Borrowing it
Nothing to install: this file belongs to guillermoscript/lms-front. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guillermoscript/lms-front/master/.agents/skills/critique/SKILL.mdgit clone --depth 1 https://github.com/guillermoscript/lms-frontWrote 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/guillermoscript/lms-front/critique)<a href="https://agentmods.dev/skills/guillermoscript/lms-front/critique"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/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/guillermoscript/lms-front/critique"><img src="https://agentmods.dev/badge/skills/guillermoscript/lms-front/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 | $0.00030 | $0.01126 |
| Opus 5.5 | $0.00012 | $0.00450 |
| Sonnet 5.5 | $0.00006 | $0.00225 |
| Haiku 4.5 | $0.00003 | $0.00113 |
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
100% identical to critique — 0 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
First: Use the frontend-design skill for design principles and anti-patterns.
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
- Is the structure intuitive? Would a new user understand the organization?
- Is related content grouped logically?
- Are there too many choices at once? (cognitive overload)
- Is the navigation clear and predictable?
4. Emotional Resonance
- What emotion does this interface evoke? Is that intentional?
- Does it match the brand personality?
- Does it feel trustworthy, approachable, premium, playful—whatever it should feel?
- Would the target user feel "this is for me"?
5. Discoverability & Affordance
- Are interactive elements obviously interactive?
- Would a user know what to do without instructions?
- Are hover/focus states providing useful feedback?
- Are there hidden features that should be more visible?
6. Composition & Balance
- Does the layout feel balanced or uncomfortably weighted?
- Is whitespace used intentionally or just leftover?
- Is there visual rhythm in spacing and repetition?
- Does asymmetry feel designed or accidental?
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 · 118 lines · 30 tokens per session scan A 244084b8fefc
critique is a skill published in the GitHub repository guillermoscript/lms-front (40 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,126 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to critique, differing in 0 lines, and is treated as a copy.
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