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/create-rubric)<a href="https://agentmods.dev/commands/owl-listener/ai-design-skills/create-rubric"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/create-rubric/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/create-rubric"><img src="https://agentmods.dev/badge/commands/owl-listener/ai-design-skills/create-rubric.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 | $0.00010 | $0.00438 |
| Opus 5 | $0.00005 | $0.00219 |
| Sonnet 5 | $0.00002 | $0.00088 |
| Haiku 4.5 | $0.00001 | $0.00044 |
Grade A, and why
create-rubric 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.
What it actually says
You are building a quality evaluation rubric. Use only skills from the evaluation plugin. Follow this process:
Step 1: Define What You're Evaluating
- What AI feature or output type is this rubric for?
- What does the user expect from this output?
- What are the highest-priority quality attributes?
Step 2: Select Quality Dimensions
Using output-quality-rubrics:
- Select relevant quality dimensions from: accuracy, relevance, completeness, helpfulness, clarity, tone appropriateness, safety
- Add any domain-specific dimensions needed
- Define each dimension in concrete terms for this specific output type
Step 3: Build the Scoring Scale
Using output-quality-rubrics:
- For each dimension, create a 1-5 scale with detailed descriptions at each level
- Write anchor examples at levels 1, 3, and 5
- Define what differentiates each adjacent level
Step 4: Weight the Dimensions
Using output-quality-rubrics and task-success-metrics:
- Assign weights to each dimension based on importance for the user's task
- Justify the weighting
- Define the overall score calculation
Step 5: Map to Failure Types
Using failure-taxonomy:
- For each low score (1-2) on each dimension, identify the corresponding failure type
- This connects the rubric to the failure tracking system
Step 6: Design Calibration
Using output-quality-rubrics:
- Create a calibration set of 5 outputs with pre-scored ratings
- Write evaluator instructions
- Define the calibration process for new evaluators
Output
Deliver a complete evaluation rubric:
- Dimension definitions with scoring scales (1-5)
- Anchor examples at levels 1, 3, and 5 for each dimension
- Dimension weights and overall score formula
- Failure type mapping
- Calibration set with pre-scored outputs
- Evaluator instructions
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 · 43 lines · 10 tokens per session scan A 2f1fb42df2d9
create-rubric is a command published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 10 tokens to every session and 438 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.