Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/yeaight7/agent-powerups/skill-evaluation-workbenchnpx skills add yeaight7/agent-powerups --skill skill-evaluation-workbenchgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yeaight7/agent-powerups/skill-evaluation-workbench)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/skill-evaluation-workbench"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/skill-evaluation-workbench.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.00840 |
| Opus 5 | $0.00017 | $0.00420 |
| Sonnet 5 | $0.00007 | $0.00168 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
skill-evaluation-workbench scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
-
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.
-
Create the minimal suite — one positive, one edge, one control case. Add more only after the minimal suite passes.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 87 lines · 34 tokens per session scan A 14989d856574
skill-evaluation-workbench is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 840 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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