Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/realmpastai-web/claude-design-suitenpx agentmods add skills/realmpastai-web/claude-design-suite/apply-reasoning-ruleWrote 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/realmpastai-web/claude-design-suite/apply-reasoning-rule)<a href="https://agentmods.dev/skills/realmpastai-web/claude-design-suite/apply-reasoning-rule"><img src="https://agentmods.dev/badge/skills/realmpastai-web/claude-design-suite/apply-reasoning-rule/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/realmpastai-web/claude-design-suite/apply-reasoning-rule"><img src="https://agentmods.dev/badge/skills/realmpastai-web/claude-design-suite/apply-reasoning-rule.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 | $0.00061 | $0.00413 |
| Opus 5 | $0.00030 | $0.00206 |
| Sonnet 5 | $0.00012 | $0.00083 |
| Haiku 4.5 | $0.00006 | $0.00041 |
Grade A, and why
apply-reasoning-rule 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.
What it actually says
Apply Reasoning Rule
Context
You are a design-lead backing up a proposal with a documented rule for $ARGUMENTS. The 161 rules in data/ui-reasoning.csv are industry-sourced heuristics — each with a rule name, condition, and rationale.
Domain Context
- Design reviews go better when decisions cite a rule, not a taste preference.
- Rules cover: hierarchy, error states, empty states, loading, density (dashboards vs marketing), micro-copy, onboarding, settings, privacy UX, monetization, trust signals.
- Each row has
Rule,When,Why,Do,Avoidcolumns.
Instructions
- Describe the design decision you're trying to justify — 1 sentence with product type + pattern name.
- Run:
python scripts/search.py "<decision>" --domain ux --max-results 3 - Present each hit as a quotable card:
- Rule:
<name> - When:
- Why:
- Do:
- Avoid:
- Rule:
- Pick the most applicable one and write 2 sentences of "how this applies to our situation" connecting the rule to the concrete decision.
- If no rule scores above 5, say so — the decision may be novel, and the user should document a new rule.
Further Reading
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 · 39 lines · 61 tokens per session scan A 8057d6fa7767
apply-reasoning-rule is a skill published in the GitHub repository realmpastai-web/claude-design-suite (2 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 413 once invoked, about $0.0003 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 2026-08-31.
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