OpenMark — Weekly Digest

What Ahmad saved in the last N days, clustered by topic, scannable in 60 seconds. NOT a newsletter — this is internal recon: "what should I pay attention to from my own saves."

When to use vs newsletter

Asker says	Skill
"what did I save this week"	weekly-digest (this)
"give me a recap of last week"	weekly-digest
"write me a newsletter on this week's AI saves"	openmark-newsletter
"what's new in OpenMark"	weekly-digest

Digest is private notes. Newsletter is a publishable post.

Tool sequence

1. Get window. Default: last 7 days. Ahmad can override ("last 30 days", "since yesterday", "this month").
2. Pull recent: find_recent(days=N, n=40) — empty query, just get everything fresh.
3. If thin (< 10 results): the time filter only matches nodes with created_at set, which today is only LinkedIn. Fall back to find_by_source(source="linkedin", n=30) and warn Ahmad: "Only LinkedIn nodes have timestamps. Edge/Raindrop/YouTube need backfill before they show up in time queries."
4. Cluster. Read the categories of returned hits. Group into 3–6 topical buckets by category + tag overlap. Examples:
   • "Agent frameworks" (LangChain, LangGraph, Agent Development)
   • "RAG patterns" (RAG & Vector Search)
   • "Models + research" (Data Science & ML, AI Tools)
   • "Tooling I want to try" (GitHub Repos & OSS with score ≥ 6)
   • "LinkedIn discourse" (LinkedIn posts, often with takes)

Output format

# OpenMark digest — last {N} days
**{total_count} new items · {bucket_count} clusters**

## {Bucket 1 name}  ({count})
- {Title} — {URL} — {one-line so-what}
- {Title} — {URL} — {one-line so-what}
- ... (max 6 per bucket)

## {Bucket 2 name}  ({count})
...

---
**Quick stats**
- By source: {raindrop: N, linkedin: M, edge: K, ...}
- Top tags: {tag1 (n), tag2 (n), tag3 (n)}
- Highest similarity in window: {Title} — {URL} ({sim:.2f})

Rules

• Buckets must be inferred from the data, not preset. If everything is one topic, output one bucket — don't fake variety.
• No "key takeaways" section. Ahmad scans this, he doesn't need conclusions.
• Order buckets by count desc so the heavy stuff is on top.
• Within a bucket, sort by similarity if a query was provided, else by bm_score desc.
• Cap each bucket at 6 items. If more, append ... and N more with a follow-up suggestion: (run: search_by_category('X') for the full list).

What NOT to do

• Don't editorialize ("this is interesting because…"). Ahmad makes those calls.
• Don't fetch full page content (no WebFetch) — this is a fast digest, not deep research.
• Don't drop items just because the title is bare. The URL is the value; render whatever title exists or use (no title) as fallback.