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 guyulong/cn-agent-skills --skill wechat-authgit clone --depth 1 https://github.com/guyulong/cn-agent-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/guyulong/cn-agent-skills/wechat-auth)<a href="https://agentmods.dev/skills/guyulong/cn-agent-skills/wechat-auth"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/wechat-auth/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/guyulong/cn-agent-skills/wechat-auth"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/wechat-auth.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 | $0.00018 | $0.00643 |
| Opus 5 | $0.00009 | $0.00321 |
| Sonnet 5 | $0.00004 | $0.00129 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
wechat-auth scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(url, params=params).json() What it actually says
微信登录授权集成
使用场景
集成微信登录功能,支持公众号、小程序、开放平台三种方式。
小程序登录流程
前端 wx.login() → 获取 code
↓
后端用 code 换取 openid/session_key
↓
生成自定义 token 返回给前端
↓
前端保存 token,后续请求携带
后端代码 (Python/Flask)
import requests
def wx_login(code: str) -> dict:
"""微信小程序登录"""
url = "https://api.weixin.qq.com/sns/jscode2session"
params = {
"appid": APPID,
"secret": SECRET,
"js_code": code,
"grant_type": "authorization_code"
}
resp = requests.get(url, params=params).json()
if "errcode" in resp:
raise ValueError(f"微信登录失败: {resp['errmsg']}")
openid = resp["openid"]
session_key = resp["session_key"]
# 生成自定义token
token = generate_token(openid)
return {"token": token, "openid": openid}
公众号OAuth登录
def get_wechat_auth_url(redirect_uri: str, state: str = "STATE") -> str:
"""获取微信授权URL"""
return (
f"https://open.weixin.qq.com/connect/oauth2/authorize"
f"?appid={APPID}"
f"&redirect_uri={redirect_uri}"
f"&response_type=code"
f"&scope=snsapi_userinfo"
f"&state={state}"
f"#wechat_redirect"
)
def get_wechat_userinfo(code: str) -> dict:
"""通过code获取用户信息"""
# 1. 获取access_token
token_url = "https://api.weixin.qq.com/sns/oauth2/access_token"
token_resp = requests.get(token_url, params={
"appid": APPID, "secret": SECRET,
"code": code, "grant_type": "authorization_code"
}).json()
# 2. 获取用户信息
user_url = "https://api.weixin.qq.com/sns/userinfo"
user_resp = requests.get(user_url, params={
"access_token": token_resp["access_token"],
"openid": token_resp["openid"],
"lang": "zh_CN"
}).json()
return user_resp
安全注意事项
session_key不要返回给前端- 不要用
wx.getUserInfo(已废弃),用wx.getUserProfile - 建议使用云函数处理敏感逻辑
- 定期刷新 access_token
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.
- 8d ago First seen · 92 lines · 18 tokens per session scan A 48f9918aaa29
wechat-auth is a skill published in the GitHub repository guyulong/cn-agent-skills (3 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 643 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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