Ai News Digest

Step 3: Ask Category Filter

Use AskUserQuestion with multiSelect to let the user filter by source category.

AskUserQuestion:
  questions:
    - question: "어떤 소스의 뉴스를 볼까요?"
      header: "카테고리"
      multiSelect: true
      options:
        - label: "전체"
          description: "모든 소스에서 가져오기 (Recommended)"
        - label: "AI 도구/에이전트"
          description: "Claude Code, Copilot, LangChain, Vercel AI SDK 등 실무 도구"
        - label: "공식 블로그"
          description: "Anthropic, OpenAI, DeepMind 등 공식 발표"
        - label: "연구 논문"
          description: "ArXiv ML/AI/CL 최신 논문"

Map selection to --category argument:

• "전체" selected → --category all (ignore other selections)
• "AI 도구/에이전트" → ai_tools
• "공식 블로그" → official
• "연구 논문" → research
• "커뮤니티/뉴스" → community,tech_news
• Multiple selections → comma-join (e.g., official,ai_tools)

Step 4: Check Dependencies & Fetch Data

4-1. Check Python dependencies:

python3 -c "import feedparser, yaml, certifi" 2>/dev/null || \
  pip3 install feedparser pyyaml certifi --quiet

4-2. Inform user about fetching:

"AI 뉴스를 가져오는 중입니다... (약 5-10초 소요됩니다)"

Note: The plugin now uses:

• Parallel fetching (much faster than before)
• Caching (subsequent runs within 30 min are instant)
• Real-time progress updates showing each feed as it completes

4-3. Locate and run the fetch script:

First, find the plugin directory. Check these paths in order:

1. ~/.claude/skills/ai-news-digest/../../config/fetch_news.py (installed via symlink)
2. plugins/ai-news-digest/config/fetch_news.py (local development)

python3 {path_to_fetch_news.py} --days {days} --top 10 --category {category} --output json

The script will show real-time progress like:

Fetching from 17 RSS feeds (parallel)...
[1/17] OpenAI News... ✓ (12 articles)
[2/17] DeepMind Blog... ✓ (8 articles)
...
Total articles found: 145

Error handling:

• If script not found: inform user of the path issue
• If script fails: show error and suggest checking internet connection
• If no results: suggest expanding the time range

Step 4.5: Analyze and Show Trending Topics (Optional)

After fetching data successfully, you can optionally show trending topics:

from config.trend_analyzer import TrendAnalyzer

analyzer = TrendAnalyzer()
trends = analyzer.analyze_trends(all_entries, top_n=5)

if trends:
    print("\n## 이번 주 AI 뉴스 트렌드:")
    for trend in trends:
        print(f"- **{trend['term']}** ({trend['count']}개 기사)")
        if trend['example_articles']:
            example = trend['example_articles'][0]
            print(f"  예: {example['title']} ({example['source']})")

Example output:

## 이번 주 AI 뉴스 트렌드:
- **AI agent** (8개 기사)
  예: New autonomous agents from OpenAI (OpenAI News)
- **Claude 4** (5개 기사)
  예: Claude 4 benchmarks released (Anthropic Engineering)
- **RAG** (4개 기사)
  예: Improving RAG with vector databases (LangChain Blog)

Step 5: Display Results in Terminal

Parse the JSON output and display results directly in the terminal. DO NOT save to a file yet.

Format each entry as:

**IMPORTANT**: Run `date '+%Y-%m-%d %H:%M'` to get the exact current date/time. Never estimate.

## AI News Top 10 — {today's date} (최근 {period})

---

**1. {Title}** (Score: {score})
{Source} | {Published date}
{Summary (first 200 chars)}
Link: {url}

---

**2. {Title}** (Score: {score})
{Source} | {Published date}
{Summary (first 200 chars)}
Link: {url}

---

... (up to 10 items)

Display rules:

• Number each item clearly (1-10)
• Show score for reference
• Keep summary concise (max ~200 chars)
• Include the link for each item
• If fewer than 10 results, show all available

After displaying all results, proceed to Step 6.

Step 6: Ask What to Save

After displaying the list, ask the user in plain text:

Output this exact message:

"저장할 뉴스가 있다면 번호로 알려주세요. (예: 1, 3, 7)" "없으면 '없음'이라고 해주세요."

Wait for user response. The user may respond in various formats:

• "2, 5번 저장해줘" → save items 2 and 5
• "1,3,7" → save items 1, 3, 7
• "전부 저장" or "all" → save all items
• "없음" or "없어" or "no" → skip saving, go to Step 7 with no-save path

Parse the numbers from the response. Extract all digits that correspond to displayed item numbers.

Step 7: Save Selected Items (or Skip)

If user said "없음" / no save:

• Output: "알겠습니다. 저장 없이 마무리합니다. 다음에 또 AI 뉴스가 필요하면 말씀해주세요!"
• End execution.

If user selected specific items:

7-1. Determine save location:

• Read ~/.claude/skills/learning-summary/config.yaml
• If learning_repo is configured: save to {learning_repo}/digests/ai-news-digest-YYYY-MM-DD.md
• Otherwise: save to current directory as ./ai-news-digest-YYYY-MM-DD.md

7-2. Generate markdown for selected items only:

# AI News Digest - YYYY-MM-DD

> **Generated**: YYYY-MM-DD HH:MM
> **Period**: Last {N} days
> **Categories**: {selected categories}
> **Saved items**: {count} of {total}

---

## 1. {Title}

**Source**: {Source} | **Published**: YYYY-MM-DD | **Score**: {Score}

{Full summary}

**Key Points**:
- {Extracted key point 1}
- {Extracted key point 2}

**Why It Matters**: {Brief significance analysis}

**Read More**: {Link}

---

## 2. {Title}
...

7-3. Save using Write tool.

7-4. Confirm to user:

Saved {N}개 뉴스를 저장했습니다: {file_path}

저장된 항목:
- 1. {Title}
- 2. {Title}

다음에 또 AI 뉴스가 필요하면 말씀해주세요!

Trigger Phrases

English:

• "latest AI news"
• "AI news digest"
• "what's new in AI"
• "fetch AI news"
• "get latest AI developments"

Korean:

• "최신 AI 뉴스"
• "AI 뉴스 정리"
• "AI 소식"
• "AI 업데이트"

Configuration

RSS Feed Sources (config/feeds.yaml)

Official Blogs (weight: 9-10):

• OpenAI, DeepMind, Anthropic (community feed)

Research Papers (weight: 8):

• ArXiv (ML, AI, CL)

Community (weight: 6):

• Hacker News, Reddit (MachineLearning, LocalLLaMA)

Tech News (weight: 5):

• The Verge, TechCrunch

Scoring System

Final Score = Base Weight + Keyword Boost + Recency Boost

User Preferences (config/user_preferences.yaml - Optional)

Power users can create a user_preferences.yaml file to customize defaults:

default_time_range: 7  # Skip time range question
default_categories: "all"  # Skip category question
default_top_n: 10

favorite_sources:  # Get +2 weight boost
  - "OpenAI News"
  - "Anthropic Engineering (Community)"

excluded_sources: []  # Skip these feeds entirely