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 subhansh-dev/agent-maxxing --skill 08-critiquegit clone --depth 1 https://github.com/subhansh-dev/agent-maxxingWrote 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/subhansh-dev/agent-maxxing/08-critique)<a href="https://agentmods.dev/skills/subhansh-dev/agent-maxxing/08-critique"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/08-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/subhansh-dev/agent-maxxing/08-critique"><img src="https://agentmods.dev/badge/skills/subhansh-dev/agent-maxxing/08-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 | $0.01172 |
| Opus 5 | $0.00015 | $0.00586 |
| Sonnet 5 | $0.00006 | $0.00234 |
| Haiku 4.5 | $0.00003 | $0.00117 |
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 5d 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
91% identical to critique — 15 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 — 119 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.
- 5d ago First seen · 119 lines · 30 tokens per session scan A da015c2b9dce
critique is a skill published in the GitHub repository subhansh-dev/agent-maxxing (2 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,172 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to critique, differing in 15 lines, and is treated as a copy.
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