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 commands/aotenjou/silicon-paddleocr/ocrgit clone --depth 1 https://github.com/aotenjou/silicon-PaddleOCRWrote 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/commands/aotenjou/silicon-paddleocr/ocr)<a href="https://agentmods.dev/commands/aotenjou/silicon-paddleocr/ocr"><img src="https://agentmods.dev/badge/commands/aotenjou/silicon-paddleocr/ocr.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.00267 |
| Opus 5 | $0.00005 | $0.00133 |
| Sonnet 5 | $0.00002 | $0.00053 |
| Haiku 4.5 | $0.00001 | $0.00027 |
Grade A, and why
ocr 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 4d 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
Context
- 当前工作目录:
pwd - API Key: 已通过 SILICONFLOW_API_KEY 环境变量设置
Your task
调用 OCR skill 识别指定图片中的文字内容。
调用格式：
export SILICONFLOW_API_KEY="sk-xxxxxxxxxxxxx"
python3 ./skills/ocr/scripts/ocr_skill.py [参数] 图片路径
常用参数：
- 图片路径（必选）
-j, --json: 以 JSON 格式输出-o, --output: 保存到指定文件-k, --api-key: 指定 API Key-m, --model: 指定模型--max-tokens: 最大 token 数
示例：
# 单张图片
python3 ./skills/ocr/scripts/ocr_skill.py ./test.jpg
# JSON 输出
python3 ./skills/ocr/scripts/ocr_skill.py -j ./test.jpg
# 多张图片
python3 ./skills/ocr/scripts/ocr_skill.py ./*.png
执行后直接返回识别结果给用户。
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.
- 4d ago First seen · 42 lines · 9 tokens per session scan A 66ddac226f4b
ocr is a command published in the GitHub repository aotenjou/silicon-PaddleOCR (1 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 267 once invoked, about $0.0000 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 2026-08-31.
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