Hn sentiment analysis

Hacker News Sentiment Analysis

Analyze a Hacker News thread URL provided through /skill:hn-sentiment-analysis.

Non-negotiable rules

• Do not write any additional scripts, one-off parsers, notebooks, or ad-hoc data-processing code for this task. The scripts in this skill are the complete analysis pipeline.
• Do not read thread.json, comments.jsonl, or every chunks/comments-*.md file into context. Large HN threads will overflow the model context.
• Do not include raw HN item IDs, comment IDs, thread IDs, naked HN URLs, or internal lookup labels in the human-facing final report. Use author names, roles, themes, and short quote snippets instead.
• If you need a different output directory, review-pack size, or chunk size, rerun the provided script with flags instead of creating new code.

Workflow

1. Prepare the HN thread artifacts with the provided pipeline:

   python skills/hn-sentiment-analysis/scripts/prepare_hn_sentiment_analysis.py 'https://news.ycombinator.com/item?id=12345678'
   

   The script parses the HN item id, downloads the full nested thread JSON from Algolia, saves it, flattens comments, creates targeted lookup chunks, and generates a bounded review-pack.md for analysis.

2. Read the generated analysis-brief.md first. Follow its reading order.

3. Read story.md, fetch the article URL with the fetch tool, and write a very short article summary. If there is no article URL, summarize the HN story text.

4. Read review-pack.md. This is the primary bounded evidence pack for sentiment analysis.

5. Read sentiment-worksheet.md as the quality checklist.

6. Only if needed, read targeted detail files:

   • top-subthreads.md for more detail on engaged subthreads.
   • key-person-candidates.md for possible insiders/authors/maintainers/executives.
   • author-index.md to avoid over-counting prolific authors.
   • chunk-index.md to choose one specific chunks/comments-*.md file for a targeted lookup.

Quality requirements

A good sentiment analysis must:

• Separate the article summary from HN commenter sentiment.
• Distinguish sentiment toward the article, topic, product/company/project, implementation details, and HN meta-discussion.
• Group opinions by theme, not only by positive/negative polarity.
• Support each major claim with representative authors, roles, or short quote snippets; never with raw numeric HN IDs.
• Identify key people in the thread, such as the article author, library maintainer, founder, CEO, CTO, developer, employee, or other company/project insiders, and summarize their comments by subthread.
• Avoid treating reply count as a vote count; use it only as engagement/context.
• Avoid over-counting prolific authors as multiple independent votes.
• Separate substantive criticism from jokes, tangents, ideology, bikeshedding, and sarcasm.
• Call out notable disagreements, minority viewpoints, and uncertainty.
• Remember that HN commenters are a technical/startup-heavy audience and not representative of the general public.

Output format

Keep the final answer concise and structured:

• Article summary
• Overall HN sentiment with confidence level
• Common opinion groups, with representative authors or short quote snippets
• Key people and their comments
• Notable caveats, minority views, and uncertainty

Scripts

• scripts/prepare_hn_sentiment_analysis.py is the main pipeline. It downloads or loads a thread, writes raw Algolia JSON, and prepares bounded analysis artifacts.
• scripts/download_hn_thread.py only downloads the complete nested Algolia item JSON for a Hacker News thread URL or item id. Use it directly only when the user specifically asks for the raw JSON.