BeClaude

feishu-bot-dev

New
16Community RegistryGeneralby yrzhe · MIT

飞书机器人配置与开发完整指南。支持零代码(机器人助手)和全代码(开放平台API)两种模式,包含发消息、创建文档、管理群组等功能。

First seen 5/22/2026

Summary

This skill provides a comprehensive guide for developing Feishu (Lark) bots, supporting both no-code (bot assistant) and full-code (open platform API) modes.

  • It covers sending messages, creating documents, managing groups, and more, enabling developers to quickly integrate Feishu capabilities into their workflows.

Overview

A collection of powerful Claude Code skills and plugins by @yrzhe.

Installation

Option 1: Via Plugin Marketplace

Add this marketplace to your Claude Code:

bash
/plugin marketplace add yrzhe/claude-skills

Then install any plugin you want:

bash
/plugin install intelligent-web-scraper@yrzhe-skills

Option 2: Manual Installation

If the plugin command doesn't work, you can manually copy the skill files:

  1. Clone or download this repository
  2. Copy the skill folder to your Claude skills directory:

macOS / Linux:

bash
cp -r plugins/intelligent-web-scraper/skills/intelligent-web-scraper ~/.claude/skills/

Windows (PowerShell):

powershell
Copy-Item -Recurse plugins\intelligent-web-scraper\skills\intelligent-web-scraper $env:USERPROFILE\.claude\skills\
  1. Restart Claude Code to load the new skill

Available Plugins

intelligent-web-scraper

Self-learning intelligent web scraper agent that automatically analyzes page structure, handles pagination, anti-blocking, and discovers article series. No user configuration needed - AI decides everything.

Features:

  • Intelligent page analysis and data extraction
  • Smart pagination handling (page numbers, infinite scroll, load more)
  • Detail link following for complete data
  • Anti-blocking with adaptive delays
  • Series/chapter discovery
  • Self-learning system that remembers successful patterns
  • Resume capability for interrupted scrapes
  • Concurrent scraping with rate limiting
  • Local browser support (preserve login sessions)

Usage:

code
/intelligent-web-scraper

Then provide a URL to scrape and let the AI handle everything.

lenny-advisor

Product and business diagnostic advisor powered by distilled wisdom from 289 Lenny's Podcast guests and 348 newsletter articles. Not a knowledge dump — an active advisor that diagnoses your real problem before delivering expert frameworks.

Features:

  • Diagnose → Probe → Deliver methodology (asks before answering)
  • 18 topic areas: growth, pricing, PMF, positioning, hiring, leadership, metrics, fundraising, marketplace, AI strategy, and more
  • 40 deep expert profiles (Shreyas Doshi, Elena Verna, April Dunford, Rahul Vohra, etc.)
  • 3-layer progressive loading (minimal token usage)
  • Companion workflow with dbskill and gstack

Install:

bash
/plugin install lenny-advisor@yrzhe-skills

Manual install:

bash
cp -r plugins/lenny-advisor/skills/lenny-advisor ~/.claude/skills/

The skill activates automatically when you discuss product decisions, business strategy, growth, pricing, or any product/business topic.

persona-sim

Simulate feedback from census-grounded virtual populations. Panel-score your product / copy / pricing, predict votes with IPF post-stratification, or run what-if social-sandbox experiments — before paying for real user research. Backed by NVIDIA Nemotron-Personas (1M US Census-aligned synthetic people) and methodology from Park et al. 2024.

Features:

  • 4 skills in one plugin: persona-sim (core engine) + product-feedback-sim (SGO A/B ranking) + vote-predict (policy/voting with IPF) + social-sandbox (what-if experiments)
  • SGO (Semantic Gradient Optimization) with anchored counterfactuals — causal attribution, not independent re-scoring
  • Persuadable-middle identification (score 4-7 only) to avoid wasting LLM calls on extremes
  • IPF post-stratification to reweight panels to real-population marginals (PUMS-ready)
  • Bias audit with 4 probe types (framing, acquiescence, order, authority) — flags when the simulation under-represents human biases
  • 6-suite eval score card (GSS attitudes, Big Five norms, test-retest, diversity, demo-correlation, bias-audit)
  • HuggingFace streaming — no local dataset download required for MVP
  • Multi-provider LLM routing: native Anthropic, Anthropic-compatible gateways, OpenAI-compatible gateways

