daily-news-report
Helps your agent collect and filter important technical information. It creates daily reports with the most relevant content.
What it does
This tool gathers high-quality technical news from a list of predefined websites and organizes them into a daily Markdown report. It prioritizes relevant information and avoids low-quality content.
When to use it
- ✓To stay updated on the latest tech news.
- ✓To get a quick summary of important technical information for the day.
- ✓To create a curated news archive on a specific topic.
Example of what to ask your AI
“Generate today's technical news report based on the configured sources.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool accesses defined websites, filters content to find high-quality technical information, and then compiles a report in Markdown format. It uses a smart system to decide which sources to check and when to stop, ensuring efficiency.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill daily-news-reportOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/daily-news-report/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Daily News Report v3.0
Architecture Upgrade: Main Agent Orchestration + SubAgent Execution + Browser Scraping + Smart Caching
Core Architecture
┌─────────────────────────────────────────────────────────────────────┐
│ Main Agent (Orchestrator) │
│ Role: Scheduling, Monitoring, Evaluation, Decision, Aggregation │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 1. Init │ → │ 2. Dispatch │ → │ 3. Monitor │ → │ 4. Evaluate │ │
│ │ Read Config │ │ Assign Tasks│ │ Collect Res │ │ Filter/Sort │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 5. Decision │ ← │ Enough 20? │ │ 6. Generate │ → │ 7. Update │ │
│ │ Cont/Stop │ │ Y/N │ │ Report File │ │ Cache Stats │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────┘
↓ Dispatch ↑ Return Results
┌─────────────────────────────────────────────────────────────────────┐
│ SubAgent Execution Layer │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Worker A │ │ Worker B │ │ Browser │ │
│ │ (WebFetch) │ │ (WebFetch) │ │ (Headless) │ │
│ │ Tier1 Batch │ │ Tier2 Batch │ │ JS Render │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ ↓ ↓ ↓ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Structured Result Return │ │
│ │ { status, data: [...], errors: [...], metadata: {...} } │ │
│ └─────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────┘
Configuration Files
This skill uses the following configuration files:
| File | Purpose |
|---|---|
sources.json |
Source configuration, priorities, scrape methods |
cache.json |
Cached data, historical stats, deduplication fingerprints |
Execution Process Details
Phase 1: Initialization
Steps:
1. Determine date (user argument or current date)
2. Read sources.json for source configurations
3. Read cache.json for historical data
4. Create output directory NewsReport/
5. Check if a partial report exists for today (append mode)
Phase 2: Dispatch SubAgents
Strategy: Parallel dispatch, batch execution, early stopping mechanism
Wave 1 (Parallel):
- Worker A: Tier1 Batch A (HN, HuggingFace Papers)
- Worker B: Tier1 Batch B (OneUsefulThing, Paul Graham)
Wait for results → Evaluate count
If < 15 high-quality items:
Wave 2 (Parallel):
- Worker C: Tier2 Batch A (James Clear, FS Blog)
- Worker D: Tier2 Batch B (HackerNoon, Scott Young)
If still < 20 items:
Wave 3 (Browser):
- Browser Worker: ProductHunt, Latent Space (Require JS rendering)
Phase 3: SubAgent Task Format
Task format received by each SubAgent:
task: fetch_and_extract
sources:
- id: hn
url: https://news.ycombinator.com
extract: top_10
- id: hf_papers
url: https://huggingface.co/papers
extract: top_voted
output_schema:
items:
- source_id: string # Source Identifier
title: string # Title
summary: string # 2-4 sentence summary
key_points: string[] # Max 3 key points
url: string # Original URL
keywords: string[] # Keywords
quality_score: 1-5 # Quality Score
constraints:
…
FAQ
How does the tool decide which news is 'high-quality'?
It uses defined filters to prioritize cutting-edge, practical, and productivity-focused technical information, while excluding general, marketing, or overly academic news.
What happens if a website doesn't work?
The tool tries to collect data from other sources and may decide to skip the problematic website in future runs to ensure the report is generated.
Is the report always the same?
No, the report is generated daily based on the latest news available from the configured sources, ensuring always up-to-date content.
Details
Original description by the author
Scrapes content based on a preset URL list, filters high-quality technical information, and generates daily Markdown reports.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2699
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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