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:
| Element | What to Extract | |---------|----------------| | URL | Any URL (https://...) | | Focus | User's specific interest (e.g., "focus on API changes") | | Direct Content | Text content if no URL provided |
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 loginx.com,twitter.com— requires loginmedium.com— may be paywalledsubstack.com— may be paywallednotion.so— requires accessdocs.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:
| Category | What to Extract | |----------|----------------| | Topic & Context | What is this about? Why does it matter? | | Key Changes/Concepts | New features, updates, or core concepts | | Practical Applications | How to use this in real projects | | Code Examples | Runnable code snippets | | Migration/Upgrade Notes | Breaking changes, migration steps | | Limitations | Known issues, constraints, gotchas |
Also extract metadata for frontmatter:
| Field | How to Derive |
|-------|---------------|
| title | Article title or main topic |
| description | 1-2 sentence summary under 160 characters |
| tags | 3-7 lowercase kebab-case keywords from content topics |
| source | Original article URL |
| lang | Always en (output is always in English) |
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.md2026-01-25-ai-openai-gpt5-features.md2026-01-25-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:
| Content Type | Characteristics | Template Variant | |-------------|-----------------|------------------| | Technical | Code changes, API updates, library releases, tutorials | Full template (with code examples, Before/After) | | Strategic/Opinion | Industry analysis, trend pieces, strategy frameworks | Strategy template (with frameworks, comparison tables) | | News/Announcement | Product launches, funding, partnerships | News template (concise, fact-focused) |
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
# Titleheading 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 categoryis alwaysdigeststagsare lowercase, kebab-case, as a YAML arraysourceis the original article URL (omit if direct content with no URL)descriptionshould 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
| Scenario | Response |
|----------|----------|
| Auth-gated URL, no content | Ask user to paste the article content. Do NOT attempt WebFetch. |
| Auth-gated URL + content provided | Use direct content. Retain URL as source reference only. |
| WebFetch fails | Error: Failed to fetch content. You can paste the content directly. |
| Config not found | Ask user for ai-learning repository path |
| Write fails (missing directory) | Create directory with mkdir -p, then retry Write |
| Write permission denied | Error: Cannot write to {path}. Check permissions. |
| Git not initialized | Warning: Not a git repository. Document saved but not committed. |
| Empty content | Error: No content to analyze. Please provide URL or text. |
| Duplicate file exists | Ask user: overwrite, append, or save with -2 suffix |
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 toblog/src/content/learnings/)project-insight: For codebase analysis
Tips
- Batch processing: Digest multiple articles in one session
- Tag consistently: Use consistent tags for searchability
- Update existing: If topic already documented, offer to update
- Link related: Suggest related articles from ai-learning repo
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