| name | ai-news |
| description | Aggregate and analyze AI news from 7 authoritative sources including expert newsletters (Andrew Ng's The Batch), research papers (HuggingFace), industry news (TechCrunch, AI News), and community discussions (Reddit, Hacker News). Provides deep trend analysis with expert sentiment and community opinions. This skill should be used when the user wants a comprehensive AI news digest, research recent developments, understand community sentiment, or stay updated on AI trends. Invoke with `/ai-news <days>` (e.g., `/ai-news 3` for past 3 days).
|
AI News Aggregator
This skill aggregates AI news from 7 authoritative sources and produces a comprehensive, deeply-analyzed report.
It uses a multi-agent workflow for parallel fetching, verification, sentiment analysis, and expert-informed reporting.
Usage
/ai-news <days>
Arguments:
days (optional, default: 7) - Number of days to look back from today
Examples:
/ai-news 3 - Get AI news from the past 3 days/ai-news 7 - Get AI news from the past week/ai-news - Same as /ai-news 7
News Sources (7 Total)
Expert & Newsletter Sources
Research Sources
Industry News
Community Sources
| Source | Type | URL | Value |
|---|
| Reddit ML | Community Discussion | r/MachineLearning, r/LocalLLaMA | Sentiment, hot takes |
| Hacker News | Dev Discussion | https://news.ycombinator.com/ | Technical discourse |
Multi-Agent Workflow
Execute this workflow in order:
Phase 1: Planning (Main Orchestrator)
- Parse the
<days> argument (default to 7 if not provided)
- Calculate the date range:
[today - days, today]
- Prepare to spawn 7 parallel executor agents
Phase 2: Parallel Execution
Spawn agents in parallel using Bash tool, each running one fetcher script:
uv run python .claude/skills/ai-news/scripts/fetch_smol_news.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_hf_papers.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_hn_ai.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_ai_news.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_techcrunch.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_the_batch.py <days>
uv run python .claude/skills/ai-news/scripts/fetch_reddit_ml.py <days> --min-score 20
Key Outputs:
- Each script returns JSON with items, metadata, and source info
- Reddit script includes
community_sentiment with hot topics and engagement stats
- The Batch includes expert attribution
Phase 3: Verification & Deduplication
After collecting results from all sources:
- Date Range Validation: Confirm all items fall within
[start_date, end_date]
- Deduplication: Remove duplicate stories across sources
- Match by URL or title similarity (>80% match)
- Keep the version with most metadata
- Quality Filter: Remove low-quality or off-topic items
Phase 4: Deep Analysis & Sentiment Extraction
This is the critical phase for producing a valuable report. Perform these analyses:
4.1 Theme Clustering
Group all items into major themes:
- Research & Models: New architectures, benchmarks, capabilities
- Industry & Business: Funding, acquisitions, enterprise adoption
- Tools & Infrastructure: Developer tools, APIs, frameworks
- Policy & Safety: Regulation, alignment, ethics
- Applications: Real-world deployments, use cases
4.2 Trend Identification
For each major theme, analyze:
- What's the narrative arc? (emerging, maturing, declining)
- How many sources cover this topic?
- What's the engagement level (scores, comments)?
4.3 Expert Sentiment Extraction
From The Batch (Andrew Ng) articles:
- Extract key opinions and predictions
- Note any warnings or concerns raised
- Identify recommended actions or takeaways
4.4 Community Sentiment Analysis
From Reddit and Hacker News:
- What are the hot topics people are excited about?
- What criticisms or concerns are being raised?
- What's the overall mood (optimistic, skeptical, concerned)?
- Use the
community_sentiment data from Reddit fetch
4.5 Cross-Source Correlation
Identify stories that appear across multiple sources:
- Research paper on HuggingFace + discussed on Reddit
- Industry news on TechCrunch + expert analysis in The Batch
- These cross-source items are often the most significant
Phase 5: Report Generation
Generate a comprehensive, detailed report with these sections:
# AI News Report: [Start Date] to [End Date]
## Executive Summary
[3-4 paragraphs providing a narrative overview of the most important developments.
Start with the single biggest story, then cover 2-3 other major themes.
End with a forward-looking statement about what to watch.]
---
## Top Stories This Period
### 1. [Most Important Story Title]
**Sources:** [list sources covering this]
**Why It Matters:** [2-3 sentences on significance]
**Expert Take:** [Quote or paraphrase from The Batch if available]
**Community Reaction:** [Sentiment from Reddit/HN if available]
[Link to primary source]
### 2. [Second Most Important Story]
[Same structure...]
### 3. [Third Most Important Story]
[Same structure...]
---
## Trend Deep Dives
### Trend 1: [Trend Name]
**What's Happening:** [Detailed explanation of the trend]
**Key Evidence:**
- [Paper/Article 1 with link]
- [Paper/Article 2 with link]
- [Paper/Article 3 with link]
**Expert Analysis:** [What experts are saying - from The Batch, etc.]
**Community Sentiment:** [What Reddit/HN thinks]
- Hot takes: [Notable comments or discussions]
- Concerns raised: [Any skepticism or criticism]
**What This Means:** [Implications for practitioners, businesses, researchers]
**What to Watch:** [Future developments to monitor]
### Trend 2: [Trend Name]
[Same detailed structure...]
