teamday-ai/agents/hackernews

Hacker News Engagement Skill

Find relevant Hacker News discussions and draft helpful responses that position TeamDay naturally.

When to Use

• User asks to find Hacker News discussions about AI agents, developer tools, startups, etc.
• User wants to monitor HN for engagement opportunities or product mentions
• User asks to draft a reply to a HN thread
• User says "check HN" or "find Hacker News conversations"
• Periodic social listening for brand mentions or relevant tech discussions

Scripts

Search Hacker News

bun .claude/skills/hackernews/scripts/hn-search.ts "<query>" [options]

Read Thread + Comments

bun .claude/skills/hackernews/scripts/hn-thread.ts "<item_id>" [--depth=N]

Fetches the full post body and comment tree. Use this to understand the conversation before drafting a response.

Target Topics

These topics are most relevant to TeamDay's domain on Hacker News:

Workflow

Phase 1: Discovery

Run targeted searches across Hacker News:

# Broad search for AI agent discussions
bun .claude/skills/hackernews/scripts/hn-search.ts "ai agents" --sort=date --time=week
 
# Technical discussions about Claude and MCP
bun .claude/skills/hackernews/scripts/hn-search.ts "claude code" --sort=date --time=week
 
# Show HN posts — prime engagement opportunities
bun .claude/skills/hackernews/scripts/hn-search.ts "Show HN ai" --sort=date --time=week
 
# Comments discussing pain points
bun .claude/skills/hackernews/scripts/hn-search.ts "ai automation" --type=comment --sort=date

Phase 2: Qualify Opportunities

High-value threads (prioritize):

• "Show HN" posts about AI tools (builders are active and responsive)
• "Ask HN" questions about AI agents, automation, or developer tools
• Technical discussions comparing AI platforms or frameworks
• Posts with 10+ comments and growing
• Threads where users share pain points that TeamDay solves

Skip these:

• Threads older than 2 weeks (HN discussions die fast)
• Posts with 0 comments and low score (no audience)
• Pure research/academic discussions (wrong audience for a product)
• Heated political/ethical AI debates (high risk, low reward)

Phase 3: Deep-Read Promising Threads

bun .claude/skills/hackernews/scripts/hn-thread.ts "12345678"

Understand:

1. What is OP actually building or struggling with?
2. What answers already exist in the comments?
3. Is there a technical gap TeamDay could fill?
4. What's the tone — technical, casual, skeptical?

Phase 4: Draft Response

HN Tone Guidelines (CRITICAL — HN is different from Reddit):

• Be deeply technical. HN readers are engineers, founders, and researchers.
• Never use marketing language. No "revolutionize", "game-changing", "unlock potential".
• Share technical details: architecture, trade-offs, what didn't work.
• Be honest about limitations. HN respects intellectual honesty above all.
• Keep it concise. HN favors dense, information-rich comments.

Phase 5: Report

Save engagement reports to track what's been done:

# Save to docs
Write to: docs/hackernews/YYYY-MM-DD-hn-engagement.md

Rate Limits

The HN Algolia API is generous — approximately 10,000 requests per hour with no authentication required. Rate limiting is unlikely to be an issue for normal usage.