🦞 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 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

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. 🦞

lunaclaw-brief

Installation