AI Skill Report Card
Synthesizing HN Discussion Threads
Quick Start12 / 15
Given raw scraped HN thread text (title, points, flat username/timestamp list, fragmented comment bodies), produce:
- A one-line summary of what the submission is and the community's overall reception
- A bulleted list of the top 3-5 discussion themes with representative viewpoints
- Notable facts surfaced by the author/maintainer (identifiable by root-level replies to many threads)
- Any controversy or unresolved debate
Recommendation▾
Add a second example showing a thread with full, non-truncated comment bodies to demonstrate the contrast with the truncated-data scenario
Workflow13 / 15
Progress:
- Identify submission title, link, points, and comment count
- Identify the likely author/maintainer (username replying across many top-level threads, often root "parent" of several subthreads)
- Group comments into threads by indentation/"root | parent" markers
- For each thread, infer the topic from usernames, position, and any visible text fragments
- Cluster threads into 3-6 recurring themes (e.g., technical architecture, comparison to alternatives, privacy concerns, licensing, performance)
- Extract any direct quotes or specifics that are present in the raw text — never invent comment content that wasn't provided
- Flag when comment bodies are missing/truncated and note that analysis is based on structure (usernames, reply patterns) rather than full text
- Write final synthesis: summary, themes, author responses, open questions
Recommendation▾
Include a bad/incorrect output example (e.g., one that fabricates sentiment or invents quotes) paired with the corrected version to reinforce the pitfalls section
Examples12 / 20
Example 1: Input: Raw HN page dump for "Hister – A private, full content search index" with 497 points, 101 comments, author "asciimoo" replying in ~15 root threads, usernames discussing comparisons to other search tools, self-hosting, licensing. Output:
- Summary: "Hister," a self-hosted personal search index tool, launched to strong reception (497 points, 101 comments). Maintainer asciimoo was highly active in the thread, engaging with ~15+ separate discussion branches.
- Themes identified from thread structure: (1) comparisons to existing tools/alternatives, (2) architecture/implementation questions directed at the maintainer, (3) self-hosting and deployment concerns, (4) privacy/data-control angle, (5) feature requests and licensing questions.
- Caveat: comment text was largely truncated in the source; this synthesis is based on thread topology (reply counts, maintainer engagement pattern) rather than full quotes. Recommend re-fetching with full comment bodies for direct quotes.
Recommendation▾
Provide a concrete output template/format (e.g., markdown skeleton with headers for Summary/Themes/Author Responses/Open Questions) so output structure is consistent rather than just described in prose
Best Practices
- Treat the maintainer/author as the user whose username recurs across the most distinct root-level threads — they're the signal for "what questions did people actually ask the creator."
- When comment text is missing or fragmented, explicitly state this limitation rather than fabricating plausible-sounding opinions.
- Use points/comment-count ratio as a rough signal of engagement intensity vs. just popularity.
- Prefer thread depth (nested "root | parent | next" chains) as a proxy for which subtopics generated real back-and-forth vs. one-off remarks.
- When multiple similar threads exist (e.g., several users asking about licensing), merge them into one theme rather than listing each separately.
Common Pitfalls
- Do not invent comment content, opinions, or quotes that aren't present in the raw input — only structure (usernames, timestamps, nesting) may be reliably inferred.
- Do not assume sentiment (positive/negative) from thread length alone; a long thread can be enthusiastic or critical.
- Don't treat every username as a unique opinion-holder if the text suggests they're the same recurring maintainer account.
- Avoid presenting a structural analysis as if it were a full content summary — always flag when working from skeletal/truncated data.