Install:

bash
/plugin install persona-sim@yrzhe-skills

Manual install:

bash
cp -r plugins/persona-sim/skills/persona-sim ~/.claude/skills/
cp -r plugins/persona-sim/skills/product-feedback-sim ~/.claude/skills/
cp -r plugins/persona-sim/skills/vote-predict ~/.claude/skills/
cp -r plugins/persona-sim/skills/social-sandbox ~/.claude/skills/

First-time setup: see plugins/persona-sim/skills/persona-sim/SETUP.md — you need to create ~/.claude/data/personas/config.json (from the provided config.example.json) with your LLM API key, and a venv for Python dependencies.

Usage examples:

  • "Score this landing page copy with 30 software developers" → auto-triggers product-feedback-sim
  • "Predict US support for a $22 minimum wage, by age and education" → auto-triggers vote-predict
  • "If AI copilots got regulated tomorrow, what would developers do?" → auto-triggers social-sandbox
  • Direct Python use: from persona_sim import sampler, sim_engine

design-distiller

Scrape any website's design system into a structured Design MD + decomposed design tokens. Ships with 55 pre-analyzed brand references (Vercel, Stripe, Linear, Notion, Claude, Figma, Airbnb, Spotify, and more) and an 8-dimension Digest Pool for mix-and-match composition.

Features:

  • 4-phase pipeline: Scrape → Analyze → Generate → Digest
  • Multi-tier browser support: Browser Use Cloud / Playwright / Chrome Headless / WebFetch fallback
  • 9-module Design MD format following awesome-design-md standard
  • 55 pre-loaded brand references with full design system documentation
  • 8-dimension Digest Pool (typography, colors, spacing, components, depth, motion, layouts, philosophy)
  • Compose command: mix-and-match from Digest Pool to generate new design systems
  • Machine-friendly cross-reference tags for programmatic matching
  • Confidence tagging: High (CSS var) / Medium (computed) / Low (visual estimate)

Install:

bash
/plugin install design-distiller@yrzhe-skills

Manual install:

bash
cp -r plugins/design-distiller/skills/design-distiller ~/.claude/skills/

Usage:

code
/design-distiller https://vercel.com              # Full pipeline
/design-distiller compose "Vercel typography + Stripe colors + Linear components"
/design-distiller compare vercel stripe            # Side-by-side comparison
/design-distiller list                             # List all 55 references

seed

Build-in-public activity recorder. A Stop hook mechanically logs every Claude Code turn (user prompt + tools used + full assistant output) to a per-session markdown file. When you're ready to tweet, /seed reads the log and helps you synthesize draft tweets from real evidence — no more "wait, what did I actually do today?"

Features:

  • Zero-cost Stop hook — mechanical turn capture, no LLM calls, no scoring, zero latency
  • De-duped by user-prompt uuid (Stop hook fires per-turn, so dedup matters)
  • Per-session markdown logs with full assistant output + tool summaries
  • /seed shot — screenshot capture bound to the current session (interactive window-pick OR headless Chrome URL mode)
  • /seed — Claude reads the log and proposes tweet drafts tied to real evidence (skips routine sessions)

Install:

bash
/plugin install seed@yrzhe-skills

Manual install:

bash
cp -r plugins/seed/skills/seed ~/.claude/skills/seed
chmod +x ~/.claude/skills/seed/scripts/*.py

Hook setup (one-time, see plugins/seed/skills/seed/README.md for full instructions):

bash
cp ~/.claude/skills/seed/hooks/capture-session-seed.sh ~/.claude/hooks/
chmod +x ~/.claude/hooks/capture-session-seed.sh

Then add to ~/.claude/settings.json:

json
{ "hooks": { "Stop": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "~/.claude/hooks/capture-session-seed.sh" }] }] } }

Usage:

code
/seed                                # synthesize current session → tweet drafts
/seed shot                           # interactive window pick
/seed shot localhost:3000            # headless Chrome screenshot
/seed list                           # list all session logs
/seed done                           # archive current session

chef

Cooking assistant for Chinese and Western cuisine — recipes are pulled from real sources, never fabricated. LLMs have no taste buds; specific quantities, timings, and heat levels must come from real recipe data or be fetched live. Ships with 604 pre-fetched recipes as evidence.

