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.
git clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote 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/commands/owl-listener/ai-design-skills/design-benchmark)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/design-benchmark"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/design-benchmark/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/commands/owl-listener/ai-design-skills/design-benchmark"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/design-benchmark.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00012 | $0.00491 |
| Opus 5 | $0.00006 | $0.00246 |
| Sonnet 5 | $0.00002 | $0.00098 |
| Haiku 4.5 | $0.00001 | $0.00049 |
Grade A, and why
design-benchmark 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 13d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are designing a benchmark suite for ongoing AI quality measurement. Use only skills from the evaluation plugin. Follow this process:
Step 1: Define Benchmark Goals
- What does this benchmark need to measure?
- What decisions will benchmark results inform? (model updates, prompt changes, feature launches)
- What's the measurement cadence? (daily, weekly, per-release)
Step 2: Design the Golden Test Set
Using output-quality-rubrics and longitudinal-measurement:
- Define input categories that cover key use cases
- Create 20-50 representative test inputs spanning:
- Common use cases (high frequency)
- Edge cases (high risk)
- Known failure modes (regression detection)
- Diverse user profiles and contexts
- For each input, define the expected quality standards
Step 3: Define Metrics
Using task-success-metrics, user-satisfaction-signals, and output-quality-rubrics:
- Select metrics to track: quality scores, task success rates, failure rates, latency
- Define how each metric is calculated
- Set baselines from current performance
- Define acceptable ranges and alert thresholds
Step 4: Design Failure Monitoring
Using failure-taxonomy:
- Define which failure types to track
- Set severity-specific thresholds (e.g., zero tolerance for critical safety failures)
- Design failure trend tracking
Step 5: Design Comparative Testing
Using comparative-evaluation:
- Define how new versions will be compared to the current baseline
- Specify A/B test protocols for significant changes
- Define statistical significance requirements
Step 6: Plan for Drift Detection
Using longitudinal-measurement:
- Define what constitutes meaningful quality drift vs. normal variance
- Design automated alerts for drift detection
- Specify the investigation and response protocol when drift is detected
Output
Deliver a complete benchmark suite specification:
- Benchmark goals and decision framework
- Golden test set (20-50 inputs with expected standards)
- Metrics definitions with baselines and thresholds
- Failure monitoring specifications
- Comparative testing protocol
- Drift detection and response plan
- Benchmark reporting template
- Recommended automation approach
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.
- 13d ago First seen · 51 lines · 12 tokens per session scan A b4ccad99cdef
design-benchmark is a command published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 491 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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