tech-news-digest

draco-agent/tech-news-digest · updated May 4, 2026

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$npx skills add https://github.com/draco-agent/tech-news-digest --skill tech-news-digest
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summary

Automated tech news digest system with unified data source model, quality scoring pipeline, and template-based output generation.

  • Collects from six sources in parallel: RSS feeds, Twitter/X KOLs, GitHub releases and trending repos, Reddit posts, and web search, with deduplication and quality scoring across all sources
  • Includes 151 pre-configured sources (62 RSS feeds, 48 Twitter accounts, 28 GitHub repos, 13 subreddits, plus web search) covering AI, crypto, and frontier tech topics
  • S
skill.md

Tech News Digest

Automated tech news digest system with unified data source model, quality scoring pipeline, and template-based output generation.

Quick Start

  1. Configuration Setup: Default configs are in config/defaults/. Copy to workspace for customization:

    mkdir -p workspace/config
    cp config/defaults/sources.json workspace/config/tech-news-digest-sources.json
    cp config/defaults/topics.json workspace/config/tech-news-digest-topics.json
    
  2. Environment Variables:

    • TWITTERAPI_IO_KEY - twitterapi.io API key (optional, preferred)
    • X_BEARER_TOKEN - Twitter/X official API bearer token (optional, fallback)
    • TAVILY_API_KEY - Tavily Search API key, alternative to Brave (optional)
    • WEB_SEARCH_BACKEND - Web search backend: auto|brave|tavily (optional, default: auto)
    • BRAVE_API_KEYS - Brave Search API keys, comma-separated for rotation (optional)
    • BRAVE_API_KEY - Single Brave key fallback (optional)
    • GITHUB_TOKEN - GitHub personal access token (optional, improves rate limits)
  3. Generate Digest:

    # Unified pipeline (recommended) — runs all 6 sources in parallel + merge
    python3 scripts/run-pipeline.py \
      --defaults config/defaults \
      --config workspace/config \
      --hours 48 --freshness pd \
      --archive-dir workspace/archive/tech-news-digest/ \
      --output /tmp/td-merged.json --verbose --force
    
  4. Use Templates: Apply Discord, email, or PDF templates to merged output

Configuration Files

sources.json - Unified Data Sources

{
  "sources": [
    {
      "id": "openai-rss",
      "type": "rss",
      "name": "OpenAI Blog",
      "url": "https://openai.com/blog/rss.xml",
      "enabled": true,
      "priority": true,
      "topics": ["llm", "ai-agent"],
      "note": "Official OpenAI updates"
    },
    {
      "id": "sama-twitter",
      "type": "twitter", 
      "name": "Sam Altman",
      "handle": "sama",
      "enabled": true,
      "priority": true,
      "topics": ["llm", "frontier-tech"],
      "note": "OpenAI CEO"
    }
  ]
}

topics.json - Enhanced Topic Definitions

{
  "topics": [
    {
      "id": "llm",
      "emoji": "🧠",
      "label": "LLM / Large Models",
      "description": "Large Language Models, foundation models, breakthroughs",
      "search": {
        "queries": ["LLM latest news", "large language model breakthroughs"],
        "must_include": ["LLM", "large language model", "foundation model"],
        "exclude": ["tutorial", "beginner guide"]
      },
      "display": {
        "max_items": 8,
        "style": "detailed"
      }
    }
  ]
}

Scripts Pipeline

run-pipeline.py - Unified Pipeline (Recommended)

python3 scripts/run-pipeline.py \
  --defaults config/defaults [--config CONFIG_DIR] \
  --hours 48 --freshness pd \
  --archive-dir workspace/archive/tech-news-digest/ \
  --output /tmp/td-merged.json --verbose --force
  • Features: Runs all 6 fetch steps in parallel, then merges + deduplicates + scores
  • Output: Final merged JSON ready for report generation (~30s total)
  • Metadata: Saves per-step timing and counts to *.meta.json
  • GitHub Auth: Auto-generates GitHub App token if $GITHUB_TOKEN not set
  • Fallback: If this fails, run individual scripts below

Individual Scripts (Fallback)

fetch-rss.py - RSS Feed Fetcher

python3 scripts/fetch-rss.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE] [--verbose]
  • Parallel fetching (10 workers), retry with backoff, feedparser + regex fallback
  • Timeout: 30s per feed, ETag/Last-Modified caching

fetch-twitter.py - Twitter/X KOL Monitor

python3 scripts/fetch-twitter.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE] [--backend auto|official|twitterapiio]
  • Backend auto-detection: uses twitterapi.io if TWITTERAPI_IO_KEY set, else official X API v2 if X_BEARER_TOKEN set
  • Rate limit handling, engagement metrics, retry with backoff

fetch-web.py - Web Search Engine

python3 scripts/fetch-web.py [--defaults DIR] [--config DIR] [--freshness pd] [--output FILE]
  • Auto-detects Brave API rate limit: paid plans → parallel queries, free → sequential
  • Without API: generates search interface for agents

