记忆

topic-monitor

试用

跨网页搜索、RSS/Atom 订阅和 GitHub 发布持续追踪主题,仅在出现相关内容时推送提醒。

它能做什么

把网页搜索查询、RSS/Atom 订阅源和 GitHub 发布订阅合并到同一管道中追踪某个主题。每个主题支持必现关键词与排除关键词的布尔过滤、可配置的运行频率(每小时/每天/每周)、重要度阈值(low/medium/high),并对每条结果打上 positive/negative/neutral/mixed 的情感标签。提醒会附带来源标识、重要度评分和情感变化标记;命中结果可立即推送,也可汇总到 digest。支持从 OPML 文件批量导入订阅列表。

什么时候用它

  • 追踪厂商博客上的产品发布与公告
  • 按 owner/repo 监控特定仓库的 GitHub 发布
  • 通过 RSS 订阅追踪安全公告
  • 以周报形式汇总某个话题的新闻动态

技能文档

Topic Monitor

Monitor topics continuously and alert only when something looks relevant.

What’s new in v1.5.0

  • RSS/Atom feed monitoring as a first-class source via feeds
  • GitHub release monitoring via github_repos → https://github.com/{owner}/{repo}/releases.atom
  • Feed auto-discovery from normal URLs
  • OPML import for feed lists
  • Advanced filters with required_keywords and exclude_keywords
  • Sentiment analysis on findings: positive, negative, neutral, mixed
  • Sentiment shift alerts with alert_on_sentiment_shift

Quick start

python3 scripts/quick.py "AI Model Releases"
python3 scripts/quick.py "OpenClaw Releases" --github-repos "openclaw/openclaw"
python3 scripts/quick.py "Security Advisories" --feeds "https://example.com/security.xml"

Then test it:

python3 scripts/monitor.py --dry-run --verbose

Core model

Each topic can mix multiple sources:

  • Web search via query
  • RSS/Atom feeds via feeds
  • GitHub releases via github_repos

All collected results flow into the same pipeline:

  1. gather results
  2. deduplicate
  3. apply advanced filters
  4. score importance
  5. classify sentiment
  6. alert immediately or save for digest
  7. track state and sentiment history

Topic config

Each topic supports these keys:

  • id
  • name
  • query
  • keywords
  • feeds
  • github_repos
  • required_keywords
  • exclude_keywords
  • frequency → hourly|daily|weekly
  • importance_threshold → high|medium|low
  • channels
  • context
  • alert_on
  • alert_on_sentiment_shift
  • ignore_sources
  • boost_sources

Example config

{
  "topics": [
    {
      "id": "openclaw-news",
      "name": "OpenClaw Updates",
      "query": "OpenClaw AI assistant update release",
      "keywords": ["OpenClaw", "release", "update"],
      "feeds": ["https://example.com/blog/rss.xml"],
      "github_repos": ["openclaw/openclaw"],
      "required_keywords": ["release"],
      "exclude_keywords": ["rumor", "affiliate"],
      "frequency": "daily",
      "importance_threshold": "medium",
      "channels": ["telegram"],
      "context": "Track product updates and releases",
      "alert_on": ["keyword_exact_match", "github_release"],
      "alert_on_sentiment_shift": true,
      "ignore_sources": [],
      "boost_sources": ["github.com"]
    }
  ]
}

Advanced filters

required_keywords

All listed terms must appear in the title/snippet before scoring.

"required_keywords": ["release", "stable"]

exclude_keywords

Any matching term filters the result out before scoring.

"exclude_keywords": ["beta", "rumor", "affiliate"]

This is intentionally simple boolean logic:

  • any exclude match → reject
  • any missing required term → reject

RSS/Atom feeds

Direct feeds

python3 scripts/manage_topics.py add "Security Feeds" \
  --feeds "https://example.com/rss.xml,https://example.com/atom.xml" \
  --keywords "security,CVE,patch"

Feed discovery

Discover feeds from a normal website URL:

python3 scripts/manage_topics.py discover-feed https://example.com/blog
python3 scripts/monitor.py --discover-feed https://example.com/blog

Add a topic and auto-discover feeds in one step:

python3 scripts/manage_topics.py add "Vendor Blog" \
  --discover-feeds "https://example.com/blog" \
  --keywords "release,announcement"

OPML import

Import feed subscriptions from an OPML file:

python3 scripts/manage_topics.py import-opml feeds.opml

Imported topics default to daily / medium unless you override:

python3 scripts/manage_topics.py import-opml feeds.opml --frequency hourly --importance high

Feed caching

Feed polling uses feedparser and stores per-feed cache data in monitor state:

  • etag
  • last-modified
  • last check metadata

That allows efficient conditional requests and avoids reprocessing unchanged feeds.

