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 skills add majiayu000/vibeguard --skill eval-harnessgit clone --depth 1 https://github.com/majiayu000/vibeguardWrote 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/majiayu000/vibeguard/eval-harness)<a href="https://agentmods.dev/skills/majiayu000/vibeguard/eval-harness"><img src="https://agentmods.dev/badge/skills/majiayu000/vibeguard/eval-harness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/majiayu000/vibeguard/eval-harness"><img src="https://agentmods.dev/badge/skills/majiayu000/vibeguard/eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00029 | $0.00909 |
| Opus 5 | $0.00015 | $0.00454 |
| Sonnet 5 | $0.00006 | $0.00182 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
eval-harness 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 10d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Harness
Overview
Evaluation-driven development: Not just "can the code run", but quantify "how good is the code".
When to Activate
- Designing or changing an evaluation harness for agent behavior or guard quality.
- Comparing prompt, skill, hook, or workflow variants with measurable outcomes.
- Adding a regression gate that must prove repair without hiding unrelated-task regressions.
- Converting subjective review criteria into deterministic or model-graded metrics.
- A guard, hook, workflow, or skill needs measurable repair/regression evidence before adoption.
- A user asks whether an agent workflow is actually improving quality rather than only passing one example.
- A change affects scoring, grading, or benchmark thresholds.
Red Flags
- Only one hand-picked success case exists and there is no held-out or unrelated task coverage.
- A probabilistic grader is used where a deterministic build, test, lint, or coverage check is available.
- pass@k improves while pass^k or unrelated-task behavior regresses.
Checklist
- Define deterministic checks before adding model-judged grading.
- Record target and unrelated scenarios with before/after outcomes.
- Report pass@k, pass^k, and regression counts separately.
Core indicators
pass@k (single success rate)
- Generate k candidate solutions, with a probability of at least 1 passing
- Used to evaluate the completion quality of a single task
- Target: pass@1 > 80%
pass^k (continuous success rate)
- The probability of passing all k consecutive tasks at once
- Used to evaluate overall workflow reliability
- Goal: pass^5 > 50% (pass all 5 consecutive tasks in one go)
Grader type
Code Basics Grader (deterministic)
| Grader | Check content | Pass conditions |
|---|---|---|
| Compilation check | Whether the code can be compiled / type check passed | Zero errors |
| Test check | Whether all tests passed | Full green |
| Lint check | Whether the code style conforms to the specification | Zero warnings (or only allowed warnings) |
| Coverage check | Check whether the test coverage reaches the standard | ≥ 80% |
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.
- 10d ago First seen · 105 lines · 29 tokens per session scan A 9b18e95c1566
eval-harness is a skill published in the GitHub repository majiayu000/vibeguard (41 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 909 once invoked, about $0.0001 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 2026-08-30.
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