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How /last30days Understands Your Topic Before It Searches: Intelligent Entity Resolution Explained

Cowrie
Cowrie
Dev @ Bswen

Problem

When I searched for “Paperclip” across social platforms, I got results about paper clips, office supplies, and any mention of the word. I missed the actual Paperclip AI product, its creator, and the relevant subreddits. Keyword search across social platforms returns noise — it matches the string, not the entity.

What Happens

/last30days solves this with a pre-research phase that resolves entities before the search begins. Instead of searching for the literal string “Paperclip”, it discovers:

  • Who is the person behind it (X handle, GitHub username)
  • What are the related communities (subreddits, YouTube channels)
  • What are the related products and competitors

Here’s the flow:

Pre-research entity resolution flow
User query: "Paperclip"
         │
         ▼
┌─────────────────────┐
│  Pre-Flight Step     │  WebSearch resolves:
│  (SKILL.md Step 0.5)│  - X handle → @dotta
│                     │  - GitHub → paperclipai
│                     │  - Subreddits → r/ClaudeCode
│                     │  - Founder's related handles
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Planner             │  assigns search intents:
│  (planner.py)        │  - factual, product, opinion
│                     │  - maps to source priorities
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Engine fans out     │  uses resolved handles
│  to 10+ sources      │  instead of keywords
└─────────────────────┘

How the Resolution Works

The resolution happens in four stages defined in the SKILL.md:

Step 0.5 — Pre-Flight Resolution: WebSearch discovers the primary handles for a person, product, or topic. For “Peter Steinberger”, it finds @steipete on X, steipete on GitHub, and r/ClaudeCode.

Step 0.5b — GitHub Username: When the topic is a person, GitHub user resolution is mandatory. It finds the correct GitHub handle even when it differs from the X handle.

Step 0.5c — GitHub Repos: Identifies active repositories, their star counts, and recent releases.

Step 0.55 — Auto-Resolve: The full entity profile. Everything feeds into targeting flags for the engine:

Engine flags populated by pre-research
--x-handle=steipete
--x-related=OpenAI,TechCrunch
--github-user=steipete
--subreddits=ClaudeCode,ClaudeAI,openclaw

Why This Matters

Entity-aware search finds content that keyword search never could. “Paperclip” resolves to @dotta and OpenClaw. “Peter Steinberger” resolves to @steipete on X and steipete on GitHub. The resolution is bidirectional — person to company, product to founder, name to GitHub profile.

Compare the two approaches:

Keyword vs entity-aware search
Without pre-research (keyword only):
/last30days Paperclip --no-resolve
→ Matches any mention of "Paperclip" across all platforms


With pre-research (entity-aware):
/last30days Paperclip --auto-resolve
→ Resolves to @dotta, paperclipai GitHub, r/ClaudeCode
→ Searches each platform for these specific entities
→ Returns relevant results about Paperclip the AI product

Summary

In this post, I explained how /last30days’s pre-research phase transforms vague keyword searches into precise, entity-aware queries across 10+ platforms. The key takeaway is that the engine knows where to look before it looks — it resolves people, products, and communities before the first search even fires.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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