AI Digest

AI Digest

Analyzes AI/tech articles, blog posts, or content and creates structured learning documents optimized for quick reference and practical application. Output is blog-ready with YAML frontmatter for Astro Content Collections.

Execution Algorithm

Step 1: Parse Input

Extract from user message:

Examples:

• /ai-digest https://anthropic.com/news/... → URL extracted
• /ai-digest "Focus on breaking changes" https://... → URL + Focus
• /ai-digest "Analyze: [pasted content]" → Direct content

Step 2: Resolve Content

Determine the content source using the following priority:

Priority 1 — Direct content provided: If the user pasted article text (beyond just a URL), use it directly. Skip WebFetch entirely. This is the most reliable path.

Priority 2 — Fetchable URL only: If only a URL is provided (no pasted content), attempt WebFetch:

WebFetch(
    url=extracted_url,
    prompt="""
    Extract the main content of this article.

    Focus on:
    - Main topic and key points
    - Technical details and specifications
    - Code examples
    - Changes or new features
    - Practical applications

    Ignore navigation, ads, and footer content.
    """
)

Priority 3 — Auth-gated URL with no content: If the URL is from a known auth-gated domain AND no direct content is provided, do NOT attempt WebFetch. Instead, ask the user to paste the content.

Known auth-gated domains (non-exhaustive):

• linkedin.com — requires login
• x.com, twitter.com — requires login
• medium.com — may be paywalled
• substack.com — may be paywalled
• notion.so — requires access
• docs.google.com — requires access

When both URL + direct content are provided: Use the direct content for analysis. Retain the URL only as a source reference in the output document.

Step 3: Analyze Content

Analyze the fetched content to identify:

Also extract metadata for frontmatter:

Consider user's focus if provided (e.g., "API changes only").

Step 4: Read Configuration

Read ~/.claude/skills/learning-summary/config.yaml:

learning_repo: "/Users/jaykim/Documents/Projects/ai-learning"
auto_commit: false
auto_push: false

Why reuse learning-summary config?

• Keeps all learning documents in one repository
• Shares git settings (auto_commit, auto_push) for consistency
• No duplicate configuration needed
• Users only configure once

Output directory: Always use "blog/src/content/digests" subfolder to separate article digests from conversation summaries.

If config not found: Ask user for learning repository path. You can create the config file or use the provided path for this session only.

Step 5: Generate Filename

Create descriptive filename:

IMPORTANT: Run date '+%Y-%m-%d' to get the exact current date. Never estimate.

Format: YYYY-MM-DD-ai-[topic-slug].md

Examples:

• 2026-01-25-ai-claude-sonnet-4-5-release.md
• 2026-01-25-ai-openai-gpt5-features.md
• 2026-01-25-ai-langchain-updates.md

Topic slug rules:

• Lowercase, kebab-case
• Max 4-5 words
• Descriptive and searchable

Step 6: Generate Document

First, classify the content type to determine which template variant to use:

Template: Core (always included)

---
title: "[Article Title or Main Topic]"
date: YYYY-MM-DD
description: "[1-2 sentence summary under 160 chars]"
category: digests
tags: ["ai", "topic", "subtopic"]
source: "https://original-article-url"
lang: en
draft: false
---

## Summary

[1-2 paragraph summary in English]
[What this is about and why it matters]

## Key Concepts

### [Concept 1]
- **What**: [Description]
- **Why**: [Reasoning or benefit]
- **Impact**: [Who/what is affected]

### [Concept 2]
...

## Practical Applications

### Use Case 1: [Scenario]
[How to apply this in real projects]

### Use Case 2: [Scenario]
...

## Limitations & Gotchas

- [Warning 1]
- [Warning 2]
- [Tip 1]

## References

- [Original article URL]
- [Related documentation]
- [Related tools/libraries]

## Next Steps

- [ ] [Action item 1]
- [ ] [Action item 2]
- [ ] [Topic to explore further]

---

**Notes**:
[Personal notes or context]

Important formatting rules:

• Do NOT include # Title heading in body (the blog layout renders title from frontmatter)
• Do NOT use blockquote metadata (> **Source**: ...) — all metadata goes in frontmatter
• Body starts with ## 요약 (Summary) or first applicable section
• category is always digests
• tags are lowercase, kebab-case, as a YAML array
• source is the original article URL (omit if direct content with no URL)
• description should be under 160 characters for SEO
• Empty sections should be omitted entirely

Template: Technical Extensions (add when content type is Technical)

Include these sections when the article contains code, API changes, or migration guides:

## Code Examples

### Example 1: [What it demonstrates]
```language
[Runnable code]

Explanation: [What this code does]

Before/After Comparison

Before (Old Way)

[Old way]

After (New Way)

[New way]

Key Differences: [Key differences]

#### Template: Strategy Extensions (add when content type is Strategic/Opinion)

Include these sections when the article discusses frameworks, trends, or strategic analysis:

```markdown
## Key Framework

[Visual or structured representation of the article's main framework]
[Use tables, diagrams (text-based), or hierarchical lists]

## Case Comparisons

| Item | [Case A] | [Case B] |
|------|----------|----------|
| ... | ... | ... |

Section inclusion rules:

• Always: Summary, Key Concepts, Practical Applications, References
• Technical content: + Code Examples, Before/After Comparison
• Strategic content: + Key Framework, Case Comparisons
• Optional: Limitations & Gotchas (if important warnings exist), Next Steps
• Do not include empty sections. If a section has no meaningful content, omit it.

