python-office is a Python library that provides ready-to-use modules for automating office and media tasks such as PDF, Word, Excel, PowerPoint, email, WeChat, images, video, and OCR. It is for people who want to automate common work activities with short Python calls, including users with little Python experience. Its catalogue skills expose the library's individual automation areas to coding agents.
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 CoderWanFeng/python-office --skill chat_by_keywordsgit clone --depth 1 https://github.com/CoderWanFeng/python-officeWrote 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/coderwanfeng/python-office/chat_by_keywords)<a href="https://agentmods.dev/skills/coderwanfeng/python-office/chat_by_keywords"><img src="https://agentmods.dev/badge/skills/coderwanfeng/python-office/chat_by_keywords/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/coderwanfeng/python-office/chat_by_keywords"><img src="https://agentmods.dev/badge/skills/coderwanfeng/python-office/chat_by_keywords.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 | $0.00031 | $0.00251 |
| Opus 5.5 | $0.00012 | $0.00100 |
| Sonnet 5.5 | $0.00006 | $0.00050 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
chat_by_keywords 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.
What it actually says
chat_by_keywords Skill
根据关键词与指定联系人聊天
功能描述
通过微信自动化，根据关键词与指定联系人进行自动聊天。
所属分类
office/skills/wechat/chat_by_keywords/
调用方式
from skills.wechat import chat_by_keywords
chat_by_keywords(who='文件传输助手', keywords=['你好', '在吗'])
参数说明
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|---|---|---|---|---|
who |
str | 是 | - | 进行聊天的联系人名称 |
keywords |
list | 是 | - | 触发聊天的关键词列表 |
返回值
None
使用示例
from skills.wechat import chat_by_keywords
chat_by_keywords(who='文件传输助手', keywords=['你好', 'hello', 'hi'])
原始函数
office.api.wechat.chat_by_keywords
What ships with it
1 file 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.
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 · 47 lines · 31 tokens per session scan A d8c1a1bac0f4
chat_by_keywords is a skill published in the GitHub repository CoderWanFeng/python-office (1,361 stars, last pushed 3d ago), licensed Apache-2.0. It adds 31 tokens to every session and 251 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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