🦞 LunaClaw Brief — OpenClaw Skill

Pluggable AI-Powered Report Engine | 插件化 AI 智能简报引擎

Overview / 概述

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

Data Sources / 数据源

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 jobs

Via 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

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. 🦞