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 oyi77/1ai-skills --skill pandacss-stylinggit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/pandacss-styling)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/pandacss-styling"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/pandacss-styling/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/oyi77/1ai-skills/pandacss-styling"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/pandacss-styling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.01474 |
| Opus 5.5 | $0.00014 | $0.00590 |
| Sonnet 5.5 | $0.00007 | $0.00295 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
pandacss-styling 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Panda CSS is a zero-runtime CSS-in-JS framework with a token system, patterns, recipes, and JSX style props. Generates static CSS at build time with full TypeScript support.
Capabilities
- Zero-runtime CSS generation
- Design token system with semantic tokens
- Patterns for common layout primitives
- Recipes for component variants
- Conditions for responsive and state styles
- JSX style props for inline styling
- Works with React, Vue, Solid, Qwik
When to Use
Trigger phrases:
-
"pandacss styling"
-
"Panda CSS zero-runtime styling — token system, patterns, recipes, conditions, JS"
-
Want type-safe styling with design tokens
-
Need component variants without runtime cost
-
Building design systems with consistent tokens
-
Using JSX style props for rapid development
When NOT to Use
- Task is about content strategy, not creation (use strategy skills)
- Task is about content distribution (use distribution skills)
- You need to analyze content performance (use analytics skills)
- Task is about content moderation (use moderation tools)
- You don't have content guidelines
- Task requires domain expertise (consult experts)
Pseudo Code
The pandacss-styling workflow follows a standard pipeline pattern.
Core flow:
# pandacss-styling primary flow
input = prepare(raw_data)
result = process(input, config={conditions, panda, pandacss, patterns, recipes})
validate(result)
deliver(result)
Error handling:
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Core Workflow
# pandacss-styling primary flow
input = prepare(raw_data)
result = process(input, config={conditions, panda, pandacss, patterns, recipes})
validate(result)
deliver(result)
Error Handling
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Configuration
// panda.config.ts
import { defineConfig } from "@pandacss/dev"
export default defineConfig({
preflight: true,
include: ["./src/**/*.{ts,tsx}"],
exclude: [],
theme: {
extend: {
tokens: {
colors: {
primary: { value: "#3b82f6" },
secondary: { value: "#8b5cf6" },
},
},
},
},
outdir: "styled-system",
})
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 · 234 lines · 36 tokens per session scan A 3b9d89095bdb
pandacss-styling is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 1,474 once invoked, about $0.0001 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 2026-09-03.
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