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 huangdijia/wechat-skills --skill wechatgit clone --depth 1 https://github.com/huangdijia/wechat-skillsWrote 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/huangdijia/wechat-skills/wechat)<a href="https://agentmods.dev/skills/huangdijia/wechat-skills/wechat"><img src="https://agentmods.dev/badge/skills/huangdijia/wechat-skills/wechat/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/huangdijia/wechat-skills/wechat"><img src="https://agentmods.dev/badge/skills/huangdijia/wechat-skills/wechat.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 | $0.00021 | $0.00482 |
| Opus 5 | $0.00010 | $0.00241 |
| Sonnet 5 | $0.00004 | $0.00096 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
WeChat 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 10d 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
macOS WeChat automation - sending messages and images via pyautogui keyboard simulation.
Features
- Open WeChat (via Spotlight search)
- Search for contacts/groups
- Send text messages
- Send images
Requirements
- macOS system
- Python 3.x
- WeChat desktop app
Dependencies
pip install pyautogui pyperclip pyperclipimg
Usage
cd scripts
# Send text message
python3 wechat_send.py --name "Contact Name" --message "Message content"
# Send image
python3 wechat_send.py --name "Contact Name" --image_path="/path/to/image.png"
# Send both message and image
python3 wechat_send.py --name "Contact Name" --message "Check this out" --image_path="test.png"
Arguments
| Argument | Required | Description |
|---|---|---|
--name |
Yes | Contact or group name |
--message |
At least one | Text message to send |
--image_path |
At least one | Path to image file |
Note: At least one of --message or --image_path must be provided.
How It Works
- Open WeChat: Simulate
Cmd+Spaceto open Spotlight, search for "WeChat" and launch - Search contact: Simulate
Cmd+Fto open search, paste contact name, press Enter to enter chat - Send message: Copy text to clipboard, paste and send
- Send image: Copy image to clipboard, paste and send
Important Notes
- Accessibility permission is required for terminal/IDE (System Settings > Privacy & Security > Accessibility)
- WeChat must be logged in
- Contact name must match exactly as shown in WeChat
- Do not operate mouse/keyboard during sending to avoid interference
File Structure
wechat-skill/
├── SKILL.md # This documentation
├── scripts/
│ └── wechat_send.py # Main script
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.
- 10d ago First seen · 76 lines · 21 tokens per session scan A 5587adf3a871
WeChat is a skill published in the GitHub repository huangdijia/wechat-skills (11 stars, last pushed 7mo ago), licensed MIT. It adds 21 tokens to every session and 482 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 2026-08-30.
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