hnread

name: hnread description: >- Read and analyze Hacker News discussion threads. Fetches an HN thread URL, converts it to flat markdown using hn_flat.py, then extracts and presents the most valuable discussion insights — critical rebuttals, personal anecdotes, technical corrections, and notable debates. Use when the user provides an HN URL and wants a curated summary of the discussion.

HNRead - Hacker News Discussion Analyzer

Fetch a Hacker News discussion thread and extract the most valuable insights from the comments.

Available Scripts

• scripts/hn_flat.py — Fetches an HN thread and converts it to flat markdown with nested comment structure.

Workflow

1. Receive the HN URL from the user (e.g., https://news.ycombinator.com/item?id=12345)

2. Fetch and convert the discussion to markdown:

   uv run scripts/hn_flat.py "<URL>" --no-frontmatter --stdout
   

   If the output is very long (large threads), use condensing to reduce noise:

   uv run scripts/hn_flat.py "<URL>" --no-frontmatter --stdout --condense 0.6
   

3. Read the markdown output and analyze the comments to extract valuable insights.

4. Present findings organized into the following categories (skip any category that has no relevant content):

   Output Format

   Start with the post title and a one-line summary of what the linked article/post is about (infer from comments if needed).

   Then list insights by category:

   Critical Rebuttals / Counterarguments — Comments that challenge, critique, or offer counterpoints to the article's claims.

   Personal Experiences & Anecdotes — First-hand stories or real-world experiences shared by commenters that add context.

   Technical Insights & Corrections — Technical details, corrections of misconceptions, or deeper explanations.

   Notable Debates — Back-and-forth exchanges between commenters that surface interesting perspectives.

   Other Noteworthy Points — Anything else that stands out as valuable, surprising, or thought-provoking.

   For each insight:

   • Write a short summary line describing the point
   • Quote the relevant comment text (use > blockquote), attribute it to the author (@username)
   • If it's a debate, quote both sides

   Example:

   **Critical Rebuttals / Counterarguments**
   
   1. The article's claim about X is misleading — the actual situation is Y.
      > @someuser: "The article completely ignores the fact that..."
   
   **Notable Debates**
   
   1. Whether approach A or B is better for large-scale systems.
      > @user1: "In my experience with distributed systems..."
      > @user2: "That's true for small clusters, but at scale..."
   

5. Language: Present the analysis in the same language the user used when invoking the skill. If the user wrote in Chinese, respond in Chinese. If in English, respond in English.

Notes

• Focus on quality over quantity — surface 5-15 truly valuable insights rather than listing everything.
• Prefer comments with substance: data, experience, expertise. Skip low-effort reactions.
• When a comment thread builds on itself (A replies to B replies to C), capture the full arc if it's insightful.
• If the thread is very large, use --condense 0.5 to reduce noise before analysis.