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v1.7.0 — Proactive Library Usage & Refined Positioning

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@barkain barkain released this 28 Mar 16:55
· 16 commits to main since this release
v1.7.0
64f6b09

What's New

Proactive Library Usage

  • The agentlib-knowledge skill now auto-triggers on domain-specific coding tasks — not just explicit research questions
  • Expanded trigger patterns: writing code involving domain-specific parameters, protocols, standards, or configurations; encountering technical terms from the library; methodology and best practices questions
  • Narrowed exclusions: "general programming tasks" instead of blanket "code editing" exclusion, so domain-specific coding triggers the library while generic tasks don't

Refined Positioning & Documentation

  • New README pitch: "Curate your knowledge library. Your agent works from sources you trust." — focused on curation, persistent knowledge, proactive integration, and citable answers
  • Replaced the O(n²) token-efficiency opening with a user-centric value proposition
  • Four key pillars: your sources/your curation, always available, proactive not reactive, citable answers

Demo Screenshots

  • Added proactive query demo: agent automatically consults library when asked about BOM maturity dimensions
  • Added collapsible library-researcher navigation view showing the full tool chain (NAVIGATION.md → concepts.json → chunks → synthesized answer)

Navigation Diagram

  • Redesigned "How agents navigate the library" diagram from vertical ASCII to horizontal Mermaid flowchart — wider and more readable

Hero Animation

  • README hero image replaced with looping GIF demo (800px, 15fps)

Session Summary

This release caps a productive session that also shipped v1.5.0 (content-aware chunking, PDF table/image extraction) and v1.6.0 (parallel ingestion, auto-recovery, large book support). Together these three releases transform AgentLib from a basic chunking pipeline into a production-ready knowledge layer for AI agents.

Across v1.5.0–v1.7.0:

  • Parser: PDF tables extracted as clean markdown, images extracted with vision-based summarization
  • Chunker: Tables and code fences kept atomic with smart splitting
  • Ingestion: 10x faster via parallel summarization, auto-recovery on failure, batched concept extraction
  • Agent integration: Proactive library usage without explicit user commands
  • Testing: 98 tests passing (up from ~34 at session start)
  • Issues: #8 implemented, #9-12 closed as not needed, #16 fixed

Full Changelog: v1.6.0...v1.7.0

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