Openmark Fast Search

OpenMark — Fast Search

Single-shot search over Ahmad's personal knowledge graph. Optimised for "I just want results, now."

The recipe (in this exact order)

1. One call: search_semantic(query=<user query>, n=12).
2. Inspect the returned hits[]. Each hit has url, title, similarity, category, source, tags, community_id.
3. No second tool call unless you see one of these failure signals:
   • total_found == 0 → fall back to find_by_tag if the query looks like a single token, else search_by_category if you can map it to a canonical category.
   • First three hits all have similarity < 0.55 → escalate to search_by_community (same query).
   • User explicitly named a source ("youtube", "linkedin") → use search_youtube or search_linkedin instead of search_semantic from the start.

Output format

Numbered list. Each line is: N. Title — URL — short why-relevant (one phrase, max 12 words).

The "why" comes from category, source, and tags — not invention. If a hit's title is bare/generic, lean on tags or source.

Do not add a leading "Here's what I found" or any preamble. Get to the list.

Do not invent URLs. Every URL you emit must appear in the hit you cite.

Do not truncate URLs. Full URL or nothing.

When to bail out

If search_semantic returns 0 hits AND the fallback returns 0 hits, say so plainly:

No matches in OpenMark for <query>. Try a broader term, or use openmark-deep-research for cross-source expansion.

Don't apologise. Don't recommend WebFetch — that's deep-research territory.

Example

User: "find my bookmarks on prompt caching"

1. search_semantic(query="prompt caching", n=12) → 12 hits.
2. Render:

1. Prompt Caching with Claude 3.5 — https://docs.anthropic.com/.../prompt-caching — official docs, AI Tools
2. Reducing Cost with Prompt Caching — https://blog.anthropic.com/... — engineering blog post
3. How Vercel uses prompt caching — https://vercel.com/blog/... — production case study
...

That's it. Done in one tool call. Move on.