LLM Daily Newsletter Skill

Generate a professional daily AI/LLM newsletter by utilizing the fetch_sources.py script to fetch dynamically configured data, and leveraging your LLM capabilities to synthesize it into a curated briefing.

Workflow

Step 1: Collect Data

Run the universal collector script to fetch data from all sources defined in references/sources.yaml.

cd <skill_dir>/scripts
uv run --with requests --with feedparser --with pyyaml fetch_sources.py

This will parse all RSS feeds, JSON APIs, and XML APIs, generating a single markdown file at scripts/output/context.md.

Step 2: Generate Newsletter

Read the generated scripts/output/context.md file using your file reading tools. Using this context, generate the final newsletter based on the Newsletter Structure and Section Generation Prompts below.

Write the final newsletter to scripts/output/llm_newsletter_YYYY-MM-DD.md.

Step 3: Publish (Optional)

If the user wants to publish the newsletter and BUTTONDOWN_API_KEY is present in the environment:

cd <skill_dir>/scripts
uv run --with requests publish.py output/llm_newsletter_YYYY-MM-DD.md --status draft

Without BUTTONDOWN_API_KEY, the newsletter is only generated locally.

Newsletter Structure

Follow this template exactly:

# 🔍 LLM DAILY
## Your Daily Briefing on Large Language Models
**{date}**

{stats_line}

---

# HIGHLIGHTS
{3-5 bullet points of key developments across all sections}

---

# BUSINESS
{funding, M&A, company updates, market analysis}

---

# PRODUCTS
{new releases, updates, applications, community reception}

---

# TECHNOLOGY
{open source projects, models/datasets, developer tools, infrastructure}

---

# RESEARCH
## Paper of the Day
{single most significant paper with full details}

## Notable Research
{3-5 other significant papers}

---

# LOOKING AHEAD
{emerging trends and predictions, 1-2 paragraphs}

Section Generation Prompts

Apply these guidelines when drafting each section based on the collected context.

BUSINESS Section

Source data: VentureBeat, TechCrunch.

Cover: funding rounds, M&A, company announcements, market trends
Include direct links to original articles
Focus on developments from the past 24-48 hours

PRODUCTS Section

Source data: Product Hunt, Hacker News.

Cover: new AI product launches, updates, applications, user feedback
Include direct links and company attribution
Specify if startup or established player

TECHNOLOGY Section

Source data: GitHub Recent Top AI Repos, HuggingFace Trending Models.

Cover: open source projects, new models, developer tools, infrastructure
Include direct links to GitHub repos and HuggingFace pages
Highlight distinctive features and technical details

RESEARCH Section

Source data: ArXiv LLM Papers.

"Paper of the Day": single most significant paper with title, authors, institutions, arXiv link, 2-3 sentence significance explanation, key findings summary
"Notable Research": 3-5 other papers with title, primary author, arXiv link, 1-2 sentence summary
Always include arXiv URLs in format https://arxiv.org/abs/XXXX.XXXXX

HIGHLIGHTS Section

Generate after all other sections. Extract 3-5 most important developments as bullet points. Each bullet: concise (1-2 sentences), specific, using "•" symbol.

LOOKING AHEAD Section

1-2 paragraphs identifying emerging trends and predictions. Reference current quarter. Keep concise (~100 words).

Configuration

Data sources are configured in references/sources.yaml. To add a new source:

Identify if it is an rss, json_api, or xml_api.
Add a new entry to sources.yaml with the appropriate parser_config.

llm-daily