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 Owl-Listener/ai-design-skills --skill comparative-evaluationgit 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/skills/owl-listener/ai-design-skills/comparative-evaluation)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/comparative-evaluation"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/comparative-evaluation/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/owl-listener/ai-design-skills/comparative-evaluation"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/comparative-evaluation.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 | $0.00021 | $0.00504 |
| Opus 5 | $0.00010 | $0.00252 |
| Sonnet 5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
comparative-evaluation 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 11d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comparative Evaluation
Absolute quality scores are useful but limited. Comparative evaluation — putting outputs side by side and asking which is better — often reveals quality differences that rubrics miss.
Comparison Methods
- A/B testing: Show different users different versions and compare outcomes
- Side-by-side evaluation: Show evaluators two outputs for the same input and ask which is better
- Preference ranking: Show evaluators multiple outputs and rank them from best to worst
- Paired comparison: Compare every pair of options to build a complete ranking
- Elo rating: Use tournament-style comparisons to develop continuous quality scores
Designing A/B Tests for AI
A/B testing AI is different from A/B testing UI:
- Variance is high: The same prompt can produce different outputs, so you need more samples
- Context matters: The same change might help for one task and hurt for another
- Metrics lag: AI quality changes may take time to show up in user behavior
- Interaction effects: A change to one part of the conversation affects all subsequent parts Design A/B tests with:
- Sufficient sample sizes to account for output variance
- Segmentation by task type and user experience level
- Multiple metrics (don't optimise for one at the expense of others)
- Guardrails to catch severe quality regressions quickly
Side-by-Side Evaluation Design
For human evaluation of AI outputs:
- Blind evaluation: Evaluators shouldn't know which version is which
- Consistent inputs: Compare outputs generated from the same input
- Structured criteria: Give evaluators specific dimensions to compare on, not just "which is better"
- Multiple evaluators: Use at least 3 evaluators per comparison for reliability
- Diverse inputs: Test across a representative sample of real user inputs
When to Use Comparative vs. Absolute Evaluation
- Comparative: Best for choosing between alternatives, detecting subtle quality differences, and model selection
- Absolute: Best for measuring against a standard, tracking progress over time, and certification
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.
- 11d ago First seen · 40 lines · 21 tokens per session scan A 09ff2058dd59
comparative-evaluation is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 504 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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