Borrowing it
Nothing to install: this file belongs to lanesket/llm.log. 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/lanesket/llm.log/main/.agents/skills/critique/SKILL.mdgit clone --depth 1 https://github.com/lanesket/llm.logWrote 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/lanesket/llm.log/critique)<a href="https://agentmods.dev/skills/lanesket/llm.log/critique"><img src="https://agentmods.dev/badge/skills/lanesket/llm.log/critique.svg" alt="Measured on agentmods" 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 | $0.01187 |
| Opus 5 | $0.00015 | $0.00593 |
| Sonnet 5 | $0.00006 | $0.00237 |
| Haiku 4.5 | $0.00003 | $0.00119 |
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 8d 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
94% identical to critique — 12 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Use the frontend-design skill — 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.
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?
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.
- 8d ago First seen · 122 lines · 30 tokens per session scan A c1d7e64db672
critique is a skill published in the GitHub repository lanesket/llm.log (22 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 1,187 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to critique, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
qa-design
UI/UX design audit and verification of web best practices, including responsive/mobile-first breakpoints. Trigger when the user wants to audit the design, verify the UI/UX, check responsive behaviour, or improve the user interface.
dev-frontend-design
Distinctive UI design with strong art direction. Trigger when the user wants to create an interface, a landing page, a visual component, or when frontend code creation is detected without a defined design direction.
dev-shadcn
Integration and customization of shadcn/ui (copy-paste React components, Radix + Tailwind). Trigger when the user wants to install shadcn, add components, customize the theme, or when shadcn/ui usage is detected in the project.
options
Present multiple design options as a vertical stack of anchored turns.
qa-test-planner
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.
visualize
Render a polished visual inline in the chat as part of your answer — a diagram, a chart, an interactive explainer, or a UI mockup. Load it proactively whenever an explanation would land better as a picture than as prose. Do not wait to be asked.