skill-test

Name: skill-test
Author: databricks-solutions

Data Community

Repo stars 1,553
Author repo ai-dev-kit

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparin…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Quick References”, “/skill-test Command”, “Basic Usage”, “Subcommands”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name skill-test directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Quick References / /skill-test Command / Basic Usage” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write / shell-exec / env-read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Databricks Skills Testing Framework

Offline YAML-first evaluation with human-in-the-loop review and interactive skill improvement.

Quick References

Scorers - Available scorers and quality gates
YAML Schemas - Manifest and ground truth formats
Python API - Programmatic usage examples
Workflows - Detailed example workflows
Trace Evaluation - Session trace analysis

/skill-test Command

The /skill-test command provides an interactive CLI for testing Databricks skills with real execution on Databricks.

Basic Usage

/skill-test <skill-name> [subcommand]

Subcommands

Subcommand	Description
`run`	Run evaluation against ground truth (default)
`regression`	Compare current results against baseline
`init`	Initialize test scaffolding for a new skill
`add`	Interactive: prompt -> invoke skill -> test -> save
`add --trace`	Add test case with trace evaluation
`review`	Review pending candidates interactively
`review --batch`	Batch approve all pending candidates
`baseline`	Save current results as regression baseline
`mlflow`	Run full MLflow evaluation with LLM judges
`trace-eval`	Evaluate traces against skill expectations
`list-traces`	List available traces (MLflow or local)
`scorers`	List configured scorers for a skill
`scorers update`	Add/remove scorers or update default guidelines
`sync`	Sync YAML to Unity Catalog (Phase 2)

Quick Examples

/skill-test databricks-spark-declarative-pipelines run
/skill-test databricks-spark-declarative-pipelines add --trace
/skill-test databricks-spark-declarative-pipelines review --batch --filter-success
/skill-test my-new-skill init

See Workflows for detailed examples of each subcommand.

Execution Instructions

Environment Setup

uv pip install -e .test/

Environment variables for Databricks MLflow:

DATABRICKS_CONFIG_PROFILE - Databricks CLI profile (default: "DEFAULT")
MLFLOW_TRACKING_URI - Set to "databricks" for Databricks MLflow
MLFLOW_EXPERIMENT_NAME - Experiment path (e.g., "/Users/{user}/skill-test")

Running Scripts

All subcommands have corresponding scripts in .test/scripts/:

uv run python .test/scripts/{subcommand}.py {skill_name} [options]

Subcommand	Script
`run`	`run_eval.py`
`regression`	`regression.py`
`init`	`init_skill.py`
`add`	`add.py`
`review`	`review.py`
`baseline`	`baseline.py`
`mlflow`	`mlflow_eval.py`
`scorers`	`scorers.py`
`scorers update`	`scorers_update.py`
`sync`	`sync.py`
`trace-eval`	`trace_eval.py`
`list-traces`	`list_traces.py`
`_routing mlflow`	`routing_eval.py`

Use --help on any script for available options.

Command Handler

When /skill-test is invoked, parse arguments and execute the appropriate command.

Argument Parsing

args[0] = skill_name (required)
args[1] = subcommand (optional, default: "run")

Subcommand Routing

Subcommand	Action
`run`	Execute `run(skill_name, ctx)` and display results
`regression`	Execute `regression(skill_name, ctx)` and display comparison
`init`	Execute `init(skill_name, ctx)` to create scaffolding
`add`	Prompt for test input, invoke skill, run `interactive()`
`review`	Execute `review(skill_name, ctx)` to review pending candidates
`baseline`	Execute `baseline(skill_name, ctx)` to save as regression baseline
`mlflow`	Execute `mlflow_eval(skill_name, ctx)` with MLflow logging
`scorers`	Execute `scorers(skill_name, ctx)` to list configured scorers
`scorers update`	Execute `scorers_update(skill_name, ctx, ...)` to modify scorers

init Behavior

When running /skill-test <skill-name> init:

Read the skill's SKILL.md to understand its purpose
Create manifest.yaml with appropriate scorers and trace_expectations
Create empty ground_truth.yaml and candidates.yaml templates
Recommend test prompts based on documentation examples

Follow with /skill-test <skill-name> add using recommended prompts.

Context Setup

Create CLIContext with MCP tools before calling any command. See Python API for details.

File Locations

Important: All test files are stored at the repository root level, not relative to this skill's directory.