GitHub release monitoring

Track repo releases with:

"github_repos": ["openclaw/openclaw", "anthropics/claude-code"]

These map to GitHub Atom feeds automatically:

  • https://github.com/openclaw/openclaw/releases.atom
  • https://github.com/anthropics/claude-code/releases.atom

CLI example:

python3 scripts/manage_topics.py add "CLI Releases" \
  --github-repos "openclaw/openclaw,anthropics/claude-code" \
  --keywords "release,version"

GitHub release items are labeled clearly in alerts.

Sentiment analysis

Each scored finding also gets a sentiment label:

  • positive
  • negative
  • neutral
  • mixed

Alerts and digest entries include that sentiment.

Sentiment shift alerts

Enable:

"alert_on_sentiment_shift": true

When enabled, a result that changes sentiment versus the topic’s previous sentiment history gets promoted for alerting.

State tracks:

  • last_sentiment
  • sentiment_history

Scripts

scripts/manage_topics.py

# Add topic
python3 scripts/manage_topics.py add "Topic Name" \
  --query "search query" \
  --keywords "word1,word2" \
  --feeds "https://example.com/rss.xml" \
  --github-repos "openclaw/openclaw" \
  --required-keywords "release" \
  --exclude-keywords "beta,rumor"

# List topics
python3 scripts/manage_topics.py list

# Edit topic
python3 scripts/manage_topics.py edit topic-id --feeds "https://example.com/rss.xml"

# Discover feeds
python3 scripts/manage_topics.py discover-feed https://example.com/blog

# Import OPML
python3 scripts/manage_topics.py import-opml feeds.opml

# Test topic
python3 scripts/manage_topics.py test topic-id

scripts/monitor.py

python3 scripts/monitor.py
python3 scripts/monitor.py --dry-run
python3 scripts/monitor.py --topic openclaw-news --verbose
python3 scripts/monitor.py --discover-feed https://example.com/blog

Alert output

Alerts can now include:

  • source label (Web, Feed, GitHub Release)
  • score and reason
  • sentiment
  • sentiment shift marker when applicable

Installation note

Feed support uses Python feedparser.

Install if needed:

pip3 install feedparser

Troubleshooting

Feeds not showing results

  • verify the feed URL manually
  • try discover-feed against the site URL
  • install feedparser
  • run python3 scripts/monitor.py --dry-run --verbose

Too much noise

  • tighten required_keywords
  • add exclude_keywords
  • increase importance_threshold

Missing GitHub release alerts

  • confirm repo is owner/repo
  • verify releases exist on GitHub
  • test with --verbose

Notes

  • Search and feed results are merged into the same scoring pipeline.
  • Existing web-search behavior remains supported.
  • Digest entries store sentiment so weekly output reflects tone changes too.

相关技能

在本地磁盘以分类纯 Markdown 文件保存需要长期留存的事实,与智能体内置记忆并存。

作者 Iván559 次安装18 星标

以 AI 机器人身份加入视频会议,提供语音、虚拟形象与屏幕共享四种模式。

作者 johnpatternai22 次安装8 星标

把自然语言描述转为结构化 JSON,并由 mcp-diagram-generator MCP 服务生成 Draw.io、Mermaid 或 Excalidraw 图表文件。

作者 nssa.io1.0k 次安装47 星标

从 AdMapix API 拉取广告创意、应用、榜单和收入预估等数据,原样返回结构化 JSON。

作者 fly0pants4.3k 次安装296 星标

诊断生产力系统反复失效的根因,给出最小干预——容量测算、瓶颈定位、可靠的本地记录。

作者 Iván855 次安装69 星标

robbyczgw-cla 的更多技能

浏览全部技能

让 Agent 自动生成 400–600 字的第一人称反思日记,并跟踪金句、决策与心情趋势。

作者 robbyczgw-cla208 次安装6 星标

调用 ElevenLabs API,提供 18 种人声角色、32 种语言合成、音效生成与自定义人声设计。

作者 robbyczgw-cla275 次安装16 星标

三位专家子代理并行作答,再可选互相质询,最终输出一份带共识、异议与置信度的综合答案。

作者 robbyczgw-cla62 次安装2 星标

从云服务器环境获取 YouTube 视频转录文本,绕过 YouTube 的反爬检测。

作者 robbyczgw-cla151 次安装5 星标

在任意 AI 回复后,基于上下文生成三条追问建议:快速澄清、深入探索、相关话题。

作者 robbyczgw-cla192 次安装8 星标

随时切换到 20 个内置 AI 人格,覆盖编程、写作、健身、医法科普等场景。

作者 robbyczgw-cla282 次安装27 星标