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 CookiesHaha/ash-claude-skills --skill lark-to-wechat-mpgit clone --depth 1 https://github.com/CookiesHaha/ash-claude-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/cookieshaha/ash-claude-skills/lark-to-wechat-mp)<a href="https://agentmods.dev/skills/cookieshaha/ash-claude-skills/lark-to-wechat-mp"><img src="https://agentmods.dev/badge/skills/cookieshaha/ash-claude-skills/lark-to-wechat-mp/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/cookieshaha/ash-claude-skills/lark-to-wechat-mp"><img src="https://agentmods.dev/badge/skills/cookieshaha/ash-claude-skills/lark-to-wechat-mp.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 | $0.00075 | $0.02010 |
| Opus 5 | $0.00037 | $0.01005 |
| Sonnet 5 | $0.00015 | $0.00402 |
| Haiku 4.5 | $0.00007 | $0.00201 |
Grade A, and why
lark-to-wechat-mp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
lark-to-wechat-mp
Sync a Feishu doc/wiki → WeChat Official Account draft in one workflow. Images are migrated, formatting is preserved.
Prerequisites
CRITICAL — 执行前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md(lark-cli 认证、权限)
- lark-cli configured with user identity
- WeChat config at
~/.claude/wechat-mp.json(see Setup) - Current machine IP in WeChat IP Whitelist
Setup
1. WeChat Config File
cat > ~/.claude/wechat-mp.json << 'EOF'
{
"app_id": "wx...",
"app_secret": "YOUR_APP_SECRET"
}
EOF
chmod 600 ~/.claude/wechat-mp.json
2. IP Whitelist(首次必做,只做一次)
- 打开 微信公众平台 → 设置与开发 → 基本配置
- IP白名单 → 添加当前机器 IP
- 获取当前 IP:
curl -s ifconfig.me
Workflow(每次同步执行以下步骤)
Step 1 — 读取配置,获取 Access Token
APP_ID=$(python3 -c "import json; c=json.load(open(os.path.expanduser('~/.claude/wechat-mp.json'))); print(c['app_id'])" 2>/dev/null || \
python3 -c "import json,os; c=json.load(open(os.path.expanduser('~/.claude/wechat-mp.json'))); print(c['app_id'])")
APP_SECRET=$(python3 -c "import json,os; c=json.load(open(os.path.expanduser('~/.claude/wechat-mp.json'))); print(c['app_secret'])")
TOKEN=$(curl -s "https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=${APP_ID}&secret=${APP_SECRET}" | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")
echo "Token OK: ${TOKEN:0:20}..."
Step 2 — 拉取飞书文档(Markdown 格式)
DOC_URL="https://xxx.feishu.cn/wiki/..."
lark-cli docs +fetch --api-version v2 --doc "$DOC_URL" --doc-format markdown > /tmp/lark_doc.json
⚠️ 飞书图片 URL 有时效性,必须立即进行 Step 3,不可搁置。
Step 3 — 下载飞书图片 & 上传到微信 CDN
对文档中每个  图片:
mkdir -p /tmp/wx_imgs
# 下载
curl -L -o /tmp/wx_imgs/imgN.png "FEISHU_IMAGE_URL" -s
# 上传到微信(返回 wechat_cdn_url)
curl -s -X POST "https://api.weixin.qq.com/cgi-bin/media/uploadimg?access_token=${TOKEN}" \
-F "media=@/tmp/wx_imgs/imgN.png;type=image/png"
# → {"url":"http://mmbiz.qpic.cn/..."}
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.
- 12d ago First seen · 189 lines · 75 tokens per session scan E dc255c7ca97e
lark-to-wechat-mp is a skill published in the GitHub repository CookiesHaha/ash-claude-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 2,010 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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