### Trend 3: [Trend Name]
[Same detailed structure...]
---
## Research Highlights
### Papers of the Week
[For each top paper from HuggingFace:]
#### [Paper Title]
- **Link:** [arxiv/HF link]
- **TL;DR:** [1-2 sentence summary]
- **Why Notable:** [What makes this significant]
[engagement metric]
[Repeat for top 5-10 papers]
[Group papers by theme with brief analysis]
---
[List with brief analysis of what it signals]
[Notable AI product launches with impact assessment]
[Companies adopting AI, partnerships, deployments]
[Any regulatory news or policy developments]
---
[Title] - [score] points, [comments] comments
Key debate: [what people are arguing about]
[Title] - [score] points, [comments] comments
Key insight: [notable comment or consensus]
Overall mood: [optimistic/skeptical/mixed]
Hot topics: [list from sentiment analysis]
Emerging interests: [what's gaining traction]
[Notable AI discussions with key points]
---
[Summarize main points from The Batch articles]
[Direct quotes or paraphrased expert opinion]
[Any actionable advice from expert sources]
---
[Implications and opportunities]
[What to learn, tools to try, skills to develop]
[Strategic implications, investment signals]
[Where things are heading, big picture trends]
---
[Complete chronological list with:
Date
Title (linked)
Source
Brief description if available]
---
[Start] to [End]
[count]
[list of 7 sources]
[timestamp]
Phase 5.1: Persist Report
After generating the report markdown, save it to disk:
cat <<'EOF' | uv run python .claude/skills/ai-news/scripts/write_report.py \
--start-date YYYY-MM-DD \
--end-date YYYY-MM-DD \
--days N \
--sources-ok source1,source2 \
--sources-failed source3 \
--total-items COUNT
<REPORT MARKDOWN HERE>
EOF
The script will:
- Write the report to
reports/ai-news_START_to_END_TIMESTAMP.md
- Update
reports/manifest.jsonl
- Copy to
reports/latest.md
- Return JSON with filepath and metadata
Verify the JSON response includes filepath (and other expected fields) after the command runs.
Important: Always run this after displaying the report to the user.
Phase 5.2: Render HTML
After saving the markdown, generate a self-contained HTML version alongside it:
uv run python .claude/skills/ai-news/scripts/render_html.py /path/to/report.md
The script writes /path/to/report.html (same basename) and prints the HTML filepath to stdout. Use the
filepath returned from Phase 5.1 as the input path.
Phase 5.3: Upload to Cloudflare Archive (Optional)
If the ADMIN_API_SECRET environment variable is set, upload the HTML report to the Cloudflare archive:
ADMIN_API_SECRET=$ADMIN_API_SECRET uv run python .claude/skills/ai-news/scripts/upload_to_cloudflare.py \
/path/to/report.html \
--start-date YYYY-MM-DD \
--end-date YYYY-MM-DD \
--days N \
--total-items COUNT
The script uploads the HTML to Cloudflare R2 and updates the KV index. The report will be immediately available at:
Note: This step is optional and only runs if ADMIN_API_SECRET is available in the environment.
Scripts Reference
All scripts are in .claude/skills/ai-news/scripts/ directory:
| Script | Source | API/Method | Special Features |
|---|
fetch_smol_news.py | smol.ai | RSS feed | Curated summaries |
fetch_hf_papers.py | HuggingFace | Date-based URL | Upvote counts |
fetch_hn_ai.py | Hacker News | Algolia API | AI keyword filtering |
fetch_ai_news.py | AI News | HTML scraping | Enterprise focus |
fetch_techcrunch.py | TechCrunch | RSS feed | Startup/funding focus |
fetch_the_batch.py | The Batch | HTML parsing | Expert analysis |
fetch_reddit_ml.py | Reddit | JSON API | Sentiment analysis |
render_html.py | Markdown | python-markdown | Self-contained HTML output |
upload_to_cloudflare.py | Cloudflare | Worker API | Upload to R2 + KV archive |
Error Handling
- If a source fails, continue with available sources
- Report which sources succeeded/failed in the output
- Minimum viable report requires at least 2 sources
Quality Guidelines
Report Length
- Executive Summary: 300-500 words
- Each Trend Deep Dive: 400-600 words
- Total report: 2000-4000 words depending on activity level
Analysis Depth
- Don't just list items - explain significance
- Connect dots across sources
- Provide actionable insights
- Include both optimistic and critical perspectives
Linking
- Every claim should link to a source
- Use markdown hyperlinks consistently
- Include both discussion links and original sources
Architecture Reference
See references/ARCHITECTURE.md for detailed workflow diagrams and technical specifications.
