Profile My skills Saved Collections Edit profile Submit a skill

Nestbrowse Info Seeking

Scale information-seeking agents via nested architecture separating outer reasoning from inner page exploration. Minimal toolkit (search, visit, click, fill) handles dynamic web interactions, inner loop filters content before context injection, multi-task learning trains outer/inner jointly—enabling 4B models to match larger competitors.

adu2021 Updated
Productivity

File contents

Overview

Nested architecture decouples reasoning from page content extraction.

Core Technique

Nested Loop Structure:

# Outer loop: reasoning
reasoning = outer_agent.think(state)
tool_call = outer_agent.select_tool()

# Inner loop: extract relevant content
relevant_content = inner_agent.extract(page, goal)

# Return minimal content to reasoning

When to Use

Use when: Information-seeking agents, large-scale web interaction, efficiency critical.

References

  • Nested outer/inner loop architecture
  • Minimal browser toolkit
  • Content filtering before context injection

adu2021/skillxiv/tree/main/skills/skillxiv-v0.0.2-claude-opus-4.6/nestbrowse-info-seeking commit c40d2cfaee

Browse category

Frequently asked questions

npx skillmds@latest add adu2021/nestbrowse-info-seeking