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 output-quality-rubricsgit 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/output-quality-rubrics)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/output-quality-rubrics"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/output-quality-rubrics/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/output-quality-rubrics"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/output-quality-rubrics.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 | $0.00025 | $0.00511 |
| Opus 5 | $0.00013 | $0.00255 |
| Sonnet 5 | $0.00005 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
output-quality-rubrics 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 12d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output Quality Rubrics
Without a rubric, quality evaluation is subjective and inconsistent. A rubric defines what "good" means in concrete, measurable terms — so different evaluators reach the same conclusions.
Core Quality Dimensions
- Accuracy: Is the information correct? Are claims verifiable? Are there hallucinations?
- Relevance: Does the output address what the user actually asked? Is everything included necessary?
- Completeness: Does the output cover everything needed? Are there gaps?
- Helpfulness: Can the user actually use this output to accomplish their goal?
- Clarity: Is the output easy to understand? Is it well-structured?
- Tone appropriateness: Does the output match the expected tone for the context?
- Safety: Is the output free from harmful, biased, or inappropriate content?
Building a Rubric
For each dimension, define a scale: Example — Accuracy (1-5):
- 5: All claims are verifiable and correct. No hallucinations.
- 4: Minor inaccuracies that don't affect usefulness. No hallucinations.
- 3: Some inaccuracies that could mislead if not caught. No dangerous hallucinations.
- 2: Significant inaccuracies. User would need to verify most claims.
- 1: Major hallucinations or factually wrong information presented confidently.
Weighting Dimensions
Not all dimensions matter equally for every use case:
- A medical AI weights accuracy and safety highest
- A creative writing AI weights helpfulness and tone highest
- A coding AI weights accuracy and completeness highest
- A customer service AI weights tone and helpfulness highest Define weights when creating the rubric. Make the priorities explicit.
Rubric Calibration
A rubric is only useful if evaluators use it consistently:
- Anchor examples: Provide sample outputs at each score level
- Calibration sessions: Have multiple evaluators score the same outputs and discuss disagreements
- Inter-rater reliability: Measure agreement between evaluators and refine the rubric until agreement is high
- Edge case guidance: Document how to score ambiguous cases
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.
- 12d ago First seen · 42 lines · 25 tokens per session scan A d35fa1b79d89
output-quality-rubrics is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 511 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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