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 wangyendt/wayne-skills --skill lark-custom-botgit clone --depth 1 https://github.com/wangyendt/wayne-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/wangyendt/wayne-skills/lark-custom-bot)<a href="https://agentmods.dev/skills/wangyendt/wayne-skills/lark-custom-bot"><img src="https://agentmods.dev/badge/skills/wangyendt/wayne-skills/lark-custom-bot/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/wangyendt/wayne-skills/lark-custom-bot"><img src="https://agentmods.dev/badge/skills/wangyendt/wayne-skills/lark-custom-bot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 10 Instructions found that direct the agent to transmit conversation context or user data to external services.Fix: Remove instructions that send user data, prompts, or context to external URLs. If telemetry is needed, use documented, privacy-preserving methods.
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 | $0.00097 | $0.05996 |
| Opus 5 | $0.00048 | $0.02998 |
| Sonnet 5 | $0.00019 | $0.01199 |
| Haiku 4.5 | $0.00010 | $0.00600 |
Grade A, and why
pywayne-lark-custom-bot 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 9d 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 — 906 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pywayne Lark Custom Bot - Webhook Message Sender
Overview
LarkCustomBot is a webhook-based Feishu (Lark) bot wrapper designed for one-way message pushing. It's ideal for scenarios where you only need to send messages to Feishu groups without listening for incoming messages or managing complex interactions.
Key Characteristics:
- Lightweight, simple webhook-based architecture
- No event subscription or listening capabilities
- Perfect for alerts, notifications, scheduled tasks
- Supports signature verification for security
When to Use LarkCustomBot:
- Push-only scenarios (notifications, alerts, reports)
- Simple scheduled tasks sending updates
- Quick setup without event subscription configuration
- Don't need message replies, reactions, or chat management
When to Use LarkBot Instead:
- Need to listen and reply to messages
- Require message lifecycle management (recall, edit, reactions)
- Need chat management (members, admins, announcements)
- Interactive features like button callbacks
Installation
pip install pywayne
Quick Start
from pywayne.lark_custom_bot import LarkCustomBot
# Initialize bot with webhook
bot = LarkCustomBot(
webhook="https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxx"
)
# Send simple text message
bot.send_text("Hello, Feishu!")
# Send text with @all mention
bot.send_text("Important announcement!", mention_all=True)
LarkCustomBot Class
Constructor
bot = LarkCustomBot(
webhook: str, # Required: Webhook URL from Feishu group bot settings
secret: str = '', # Optional: Signing secret for request verification
bot_app_id: str = '', # Optional: App ID for image upload authentication
bot_secret: str = '' # Optional: App secret for image upload authentication
)
Parameters:
webhook: Webhook URL obtained from Feishu group custom bot settingssecret: Signing secret for signature verification (enhances security)bot_app_id: Required forupload_image()andupload_image_from_cv2()bot_secret: Required forupload_image()andupload_image_from_cv2()
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.
- 9d ago First seen · 906 lines · 97 tokens per session scan A 9b9469ba2427
pywayne-lark-custom-bot is a skill published in the GitHub repository wangyendt/wayne-skills (8 stars, last pushed 13d ago), licensed MIT. It adds 97 tokens to every session and 5,996 once invoked, about $0.0005 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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