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 skills/djblack1209-coder/openclaw-bot/glmocrnpx skills add djblack1209-coder/OpenClaw-Bot --skill glmocrgit clone --depth 1 https://github.com/djblack1209-coder/OpenClaw-BotWrote 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/djblack1209-coder/openclaw-bot/glmocr)<a href="https://agentmods.dev/skills/djblack1209-coder/openclaw-bot/glmocr"><img src="https://agentmods.dev/badge/skills/djblack1209-coder/openclaw-bot/glmocr.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 | $0.00087 | $0.01510 |
| Opus 5 | $0.00044 | $0.00755 |
| Sonnet 5 | $0.00017 | $0.00302 |
| Haiku 4.5 | $0.00009 | $0.00151 |
Grade A, and why
glmocr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GLM-OCR 统一文字识别技能
智谱 GLM-OCR（OmniDocBench V1.5 第一名），支持 4 种识别模式。
识别模式
| 模式 | 说明 | 输出格式 |
|---|---|---|
| general | 通用文字提取（默认） | Markdown |
| table | 表格识别 | Markdown 表格 |
| formula | 数学公式识别 | LaTeX |
| handwriting | 手写体识别 | Markdown |
ClawBot 自动集成（已内置）
直接在 Telegram 发图片或 PDF，ClawBot 自动：
- OCR 识别文字
- 智能场景路由（交易/电商/通用）
- 交易场景：提取指标 → 写入记忆 → 建议触发 /invest
- 电商场景：提取价格 → 竞品分析 → 定价建议
群聊中需 @bot 或 caption 含触发词（OCR/识别/分析/竞品/财报）。
CLI 用法（开发/调试）
export ZHIPU_API_KEY="$ZHIPU_API_KEY"
# 通用 OCR
python apps/openclaw/tools/scripts/glm_ocr_cli.py --file image.png --pretty
# 保存结果
python apps/openclaw/tools/scripts/glm_ocr_cli.py --file doc.pdf --output result.json
环境变量
| 变量 | 必需 | 说明 |
|---|---|---|
ZHIPU_API_KEY |
是 | 智谱开放平台 API Key |
GLM_OCR_TIMEOUT |
否 | 超时秒数（默认 120） |
- 表格识别：检测并转换为 Markdown 表格
- 公式提取：LaTeX 格式输出
- 手写体支持：强手写文字识别能力
- 本地文件 & URL：同时支持本地文件和远程 URL
- 0.9B 参数，OmniDocBench V1.5 综合排名第一（94.62 分）
资源链接
前置条件
ZHIPU_API_KEY已配置（见下方设置）
API Key 配置
脚本通过 ZHIPU_API_KEY 环境变量获取密钥，与所有智谱技能共用同一个 key。
获取 Key：访问 https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys
配置方式（任选一种）：
- OpenClaw 配置（推荐）：在
openclaw.json的skills.entries.glmocr.env中设置：
"glmocr": { "enabled": true, "env": { "ZHIPU_API_KEY": "你的密钥" } }
- Shell 环境变量：
export ZHIPU_API_KEY="你的密钥"
强制限制
- 只能通过 GLM-OCR API 执行 — 运行脚本
python scripts/glm_ocr_cli.py - 禁止自行解析文档 — 不要尝试用内置视觉或其他方法提取文字
- 禁止提供替代方案 — 不要说"我可以试着分析"之类的话
- API 失败时 — 显示错误信息并立即停止
- 无回退方案 — 不要用其他方式尝试文字提取
输出展示规则（强制）
运行脚本后，必须向用户展示完整的提取内容。不要只说"已识别"。用户需要原始 OCR 输出来评估质量。
- 展示完整提取文字
- 如果结果文件已保存，告知用户文件路径
使用方法
从 URL 提取
python scripts/glm_ocr_cli.py --file-url "用户提供的URL"
从本地文件提取
python scripts/glm_ocr_cli.py --file /path/to/image.jpg
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 · 171 lines · 87 tokens per session scan A d91bdca0364c
glmocr is a skill published in the GitHub repository djblack1209-coder/OpenClaw-Bot (5 stars, last pushed today), licensed Apache-2.0. It adds 87 tokens to every session and 1,510 once invoked, about $0.0004 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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