File Type	Path
Ground truth	`{repo_root}/.test/skills/{skill-name}/ground_truth.yaml`
Candidates	`{repo_root}/.test/skills/{skill-name}/candidates.yaml`
Manifest	`{repo_root}/.test/skills/{skill-name}/manifest.yaml`
Routing tests	`{repo_root}/.test/skills/_routing/ground_truth.yaml`
Baselines	`{repo_root}/.test/baselines/{skill-name}/baseline.yaml`

For example, to test databricks-spark-declarative-pipelines in this repository:

/Users/.../ai-dev-kit/.test/skills/databricks-spark-declarative-pipelines/ground_truth.yaml

Not relative to the skill definition:

/Users/.../ai-dev-kit/.claude/skills/skill-test/skills/...  # WRONG

Directory Structure

.test/                          # At REPOSITORY ROOT (not skill directory)
├── pyproject.toml              # Package config (pip install -e ".test/")
├── README.md                   # Contributor documentation
├── SKILL.md                    # Source of truth (synced to .claude/skills/)
├── install_skill_test.sh       # Sync script
├── scripts/                    # Wrapper scripts
│   ├── _common.py              # Shared utilities
│   ├── run_eval.py
│   ├── regression.py
│   ├── init_skill.py
│   ├── add.py
│   ├── baseline.py
│   ├── mlflow_eval.py
│   ├── routing_eval.py
│   ├── trace_eval.py           # Trace evaluation
│   ├── list_traces.py          # List available traces
│   ├── scorers.py
│   ├── scorers_update.py
│   └── sync.py
├── src/
│   └── skill_test/             # Python package
│       ├── cli/                # CLI commands module
│       ├── fixtures/           # Test fixture setup
│       ├── scorers/            # Evaluation scorers
│       ├── grp/                # Generate-Review-Promote pipeline
│       └── runners/            # Evaluation runners
├── skills/                     # Per-skill test definitions
│   ├── _routing/               # Routing test cases
│   └── {skill-name}/           # Skill-specific tests
│       ├── ground_truth.yaml
│       ├── candidates.yaml
│       └── manifest.yaml
├── tests/                      # Unit tests
├── references/                 # Documentation references
└── baselines/                  # Regression baselines

References

Scorers - Available scorers and quality gates
YAML Schemas - Manifest and ground truth formats
Python API - Programmatic usage examples
Workflows - Detailed example workflows
Trace Evaluation - Session trace analysis

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Data
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @databricks-solutions · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Python
Detected file/system behavior: Read-only
Write / modify
Shell exec
Env read
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview skill-test-databricks-solutions.preview

# Quick Examples

/skill-test databricks-spark-declarative-pipelines run
/skill-test databricks-spark-declarative-pipelines add --trace
/skill-test databricks-spark-declarative-pipelines review --batch --filter-success
/skill-test my-new-skill init

SKILL.md SKILL.md

# Databricks Skills Testing Framework

Offline YAML-first evaluation with human-in-the-loop review and interactive skill improvement.

## Quick References

- [Scorers](references/scorers.md) - Available scorers and quality gates
- [YAML Schemas](references/yaml-schemas.md) - Manifest and ground truth formats
- [Python API](references/python-api.md) - Programmatic usage examples
- [Workflows](references/workflows.md) - Detailed example workflows
- [Trace Evaluation](references/trace-eval.md) - Session trace analysis

## /skill-test Command

The `/skill-test` command provides an interactive CLI for testing Databricks skills with real execution on Databricks.

### Basic Usage

```
/skill-test <skill-name> [subcommand]
```

### Subcommands

| Subcommand | Description |
|------------|-------------|
| `run` | Run evaluation against ground truth (default) |
| `regression` | Compare current results against baseline |
| `init` | Initialize test scaffolding for a new skill |
| `add` | Interactive: prompt -> invoke skill -> test -> save |
| `add --trace` | Add test case with trace evaluation |
| `review` | Review pending candidates interactively |
| `review --batch` | Batch approve all pending candidates |
| `baseline` | Save current results as regression baseline |
| `mlflow` | Run full MLflow evaluation with LLM judges |
| `trace-eval` | Evaluate traces against skill expectations |
| `list-traces` | List available traces (MLflow or local) |
| `scorers` | List configured scorers for a skill |
| `scorers update` | Add/remove scorers or update default guidelines |
| `sync` | Sync YAML to Unity Catalog (Phase 2) |

### Quick Examples

```
/skill-test databricks-spark-declarative-pipelines run
/skill-test databricks-spark-declarative-pipelines add --trace

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Quick References → /skill-test Command → Basic Usage → Subcommands → Quick Examples → Execution Instructions
terms    -> Important · repository root · Not · Offline YAML-first evaluation with human-in-the-loop review and interactive skill improvement. · The /skill-test command provides an interactive CLI for testing Databricks skills with real execution on Databricks. · See [Workflows](references/workflows.md) for detailed examples of each subcommand. · Use --help on any script for available options. · When /skill-test is invoked, parse arguments and execute the appropriate command.
files/cmd -> /skill-test · run · regression · init · add · add --trace · review · review --batch
body sha256 -> 7ff12a59851e