcript capture
  wizard/              Interactive init wizard
examples/              Example eval suites
skills/                Example skills

Eval Spec Format

name: my-eval
skill: my-skill
version: "1.0"

config:
  trials_per_task: 3
  max_attempts: 3          # Retry failed graders up to 3 times (default: 1, no retries)
  timeout_seconds: 300
  parallel: false
  executor: mock          # or copilot-sdk
  model: claude-sonnet-4-20250514
  group_by: model          # Group results by model (or other dimension)

# Custom input variables available as {{.Vars.key}} in tasks and hooks
inputs:
  api_version: v2
  environment: production
  max_retries: 3

hooks:
  before_run:
    - command: "echo 'Starting evaluation'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

  after_run:
    - command: "echo 'Evaluation complete'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

  before_task:
    - command: "echo 'Running task: {{.TaskName}}'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

  after_task:
    - command: "echo 'Task {{.TaskName}} completed'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

graders:
  - type: text
    name: pattern_check
    config:
      regex_match: ["\\d+ tests passed"]
  
  - type: behavior
    name: efficiency
    config:
      max_tool_calls: 20
      max_duration_ms: 300000

  - type: action_sequence
    name: workflow_check
    config:
      matching_mode: in_order_match
      expected_actions: ["bash", "edit", "report_progress"]

# Task definitions: glob patterns or CSV dataset
tasks:
  - "tasks/*.yaml"

# Optional: Generate tasks from CSV dataset
# tasks_from: ./test-cases.csv
# range: [1, 10]  # Only include rows 1-10 (0-indexed, skips header)

Custom Input Variables

Use the inputs section to define key-value variables available throughout your evaluation as {{.Vars.key}}:

inputs:
  api_endpoint: https://api.example.com
  timeout: 30
  environment: staging

hooks:
  before_run:
    - command: "echo 'Testing against {{.Vars.environment}}'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

Variables are accessible in:

• Hook commands
• Task prompts and fixtures (via template rendering)
• Grader configurations

CSV Dataset Support

Generate tasks dynamically from a CSV file using tasks_from:

# eval.yaml
tasks_from: ./test-cases.csv
range: [0, 50]  # Optional: limit to rows 0-50 (skip header at 0)

CSV Format:

prompt,expected_output,language
"Explain this function","Function explanation",python
"Review this code","Code review",javascript

Task Generation:

• First row is treated as column headers
• Each subsequent row becomes a task
• Column values are available as {{.Vars.column_name}}
• Range filtering (optional) allows limiting to a subset of rows

Example task prompt using CSV variables:

In your task file or inline prompt:

prompt: "{{.Vars.prompt}}"
expected_output: "{{.Vars.expected_output}}"
language: "{{.Vars.language}}"

Tasks can also be mixed — use both explicit task files and CSV-generated tasks:

tasks:
  - "tasks/*.yaml"        # Explicit tasks

tasks_from: ./test-cases.csv    # CSV-generated tasks
range: [0, 20]                  # Only first 20 rows

CSV vs Inputs:

• inputs: Static key-value pairs defined once in eval.yaml
• tasks_from: Generates multiple tasks from CSV rows
• Conflict resolution: CSV column values override inputs for the same key

Retry/Attempts

Use max_attempts to retry failed grader validations within each trial:

config:
  max_attempts: 3  # Retry failed graders up to 3 times (default: 1, no retries)

When a grader fails, waza will retry the task execution up to max_attempts times. The evaluation outcome includes an attempts field showing how many executions were needed to pass. This is useful for handling transient failures in external services or non-deterministic grader behavior.

Output: JSON results include attempts per task showing the number of executions performed.

Grouping Results

Use group_by to organize results by a dimension (e.g., model, environment). Results are grouped in CLI output and JSON results include group statistics:

config:
  group_by: model

Grouped results in JSON output include GroupStats:

{
  "group_stats": [
    {
      "name": "claude-sonnet-4-20250514",
      "passed": 8,
      "total": 10,
      "avg_score": 0.85
    }
  ]
}

Lifecycle Hooks

Use hooks to run commands before/after evaluations and tasks:

hooks:
  before_run:
    - command: "npm install"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: true

  after_run:
    - command: "rm -rf node_modules"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

  before_task:
    - command: "echo 'Task: {{.TaskName}}'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

  after_task:
    - command: "echo 'Done: {{.TaskName}}'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

Hook Fields:

• command — Shell command to execute
• working_directory — Directory to run command in (relative to eval.yaml)
• exit_codes — List of acceptable exit codes (default: [0])
• error_on_fail — Fail entire evaluation if hook fails (default: false)

Lifecycle Points:

• before_run — Execute once before all tasks
• after_run — Execute once after all tasks
• before_task — Execute before each task
• after_task — Execute after each task

