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 malue-ai/dazee-small --skill qr-codegit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/qr-code)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/qr-code"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/qr-code/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/malue-ai/dazee-small/qr-code"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/qr-code.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 | $0.00022 | $0.00580 |
| Opus 5 | $0.00011 | $0.00290 |
| Sonnet 5 | $0.00004 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
qr-code 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 9d 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.
What it actually says
QR 码生成与识别
生成和识别 QR 码,支持文本、URL、WiFi 凭据、名片(vCard)等格式。
使用场景
- 用户说「帮我生成一个二维码」「把这个链接做成二维码」
- 用户说「生成 WiFi 二维码,方便客人连接」
- 用户说「识别这个二维码图片里的内容」
执行方式
生成 QR 码
import qrcode
# 基本用法
img = qrcode.make("https://example.com")
img.save("/tmp/qr.png")
# 自定义样式
qr = qrcode.QRCode(version=1, box_size=10, border=4)
qr.add_data("https://example.com")
qr.make(fit=True)
img = qr.make_image(fill_color="black", back_color="white")
img.save("/tmp/qr.png")
WiFi QR 码
wifi_data = "WIFI:T:WPA;S:MyNetwork;P:MyPassword;;"
img = qrcode.make(wifi_data)
img.save("/tmp/wifi_qr.png")
格式:WIFI:T:{加密类型};S:{SSID};P:{密码};;
加密类型:WPA、WEP、nopass
名片 QR 码(vCard)
vcard = """BEGIN:VCARD
VERSION:3.0
FN:张三
TEL:+86-138-0000-0000
EMAIL:[email protected]
END:VCARD"""
img = qrcode.make(vcard)
img.save("/tmp/contact_qr.png")
识别 QR 码
from PIL import Image
from pyzbar.pyzbar import decode
img = Image.open("qr_image.png")
results = decode(img)
for r in results:
print(r.data.decode("utf-8"))
注意:识别功能需要额外安装 pyzbar(pip install pyzbar)和系统库 zbar。
输出规范
- 生成后返回图片文件路径
- WiFi 二维码说明使用方式(手机相机扫描即可连接)
- 不在日志中记录 WiFi 密码
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.
- 9d ago First seen · 88 lines · 22 tokens per session scan A daa0e6599c27
qr-code is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 580 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…