HN Morning Brief

Morning Briefing

Step 1 — Pull user interests from memory

memory_search("interests topics preferences technology news")

Do this first, before fetching stories — the results determine how stories are ranked. Extract any topics, technologies, or themes found. If nothing relevant comes back, fall back to HN ranking order.

Step 2 — Fetch top HN stories

python3 skills/hn-morning-brief/scripts/fetch_hn.py --limit 20

(Path is relative to the project root — openclaw installs this skill at skills/hn-morning-brief/.)

Returns JSON with: title, article_url, hn_url, domain, author, points, num_comments.

Step 3 — Rank and filter

Score each story by combining two signals:

• Relevance to user interests (from memory) — a story the user cares about is worth more regardless of score
• HN points — use as a tiebreaker and quality signal when interests are unclear

Surface the 8–12 highest-scoring stories. If memory search returned no clear interests, rank by points only.

Step 4 — Present briefing

## HN Morning Brief — {today's date}

{N} stories picked for you

1. **{Title}** `{domain}` · ⬆ {points} · 💬 {num_comments}
   {one-line context or why this is interesting}
   → [Article]({article_url}) · [HN Discussion]({hn_url})

2. ...

---
Say "dive deeper into #N" or "tell me more about [title]" to get a full summary.

Diving Deeper

When the user picks a story:

1. Fetch and summarize the article — read the article URL and write a 3–5 sentence summary of the key points. Do this even if the user just says "more on #3" — they want the content, not just the link.
2. Show both links:
• Article: {article_url}
• HN Discussion: {hn_url} (often where the most interesting debate happens)
3. Offer to go further: "Want me to search for more context on this?"

Gotchas

• article_url is the original article. hn_url is the HN discussion thread. Never swap them — linking to the HN page when the user wants the article is a bad experience.
• If the article is a PDF or appears paywalled, say so and summarize from the title, domain, and any available description instead of silently failing.
• If memory_search returns no clear interests, rank by points and don't guess — invented interests will surface irrelevant stories.