🦞 LunaClaw Brief — OpenClaw Skill
Pluggable AI-Powered Report Engine | 插件化 AI 智能简报引擎
Overview / 概述
LunaClaw Brief is an OpenClaw Skill that generates intelligent reports across multiple domains. It features a plugin architecture with 8-stage pipeline, 8 data sources, 7 editor strategies, hybrid intent routing, streaming output, and multi-channel delivery.
LunaClaw Brief 是一个 OpenClaw Skill，支持多领域智能报告生成。采用插件化架构，8 阶段管线，8 个数据源，7 种编辑器策略，混合意图路由，流式输出，多渠道推送。
Presets / 预设
| Preset | Type | Description |
|---|---|---|
ai_cv_weekly | Tech | AI/CV/多模态技术深度周报 |
ai_daily | Tech | AI 技术日报 |
finance_weekly | Finance | 金融投资周报 |
finance_daily | Finance | 金融快报日刊 |
stock_a_daily | Stock | A 股日报（大盘/板块/北向资金/IPO/异动） |
stock_hk_daily | Stock | 港股日报（恒生/南向资金/中概股/IPO） |
stock_us_daily | Stock | 美股日报（S&P/NASDAQ/科技巨头/IPO） |
| Custom | Any | via --create-preset natural language |
Data Sources / 数据源
| Source | Coverage |
|---|---|
github | Open-source projects + competitor discovery |
arxiv | Academic papers |
hackernews | Tech community discussions |
paperswithcode | ML papers with code |
finnews | Financial news (HN + BBC/NYT RSS) |
yahoo_finance | Yahoo Finance RSS + market indices |
eastmoney | 东方财富 (A-share news, IPO calendar) |
xueqiu | 雪球 (hot stocks, sentiment) |
Usage / 使用方式
Via CLI
python run.py # AI/CV Weekly (default)
python run.py --preset stock_a_daily # A-share daily
python run.py --hint "今天A股怎么样" # Auto-route → stock_a_daily
python run.py --hint "帮我看看腾讯和阿里" # LLM auto-route
python run.py --preset ai_cv_weekly --email # Generate + email
python run.py --create-preset "我是新能源基金经理" # Create custom preset
python -m brief.scheduler # Run scheduled jobsVia OpenClaw Skill API
from run import generate_report
result = generate_report({
"preset": "stock_hk_daily",
"hint": "重点关注腾讯和美团",
"send_email": True,
})Trigger Phrases / 触发短语
- "生成周报" / "生成日报" / "generate weekly/daily"
- "生成金融周报/日报" / "generate finance report"
- "生成A股/港股/美股日报" / "generate A-share/HK/US brief"
Key Capabilities / 核心能力
- Hybrid Intent Router: Regex (instant) + LLM classification (fallback) for preset routing
- Unified Markdown Schema:
## Section→### N. Item→**Label**:→**🦞 Claw**:structure - Streaming Output:
pipeline.run_stream()yields progress events + content chunks - Multi-Channel Delivery: Email (SMTP), Webhook (Slack/DingTalk/Feishu/custom)
- Cron Scheduler: Auto-generate reports on schedule with multi-channel push
- Custom Presets: Create domain-specific presets via natural language
- Quality Control: Auto-check structure/word count, retry if below threshold
- Historical Dedup: Content fingerprinting with configurable time window
Architecture / 架构
| Pattern | Where |
|---|---|
| Adapter | BaseSource → 8 source adapters |
| Strategy | BaseEditor → 7 editor strategies |
| Pipeline | 8-stage ReportPipeline (sync + stream) |
| Registry | @register_source / @register_editor decorators |
| Observer | MiddlewareChain for timing, metrics, custom hooks |
| Factory | create_sources() / create_editor() |
| Cache | FileCache with TTL for API responses |
Configuration / 配置
Global config in config.yaml. Secrets in config.local.yaml (gitignored).
# config.local.yaml (not committed)
llm:
api_key: "your-key"Auto-detects OpenClaw environment (OPENCLAW_API_KEY / OPENCLAW_BASE_URL) — no manual LLM config needed when running as an OpenClaw skill.
Output / 输出
- HTML: Self-contained dark-theme report with embedded Luna logo
- Markdown: Raw LLM output
- PDF: Via WeasyPrint (optional)
- Email: HTML body + PDF attachment
- Webhook: Slack/DingTalk/Feishu/custom HTTP POST
*Built by llx & Luna 🐱 — where the claw meets the code.* 🦞