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 arnabdeypolimi/claude_code_setup --skill critiquegit clone --depth 1 https://github.com/arnabdeypolimi/claude_code_setupWrote 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/arnabdeypolimi/claude_code_setup/critique)<a href="https://agentmods.dev/skills/arnabdeypolimi/claude_code_setup/critique"><img src="https://agentmods.dev/badge/skills/arnabdeypolimi/claude_code_setup/critique/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/arnabdeypolimi/claude_code_setup/critique"><img src="https://agentmods.dev/badge/skills/arnabdeypolimi/claude_code_setup/critique.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 | $0.00059 | $0.02517 |
| Opus 5.5 | $0.00024 | $0.01007 |
| Sonnet 5.5 | $0.00012 | $0.00503 |
| Haiku 4.5 | $0.00006 | $0.00252 |
Grade A, and why
critique 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 1mo 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.
This is a copy
89% identical to critique — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /frontend-design — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: what the interface is trying to accomplish.
Conduct a holistic design critique, evaluating whether the interface actually works — not just technically, but as a designed experience. Think like a design director giving feedback.
Phase 1: Design Critique
Evaluate the interface across these dimensions:
1. AI Slop Detection (CRITICAL)
This is the most important check. Does this look like every other AI-generated interface from 2024-2025?
Review the design against ALL the DON'T guidelines in the frontend-design skill — they are the fingerprints of AI-generated work. Check for the AI color palette, gradient text, dark mode with glowing accents, glassmorphism, hero metric layouts, identical card grids, generic fonts, and all other tells.
The test: If you showed this to someone and said "AI made this," would they believe you immediately? If yes, that's the problem.
2. Visual Hierarchy
- Does the eye flow to the most important element first?
- Is there a clear primary action? Can you spot it in 2 seconds?
- Do size, color, and position communicate importance correctly?
- Is there visual competition between elements that should have different weights?
3. Information Architecture & Cognitive Load
Consult cognitive-load for the working memory rule and 8-item checklist
- Is the structure intuitive? Would a new user understand the organization?
- Is related content grouped logically?
- Are there too many choices at once? Count visible options at each decision point — if >4, flag it
- Is the navigation clear and predictable?
- Progressive disclosure: Is complexity revealed only when needed, or dumped on the user upfront?
- Run the 8-item cognitive load checklist from the reference. Report failure count: 0–1 = low (good), 2–3 = moderate, 4+ = critical.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 201 lines · 59 tokens per session scan A 01dc72ed1960
critique is a skill published in the GitHub repository arnabdeypolimi/claude_code_setup (4 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 2,517 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to critique, differing in 13 lines, and is treated as a copy.
Other skills, from other repositories
brainstorming
Design discovery process that scales with task size and user tier. Prevents wasted work on wrong assumptions while staying out of the way for small tasks. Enforces design-before-code discipline with architecture-aware constraints.
accessibility
WCAG 2.1 AA baseline — semantic HTML, ARIA labels, color contrast, keyboard navigation, screen reader compatibility, and focus management.
figma-verification
Extract Figma design specs before implementing UI components. Use depth 5, cache to disk, extract ALL properties, map to design system tokens.
interaction-design
Use when designing how a UI feels to use. Covers audience dimensions, discoverability, mode communication, keyboard-first UX, feedback timing, empty states, and information density. Includes WCAG 2.1 AA compliance audit. Companion to visual-design.
figma-extract
Exhaustive Figma node extraction: download every detail down to leaf nodes and cache everything locally. Call Figma once, never twice. Use before any pixel-perfect implementation. Prioritizes complete local dumps over repeated API calls.
visual-design
Audit any application's visual identity and generate a distinctive direction that passes the AI slop test. Use before writing UI code to set color, typography, spacing, and motion tokens.