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Ai Product Monitor

该 Skill 提供一条四阶段流水线:抓取配置好的 RSS 源中的 AI 产品发布信息,用网页搜索补充产品细节,抓取产品落地页截图,并生成趋势分析报告。可整条运行,也可按 --stage 参数单独运行某一阶段,各阶段通过 data 目录下的 JSON 文件串联。

terrycarter1985
terrycarter1985
热度 30.14
最近更新 2026-05-11 17:47:44
分类:数据依赖:代码执行 / 联网浏览搜索 / 第三方 API应用场景:研究 / 市场分析 / 报告撰写 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add ai-product-monitor
Metadata
name
ai-product-monitor
description
One-click AI product launch monitoring pipeline — RSS monitoring, product info search, screenshot capture, and trend analysis. Run the full workflow or individual stages.
version
1.0.0
author
openclaw
tags
["rss","ai","product-launch","monitoring","screenshot","trend-analysis","pipeline"]
requires_bins
["python3"]
requires_env
[]
requires_config
[]

AI Product Monitor

One-click pipeline that runs four stages sequentially:

  1. RSS Monitor — Fetch configured RSS feeds, extract AI product launch posts
  2. Product Search — Enrich each launch with web search details
  3. Screenshot Capture — Take screenshots of product landing pages
  4. Trend Analysis — Analyze launch trends and generate a summary report

Quick Start (Full Pipeline)

python3 scripts/pipeline.py

Individual Stages

# Stage 1 only — RSS monitoring
python3 scripts/pipeline.py --stage rss

# Stage 2 only — Product info search
python3 scripts/pipeline.py --stage search

# Stage 3 only — Screenshot capture
python3 scripts/pipeline.py --stage screenshot

# Stage 4 only — Trend analysis
python3 scripts/pipeline.py --stage trends

Configuration

Edit references/feeds.yaml to customize RSS sources. Default feeds include OpenAI, Google AI, Anthropic, Meta AI, and Hugging Face blogs.

Output

File Description
data/raw_launches.json Raw RSS items from stage 1
data/enriched_launches.json Launches with search-enriched info
data/screenshots/ Landing page screenshots (PNG)
data/trend_report.md Final trend analysis report

Pipeline Flow

RSS Feeds → raw_launches.json → enriched_launches.json → screenshots/ → trend_report.md

Each stage reads the previous stage's output, so stages can be re-run independently after editing earlier results.