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 wjxcom/wjx-ai-kit --skill qrgenid-skillgit clone --depth 1 https://github.com/wjxcom/wjx-ai-kitWrote 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/wjxcom/wjx-ai-kit/qrgenid-skill)<a href="https://agentmods.dev/skills/wjxcom/wjx-ai-kit/qrgenid-skill"><img src="https://agentmods.dev/badge/skills/wjxcom/wjx-ai-kit/qrgenid-skill/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/wjxcom/wjx-ai-kit/qrgenid-skill"><img src="https://agentmods.dev/badge/skills/wjxcom/wjx-ai-kit/qrgenid-skill.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 | $0.00060 | $0.01451 |
| Opus 5 | $0.00030 | $0.00726 |
| Sonnet 5 | $0.00012 | $0.00290 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
qrgenid 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 3d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
qrgenid
从 Excel 工作簿批量生成二维码：二维码本体只编码 URL，ID 作为黑色文字显示在二维码下方。最终产物三件套：
output.xlsx：二维码图片实际嵌入单元格（不是路径）qrcodes/：PNG 文件目录qrgenid.log：完整处理日志
本 skill 直接运行自带源码（scripts/ 目录），仅支持 Windows。
When to Use
用户提到以下任一意图都应触发，无论是否明确叫出 qrgenid 这个名字：
- "Excel 生成二维码" / "xlsx 二维码" / "从表格做二维码"
- "QR code batch" / "Excel to QR"
- "批量二维码" / "给这表做二维码"
约定
- 本
SKILL.md所在目录记为<SKILL_DIR>，所有命令都在<SKILL_DIR>下执行。 - 运行命令统一为：
python -m scripts ...（源码在<SKILL_DIR>/scripts/）。
Step 1: 运行前自检
运行：
python -m scripts --version
- 输出
0.1.0→ 进入 Step 2。 - 报
ModuleNotFoundError（缺Pillow/XlsxWriter/openpyxl/qrcode/xlrd等）→ 按references/install.md给出一次性依赖补齐命令，然后停止，等用户装好后重试。 - 其他启动错误 → 参考
references/troubleshooting.md。
Step 2: 收集参数
需要 6 个参数：
| 参数 | CLI 开关 | 必填 | 默认 |
|---|---|---|---|
| 输入 Excel 路径 | --input |
✅ | — |
| 输出目录 | --output-dir |
❌ | 输入文件所在目录 |
| ID 列（字母或 1-based 列号） | --id-column |
❌ | A |
| URL 列（字母或 1-based 列号） | --url-column |
❌ | B |
| 工作表名或序号（互斥） | --sheet / --sheet-index |
❌ | 第一张表 |
| 二维码主体尺寸 | --size |
❌ | 150（可选 150/256/512） |
- 用户已给全参数 → 直接进入 Step 3 的确认。
- 参数不全 → 用你的文件读取能力读取 Excel 表头与样本行，手动确认 ID 列 / URL 列（表头含 "ID/编号/编码/序号" 倾向 ID，含 "URL/链接/网址/二维码" 倾向 URL；默认 A=ID、B=URL）。
- 多张表时列出所有工作表名，默认选第一张。
- 尺寸默认 150，除非用户明确说“高清/大尺寸”或给出数字。
- 输出目录默认 = 输入文件所在目录，必须告知用户这个默认行为。
Step 3: 覆盖风险确认（不可跳过）
执行前必须明确告知用户两项覆盖风险：
output.xlsx会被覆盖（程序先写临时文件、校验后替换，但正式文件仍被覆盖）qrcodes/根目录下的旧.png文件会被清空（子目录和其他文件保留）
等待用户明确确认后才调用命令。用户拒绝 → 停止，告知任务已取消。
Step 4: 执行
python -m scripts --input "<input-path>" --output-dir "<output-dir>" --id-column <col> --url-column <col> [--sheet <name> | --sheet-index <n>] --size <150|256|512>
- 路径含空格或特殊字符时用引号。
- 捕获 stdout（JSON）与 stderr（日志）；stderr 仅在排障时读取，不主动展示给用户。
Step 5: 按退出码分支处理
解析 stdout 中的 JSON（字段语义详见 references/exit-codes.md）。
退出码 0（全成功，status=success）
报告：✅ 全部成功 N 张；output.xlsx、qrcodes/、qrgenid.log 的绝对路径。
退出码 1（部分失败/取消，status=partial_failure|cancelled）
What ships with it
16 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.
- references/cli.md 1.5 KB
- references/exit-codes.md 2.1 KB
- references/install.md 1.2 KB
- references/safety.md 1.1 KB
- references/troubleshooting.md 2.0 KB
- scripts/__init__.py 77 B runs code
- scripts/__main__.py 633 B runs code
- scripts/cli.py 3.0 KB runs code
- scripts/core.py 8.8 KB runs code
- scripts/excel.py 7.9 KB runs code
- scripts/exceptions.py 406 B runs code
- scripts/gui.py 7.5 KB runs code
- scripts/models.py 2.1 KB runs code
- scripts/pyproject.toml 548 B
- scripts/qr.py 5.4 KB runs code
- scripts/writer.py 3.6 KB runs code
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.
- 3d ago First seen · 116 lines · 60 tokens per session scan A fedcd56ae4ba
qrgenid is a skill published in the GitHub repository wjxcom/wjx-ai-kit (18 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,451 once invoked, about $0.0003 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 2026-09-09.
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