# Skill Evaluation Workbench

> Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or MCP-backed cases.

- **Type:** Skill
- **Install:** `agentstack add skill-yeaight7-agent-powerups-skill-evaluation-workbench`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [yeaight7](https://agentstack.voostack.com/s/yeaight7)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [yeaight7](https://github.com/yeaight7)
- **Source:** https://github.com/yeaight7/agent-powerups/tree/main/plugins/agent-evaluation-lab/skills/skill-evaluation-workbench

## Install

```sh
agentstack add skill-yeaight7-agent-powerups-skill-evaluation-workbench
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Skill Evaluation Workbench

## When To Use

- A skill or prompt needs repeatable quality checks across models or configurations.
- A workflow needs file-based graders, command traces, or local artifact checks.
- A tool or MCP skill needs a hidden service fixture or sandboxed test workspace.
- A previous agent attempt failed and you need trace-driven diagnosis before editing instructions.

## Requirements / Checks

- Confirm an eval runner exists locally before running anything. Do not install deps without approval.
- Prefer local deterministic graders over model-graded assertions.
- If Docker, remote models, API keys, or live services are involved, ask before execution.
- Treat traces, result files, preserved workspaces, and stdout as potentially sensitive.

## Minimal Suite Structure

Every suite should have at least three cases:

| Case | Purpose |
|---|---|
| Positive (golden path) | Skill handles the normal use case correctly |
| Edge case | Skill handles an important boundary condition |
| Control (no-tool-needed) | Skill does not over-trigger on a clearly unrelated input |

Place fixtures in `cases/`, skill/reference material in `references/`, and grader scripts in `graders/`.

## Grader Types

| Type | When to use | Deterministic? |
|---|---|---|
| File existence | Skill was supposed to create a file | Yes |
| File content match | Output matches expected text or schema | Yes |
| Command exit code | Script/tool succeeded | Yes |
| JSON schema | Output is valid structured data | Yes |
| Regex match | Output contains expected pattern | Yes |
| Custom script | Complex logic not covered above | Yes (if written correctly) |
| Model grader | Subjective quality judgment | No — use sparingly, pin model |

## Workflow

1. **Define observable behavior** — state what the skill must produce: files, command args, JSON output, logs, or a safety refusal. If it's not observable, it can't be graded.

2. **Create the minimal suite** — one positive, one edge, one control case. Add more only after the minimal suite passes.

3. **Structure fixtures** — put case inputs in `cases//`, expected outputs in `cases//expected/`. For MCP-backed cases, use hidden service fixtures so server internals are not visible to the agent.

4. **Run smallest local suite first** — inspect the `result`, `summary`, trace, and workspace evidence from the first failure before editing anything.

5. **Classify the failure before editing**:

   | Failure type | Fix target |
   |---|---|
   | Skill instructions are unclear | Edit `SKILL.md` |
   | Missing reference material | Add to `references/` |
   | Grader logic is brittle | Fix the grader script |
   | Fixture is unrealistic | Replace the test input |
   | Task is ambiguous | Clarify the task definition |
   | Actual product bug | File as a bug, not a skill fix |

6. **Edit only the classified cause**, then re-run the same case.

## Safety Constraints

- Do not forward broad env vars into eval sandboxes — pass only named test variables.
- Do not print secrets in prompts, graders, traces, or artifacts.
- Do not mock a CLI or API unless the mock faithfully matches validation, output, and failure modes.
- Do not treat a green mock eval as proof that live integration works.
- Do not auto-enable host or user MCP configs inside eval containers.

## Validation / Done Criteria

- Suite has deterministic pass/fail evidence for all cases.
- Failure triage points to a concrete cause before any edits are made.
- Re-run proves the targeted change fixed the failing case without breaking passing cases.
- Output includes: command used, model(s), trial count, failures, and artifact paths.

## References

- `references/workbench-suite-model.md`

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [yeaight7](https://github.com/yeaight7)
- **Source:** [yeaight7/agent-powerups](https://github.com/yeaight7/agent-powerups)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-yeaight7-agent-powerups-skill-evaluation-workbench
- Seller: https://agentstack.voostack.com/s/yeaight7
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.