One-command AI product launch monitoring pipeline. Use when the user wants to track, discover, or analyze new AI product launches, releases, or announcements. Runs four stages automatically - RSS feed monitoring, web search enrichment, page screenshots, and trend analysis/scoring. Outputs a markdown report and structured JSON. Triggers on phrases like monitor AI product launches, track AI releases, AI launch report, what new AI products launched, product hunt AI monitoring, AI news roundup.
Memory
topic-monitor
Try itContinuously monitor topics across web search, RSS/Atom feeds, and GitHub releases, alerting only when relevant.
What it does
Combine web search queries, RSS/Atom feeds, and GitHub release feeds in one pipeline to monitor a topic. Each topic supports boolean filters via required_keywords and exclude_keywords, configurable frequency (hourly/daily/weekly), an importance threshold (low/medium/high), and sentiment labels (positive/negative/neutral/mixed) attached to every finding. Alerts include the source label, importance score, and a sentiment shift marker when the result's sentiment differs from stored history. Findings can be alerted immediately or saved for a digest, and feed lists can be imported from OPML.
When to use it
- Tracking product releases and announcements on vendor blogs
- Monitoring GitHub releases for specific repositories (owner/repo)
- Following security advisories via RSS feeds
- Reading weekly digests of news on a specific topic
The skill document
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_keywordsandexclude_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:
- gather results
- deduplicate
- apply advanced filters
- score importance
- classify sentiment
- alert immediately or save for digest
- track state and sentiment history
Topic config
Each topic supports these keys:
idnamequerykeywordsfeedsgithub_reposrequired_keywordsexclude_keywordsfrequency→hourly|daily|weeklyimportance_threshold→high|medium|lowchannelscontextalert_onalert_on_sentiment_shiftignore_sourcesboost_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:
etaglast-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.atomhttps://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:
positivenegativeneutralmixed
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_sentimentsentiment_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-feedagainst 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.
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