Template Variables in Hooks and Commands:

Available variables in hook commands and task execution contexts:

• {{.JobID}} — Unique evaluation run identifier
• {{.TaskName}} — Name/ID of the current task (available in before_task/after_task only)
• {{.Iteration}} — Current trial number (1-indexed)
• {{.Attempt}} — Current attempt number (1-indexed, used for retries)
• {{.Timestamp}} — ISO 8601 timestamp of execution
• {{.Vars.key}} — User-defined variables from the inputs section or CSV columns

Custom variables can be defined in the inputs section and referenced in hooks:

inputs:
  environment: production
  api_version: v2
  debug_mode: "true"

hooks:
  before_run:
    - command: "echo 'Starting eval {{.JobID}} in {{.Vars.environment}}'"
      working_directory: "."
      exit_codes: [0]
      error_on_fail: false

When using CSV-generated tasks, each row's column values are also available as {{.Vars.column_name}}.

CI/CD Integration

Waza is designed to work seamlessly with CI/CD pipelines.

Integrating Waza in CI

Waza can validate your skill in CI before publishing:

Installation in CI

Option 1: Binary install (recommended)

curl -fsSL https://raw.githubusercontent.com/microsoft/waza/main/install.sh | bash

Option 2: Install from source

# Requires Go 1.26+
go install github.com/microsoft/waza/cmd/waza@latest

Option 3: Use Docker

docker build -t waza:local .
docker run -v $(pwd):/workspace waza:local run eval/eval.yaml

Quick Workflow Setup

Copy .github/workflows/skills-ci-example.yml to your skill repository:

jobs:
  evaluate-skill:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install waza
        run: curl -fsSL https://raw.githubusercontent.com/microsoft/waza/main/install.sh | bash
      - run: waza run eval/eval.yaml --verbose --output results.json
      - uses: actions/upload-artifact@v4
        with:
          name: waza-evaluation-results
          path: results.json

Environment Requirements

Requirement	Details
Go Version	1.26 or higher
Executor	Use mock executor for CI (no API keys needed)
GitHub Token	Only required for copilot-sdk executor: set GITHUB_TOKEN env var
Exit Codes	0=success, 1=test failure, 2=config error

Expected Skill Structure

your-skill/
├── SKILL.md              # Skill definition
└── eval/                 # Evaluation suite
    ├── eval.yaml         # Benchmark spec
    ├── tasks/            # Task definitions
    │   └── *.yaml
    └── fixtures/         # Context files
        └── *.txt

For Waza Repository

This repository includes reusable workflows:

1. .github/workflows/waza-eval.yml - Reusable workflow for running evals

   jobs:
     eval:
       uses: ./.github/workflows/waza-eval.yml
       with:
         eval-yaml: 'examples/code-explainer/eval.yaml'
         verbose: true

2. examples/ci/eval-on-pr.yml - Matrix testing across models

3. examples/ci/basic-example.yml - Minimal workflow example

See examples/ci/README.md for detailed documentation and more examples.

Available Grader Types

Waza supports multiple grader types for comprehensive evaluation:

Grader	Purpose	Documentation
code	Python/JavaScript assertion-based validation	docs/GRADERS.md
text	Substring and pattern matching in output	docs/GRADERS.md
file	File existence and content validation	docs/GRADERS.md
diff	Workspace file comparison with snapshots and fragments	docs/GRADERS.md
behavior	Agent behavior constraints (tool calls, tokens, duration)	docs/GRADERS.md
action_sequence	Tool call sequence validation with F1 scoring	docs/GRADERS.md
skill_invocation	Skill orchestration sequence validation	docs/GRADERS.md
prompt	LLM-as-judge evaluation with rubrics	docs/GRADERS.md
trigger_tests	Prompt trigger accuracy detection	docs/GRADERS.md

See the complete Grader Reference for detailed configuration options and examples.

Documentation

• Getting Started - Complete walkthrough: init → new → run → check
• Demo Guide - 7 live demo scenarios for presentations
• Grader Reference - Complete grader types and configuration
• Tutorial - Getting started with writing skill evals
• CI Integration - GitHub Actions workflows for skill evaluation
• Token Management - Tracking and optimizing skill context size

Contributing

See AGENTS.md for coding guidelines.

• Use conventional commits (feat:, fix:, docs:, etc.)
• Go CI is required: Build and Test Go Implementation and Lint Go Code must pass
• Add tests for new features
• Update docs when changing CLI surface

Legacy Python Implementation

The Python implementation has been superseded by the Go CLI. The last Python release is available at v0.3.2. Starting with v0.4.0-alpha.1, waza is distributed exclusively as pre-built Go binaries.

License

See LICENSE.