Sources (all real): xiachufang, douguo, meishij (下厨房/豆果美食/美食杰 — Chinese), allrecipes, BBC Good Food, Food Network, TheMealDB, Serious Eats, Epicurious, and 10+ others. Zero fabrication — if a source doesn't have it, the agent says "let me fetch it" before answering.

Features:

  • Hard rule: no data = no answer. Every quantity/technique traceable to a source URL
  • Chinese cuisines: 川/粤/鲁/苏/闽/浙/湘/徽/家常
  • Western cuisines: 意/法/美式/地中海/英伦 + categories (mains/baking/soups/salads/breakfast)
  • Recipe walkthroughs with ingredients (name + gram weight), steps (timing/heat/key moments), doneness cues, common pitfalls
  • Ingredient-based suggestions (prioritize recipes covering ≥2 of what you have)
  • Pairing analysis via local co-occurrence + theory (not vibes)
  • Nutrition lookup (Open Food Facts) + substitution advice
  • 604 pre-cached recipes in data/recipes/*.md — search before fetch

Install:

bash
/plugin install chef@yrzhe-skills

Manual install:

bash
cp -r plugins/chef/skills/chef ~/.claude/skills/chef

Usage:

code
"怎么做西湖醋鱼"            → local search → walkthrough with source URL
"家里有鸡蛋西红柿土豆能做啥"  → ingredient-match suggestions
"X 和 Y 搭不搭"            → co-occurrence + pairing theory
"how do I make carbonara"   → allrecipes/BBC Good Food lookup
"这菜多少卡路里"            → nutrition from frontmatter or OpenFoodFacts

Contributing

Feel free to open issues or submit pull requests to improve these skills.

License

MIT License - see individual plugins for details.

Author

yrzhe - @yrzhe_top

Install & Usage

1
Add a marketplace
/plugin marketplace add <org/repo>
2
Install the plugin

Add the configuration to /plugin install feishu-bot-dev@<marketplace>

3
Manage with /plugin
/plugin

Use Cases

Configure a Feishu bot to automatically send notifications from a CI/CD pipeline.
Build a custom bot that creates and updates Feishu documents based on user commands.
Manage group members and send messages programmatically using the Feishu Open API.
Set up a no-code bot assistant to handle common queries without writing any code.
Integrate Feishu bot with external services to fetch and display data in chat.
Automate repetitive tasks like event reminders or approval workflows via Feishu bot.

Usage Examples

1

/feishu-bot-dev send message to group 'team' with content 'Deployment complete'

2

/feishu-bot-dev create document titled 'Meeting Notes' with content '# Agenda - Review progress'

3

/feishu-bot-dev list members of group 'engineering'

View source on GitHub
apifeishularkbot飞书机器人消息群组

Security Audits

LicensePassSourceWarnRepositoryPass

Frequently Asked Questions

What is feishu-bot-dev?

This skill provides a comprehensive guide for developing Feishu (Lark) bots, supporting both no-code (bot assistant) and full-code (open platform API) modes. It covers sending messages, creating documents, managing groups, and more, enabling developers to quickly integrate Feishu capabilities into their workflows.

How to install feishu-bot-dev?

To install feishu-bot-dev: add a marketplace (/plugin marketplace add <org/repo>), then add the config to /plugin install feishu-bot-dev@<marketplace>. Finally, /plugin in Claude Code.

What is feishu-bot-dev best for?

feishu-bot-dev is a plugin categorized under General. It is designed for: api, feishu, lark, bot, 飞书, 机器人, 消息, 群组. Created by yrzhe.

What can I use feishu-bot-dev for?

feishu-bot-dev is useful for: Configure a Feishu bot to automatically send notifications from a CI/CD pipeline.; Build a custom bot that creates and updates Feishu documents based on user commands.; Manage group members and send messages programmatically using the Feishu Open API.; Set up a no-code bot assistant to handle common queries without writing any code.; Integrate Feishu bot with external services to fetch and display data in chat.; Automate repetitive tasks like event reminders or approval workflows via Feishu bot..