Step 7: Save Document

Save location: {learning_repo}/blog/src/content/digests/{filename}

Example: /Users/jaykim/Documents/Projects/ai-learning/blog/src/content/digests/2026-01-25-ai-claude-sonnet-4-5-release.md

Deduplication check: Before saving, use Glob to check if a file with the same date and similar topic slug already exists in the digests directory. If a match is found, ask the user whether to overwrite, append, or create with a suffix (e.g., -2).

Save: Use the Write tool directly. Do NOT run mkdir -p — the blog/src/content/digests/ directory should already exist in a configured learning repo. If Write fails due to a missing directory, then create it.

Step 8: Git Operations (if auto_commit)

Only if auto_commit: true in config:

cd "$learning_repo"
git add "blog/src/content/digests/$filename"
git commit -m "Add AI digest: $topic

Co-Authored-By: Claude <model> <noreply@anthropic.com>"

Note: Replace <model> with the actual model name being used (e.g., "Opus 4.6", "Sonnet 4.5"). Do not hardcode a specific model name.

If auto_push: true, also run:

git push

When pushed to main, GitHub Actions will automatically build and deploy the blog.

Step 9: Confirm to User

Show the user:

• Saved: Full path of saved file
• Captured: Brief summary of what was captured (bulleted list of key topics)
• Source: Original URL (if applicable)
• Git: Commit status (only if auto_commit enabled)
• Blog: URL where post will appear after deploy

Do not use emojis in the confirmation output. Keep it concise and scannable.

Trigger Phrases

English:

• "digest this article"
• "analyze this AI news"
• "summarize this tech blog"
• "/ai-digest [URL or content]"

Korean:

• "이 글 정리해줘"
• "AI 뉴스 요약"
• "기술 블로그 분석"

Quick Reference

When to Use

Use this skill when:

• User provides URL to AI/tech article
• User pastes content to analyze
• Need to capture rapidly changing AI updates
• Want structured notes for future reference

Skip when:

• General conversation summary (use learning-summary)
• Code review (use other tools)
• Already documented elsewhere

Error Handling

Configuration

Reuses ~/.claude/skills/learning-summary/config.yaml:

# Dedicated AI learning repository (absolute path)
learning_repo: "/Users/jaykim/Documents/Projects/ai-learning"

# Auto-commit to git
auto_commit: false

# Auto-push to remote (requires auto_commit: true)
auto_push: false

Output directory: Always uses blog/src/content/digests/ subfolder (hardcoded).

No separate config needed - shares learning_repo and git settings with learning-summary for consistency.

Examples

Example 1: URL Digest (fetchable)

User: /ai-digest https://anthropic.com/news/claude-sonnet-4-5

Claude: [Fetches content via WebFetch, classifies as Technical, generates document]

Saved: .../blog/src/content/digests/2026-01-25-ai-claude-sonnet-4-5-release.md

Captured:
- New Claude Sonnet 4.5 features and performance benchmarks
- Migration guide from previous versions
- Code examples showing before/after API changes

Example 2: Auth-gated URL + pasted content

User: /ai-digest https://linkedin.com/posts/someone/...
[pasted article text]

Claude: [Detects LinkedIn URL + direct content -> uses direct content, skips WebFetch]
[Classifies as Strategic/Opinion, uses strategy template variant]

Saved: .../blog/src/content/digests/2026-01-25-ai-agent-economy-strategy.md
Source: https://linkedin.com/posts/someone/...

Captured:
- Agent economy framework and 3 survival conditions
- Case comparisons (Supabase vs competitors, Resend vs SendGrid)

Example 3: Focused Analysis

User: /ai-digest "Focus on breaking changes only" https://openai.com/blog/gpt-5

Claude: [Fetches content, applies user focus filter to analysis]

Saved with focus on:
- API breaking changes and deprecated parameters
- Migration steps with before/after code examples

Example 4: Auth-gated URL only (no content)

User: /ai-digest https://linkedin.com/posts/someone/...

Claude: LinkedIn requires authentication, so I can't fetch this content directly.
Please paste the article text and I'll digest it.

Related Skills

• learning-summary: For conversation summaries (saves to blog/src/content/learnings/)
• project-insight: For codebase analysis

Tips

1. Batch processing: Digest multiple articles in one session
2. Tag consistently: Use consistent tags for searchability
3. Update existing: If topic already documented, offer to update
4. Link related: Suggest related articles from ai-learning repo