Borrowing it
Nothing to install: this file belongs to ReScienceLab/TrySkills. 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/ReScienceLab/TrySkills/main/.agents/skills/critique/SKILL.mdgit clone --depth 1 https://github.com/ReScienceLab/TrySkillsWrote 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/resciencelab/tryskills/critique)<a href="https://agentmods.dev/skills/resciencelab/tryskills/critique"><img src="https://agentmods.dev/badge/skills/resciencelab/tryskills/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/resciencelab/tryskills/critique"><img src="https://agentmods.dev/badge/skills/resciencelab/tryskills/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 | $0.00064 | $0.03056 |
| Opus 5.5 | $0.00026 | $0.01222 |
| Sonnet 5.5 | $0.00013 | $0.00611 |
| Haiku 4.5 | $0.00006 | $0.00306 |
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.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STEPS
Step 1: Preparation
Invoke /impeccable, which contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding. If no design context exists yet, you MUST run /impeccable teach first. Additionally gather: what the interface is trying to accomplish.
Step 2: Gather Assessments
Launch two independent assessments. Neither must see the other's output to avoid bias.
You SHOULD delegate each assessment to a separate sub-agent for independence. Use your environment's agent spawning mechanism (e.g., Claude Code's Agent tool, or Codex's subagent spawning). Sub-agents should return their findings as structured text. Do NOT output findings to the user yet.
If sub-agents are not available in the current environment, complete each assessment sequentially, writing findings to internal notes before proceeding.
Tab isolation: When browser automation is available, each assessment MUST create its own new tab. Never reuse an existing tab, even if one is already open at the correct URL. This prevents the two assessments from interfering with each other's page state.
Assessment A: LLM Design Review
Read the relevant source files (HTML, CSS, JS/TS) and, if browser automation is available, visually inspect the live page. Create a new tab for this; do not reuse existing tabs. After navigation, label the tab by setting the document title:
document.title = '[LLM] ' + document.title;
Think like a design director. Evaluate:
AI Slop Detection (CRITICAL): Does this look like every other AI-generated interface? Review against ALL DON'T guidelines in the impeccable skill. Check for AI color palette, gradient text, dark glows, glassmorphism, hero metric layouts, identical card grids, generic fonts, and all other tells. The test: If someone said "AI made this," would you believe them immediately?
Holistic Design Review: visual hierarchy (eye flow, primary action clarity), information architecture (structure, grouping, cognitive load), emotional resonance (does it match brand and audience?), discoverability (are interactive elements obvious?), composition (balance, whitespace, rhythm), typography (hierarchy, readability, font choices), color (purposeful use, cohesion, accessibility), states & edge cases (empty, loading, error, success), microcopy (clarity, tone, helpfulness).
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 · 225 lines · 64 tokens per session scan A fd4629ea70b7
critique is a skill published in the GitHub repository ReScienceLab/TrySkills (3 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 3,056 once invoked, about $0.0003 per session on Opus 5.5. 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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