fetch-github.py - GitHub Releases Monitor

python3 scripts/fetch-github.py [--defaults DIR] [--config DIR] [--hours 168] [--output FILE]
  • Parallel fetching (10 workers), 30s timeout
  • Auth priority: $GITHUB_TOKEN → GitHub App auto-generate → gh CLI → unauthenticated (60 req/hr)

fetch-github.py --trending - GitHub Trending Repos

python3 scripts/fetch-github.py --trending [--hours 48] [--output FILE] [--verbose]
  • Searches GitHub API for trending repos across 4 topics (LLM, AI Agent, Crypto, Frontier Tech)
  • Quality scoring: base 5 + daily_stars_est / 10, max 15

fetch-reddit.py - Reddit Posts Fetcher

python3 scripts/fetch-reddit.py [--defaults DIR] [--config DIR] [--hours 48] [--output FILE]
  • Parallel fetching (4 workers), public JSON API (no auth required)
  • 13 subreddits with score filtering

enrich-articles.py - Article Full-Text Enrichment

python3 scripts/enrich-articles.py --input merged.json --output enriched.json [--min-score 10] [--max-articles 15] [--verbose]
  • Fetches full article text for high-scoring articles
  • Cloudflare Markdown for Agents (preferred) → HTML extraction (fallback) → Skip (paywalled/social)
  • Blog domain whitelist with lower score threshold (≥3)
  • Parallel fetching (5 workers, 10s timeout)

merge-sources.py - Quality Scoring & Deduplication

python3 scripts/merge-sources.py --rss FILE --twitter FILE --web FILE --github FILE --reddit FILE
  • Quality scoring, title similarity dedup (85%), previous digest penalty
  • Output: topic-grouped articles sorted by score

validate-config.py - Configuration Validator

python3 scripts/validate-config.py [--defaults DIR] [--config DIR] [--verbose]
  • JSON schema validation, topic reference checks, duplicate ID detection

generate-pdf.py - PDF Report Generator

python3 scripts/generate-pdf.py --input report.md --output digest.pdf [--verbose]
  • Converts markdown digest to styled A4 PDF with Chinese typography (Noto Sans CJK SC)
  • Emoji icons, page headers/footers, blue accent theme. Requires weasyprint.

sanitize-html.py - Safe HTML Email Converter

python3 scripts/sanitize-html.py --input report.md --output email.html [--verbose]
  • Converts markdown to XSS-safe HTML email with inline CSS
  • URL whitelist (http/https only), HTML-escaped text content

source-health.py - Source Health Monitor

python3 scripts/source-health.py --rss FILE --twitter FILE --github FILE --reddit FILE --web FILE [--verbose]
  • Tracks per-source success/failure history over 7 days
  • Reports unhealthy sources (>50% failure rate)

summarize-merged.py - Merged Data Summary

python3 scripts/summarize-merged.py --input merged.json [--top N] [--topic TOPIC]
  • Human-readable summary of merged data for LLM consumption
  • Shows top articles per topic with scores and metrics

User Customization

Workspace Configuration Override

Place custom configs in workspace/config/ to override defaults:

  • Sources: Append new sources, disable defaults with "enabled": false
  • Topics: Override topic definitions, search queries, display settings
  • Merge Logic:
    • Sources with same id → user version takes precedence
    • Sources with new id → appended to defaults
    • Topics with same id → user version completely replaces default

Example Workspace Override

// workspace/config/tech-news-digest-sources.json
{
  "sources": [
    {
      "id": "simonwillison-rss",
      "enabled": false,
      "note"
how to use tech-news-digest

How to use tech-news-digest on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tech-news-digest
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/draco-agent/tech-news-digest --skill tech-news-digest

The skills CLI fetches tech-news-digest from GitHub repository draco-agent/tech-news-digest and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tech-news-digest

Reload or restart Cursor to activate tech-news-digest. Access the skill through slash commands (e.g., /tech-news-digest) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.857 reviews
  • Neel Diallo· Dec 20, 2024

    We added tech-news-digest from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ishan Khanna· Dec 20, 2024

    We added tech-news-digest from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Camila Ndlovu· Dec 4, 2024

    tech-news-digest fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Omar Malhotra· Dec 4, 2024

    tech-news-digest reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Luis Gupta· Nov 23, 2024

    Registry listing for tech-news-digest matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Omar Liu· Nov 23, 2024

    We added tech-news-digest from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Diya Sanchez· Nov 11, 2024

    tech-news-digest reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Mia Smith· Nov 11, 2024

    tech-news-digest reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Diya Ramirez· Nov 7, 2024

    Solid pick for teams standardizing on skills: tech-news-digest is focused, and the summary matches what you get after install.

  • Diya Abbas· Oct 26, 2024

    tech-news-digest has been reliable in day-to-day use. Documentation quality is above average for community skills.

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