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 agentmods add rules/duongductrong/cursor-kit/skillgit clone --depth 1 https://github.com/duongductrong/cursor-kitWhat 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 | $0.00143 | $0.01293 |
| Opus 5 | $0.00072 | $0.00647 |
| Sonnet 5 | $0.00029 | $0.00259 |
| Haiku 4.5 | $0.00014 | $0.00129 |
Grade A, and why
aesthetic 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 2d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Aesthetic
Create aesthetically beautiful interfaces by following proven design principles and systematic workflows.
When to Use This Skill
Use when:
- Building or designing user interfaces
- Analyzing designs from inspiration websites (Dribbble, Mobbin, Behance)
- Generating design images and evaluating aesthetic quality
- Implementing visual hierarchy, typography, color theory
- Adding micro-interactions and animations
- Creating design documentation and style guides
- Need guidance on accessibility and design systems
Core Framework: Four-Stage Approach
1. BEAUTIFUL: Understanding Aesthetics
Study existing designs, identify patterns, extract principles. AI lacks aesthetic sense—standards must come from analyzing high-quality examples and aligning with market tastes.
Reference: references/design-principles.md - Visual hierarchy, typography, color theory, white space principles.
2. RIGHT: Ensuring Functionality
Beautiful designs lacking usability are worthless. Study design systems, component architecture, accessibility requirements.
Reference: references/design-principles.md - Design systems, component libraries, WCAG accessibility standards.
3. SATISFYING: Micro-Interactions
Incorporate subtle animations with appropriate timing (150-300ms), easing curves (ease-out for entry, ease-in for exit), sequential delays.
Reference: references/micro-interactions.md - Duration guidelines, easing curves, performance optimization.
4. PEAK: Storytelling Through Design
Elevate with narrative elements—parallax effects, particle systems, thematic consistency. Use restraint: "too much of anything isn't good."
Reference: references/storytelling-design.md - Narrative elements, scroll-based storytelling, interactive techniques.
Workflows
Workflow 1: Capture & Analyze Inspiration
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.
- 2d ago First seen · 122 lines · 143 tokens per session scan A c332f5bb505a
aesthetic is a cursor rule published in the GitHub repository duongductrong/cursor-kit (21 stars, last pushed 5mo ago), licensed MIT. It adds 143 tokens to every session and 1,293 once invoked, about $0.0007 per session on Opus 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 2026-08-30.
Other cursor rules, from other repositories
vibeflow
Vibeflow spec-driven development methodology — guardrails, pipeline, and project knowledge system.
test-case-to-katalon-studio
Convert Katalon True Platform/TestOps manual test cases into Katalon Studio automation inside a local Studio Test Project checkout. Use when you need to author or extend a .tc test case file and its paired Groovy script under Scripts/, keep test case variable GUIDs consistent with the .ts test suite bindings that read…
execute-test
Execute Katalon True Platform/TestOps tests when the input is an existing test case, manual test case list, test suite, suite collection, execution request, or "run with AI" instruction. Use when you need to create a manual test run, start Run with AI, poll AI session results, schedule automated suites, read…
best_practices
Advanced Best Practices covering Error Handling, Environment Variables, Performance, and Accessibility rules.
test-maintenance
Maintain and evolve a Katalon True Platform/TestOps regression suite as the application changes. Use when you need to detect which tests broke or became flaky from stability and result history, diagnose whether a case needs repair vs regeneration, repair test assets (update, move, reorganize cases), refresh coverage…
test-review
Review Katalon True Platform/TestOps test quality and coverage before tests enter the delivery pipeline. Use when you need to check whether a suite is ready to run, review requirement and configuration coverage, assess test-case quality and flakiness/stability, spot weak or unreliable cases, and produce a review…