docs-retrieval

Retrieve local ai-docs documentation and format structured context blocks for sub-agents.

8|1|Updated Jul 11, 2025

Search Tags:#documentation #context #libraries #patterns #retrieval #embedding #local-docs

Ready to run

npx skills add https://github.com/ViperJuice/treesitter-chunker --skill docs-retrieval-viperjuice

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: docs-retrieval
Source: https://github.com/ViperJuice/treesitter-chunker/tree/main/.ai-dev-kit/skills/docs-retrieval
Command: npx skills add https://github.com/ViperJuice/treesitter-chunker --skill docs-retrieval-viperjuice

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Retrieve and organize relevant local documentation to inform AI-driven tasks, reducing time spent searching and increasing context quality.

Core Features & Use Cases

Local-first retrieval from ai-docs to gather library docs and pages
Targeted page summarization to minimize token use while preserving key insights
Multi-library context consolidation for cross-cutting tasks

Quick Start

Ask the AI to retrieve targeted local docs and return a concise context block ready for sub-agent use

Frequently Asked Questions about docs-retrieval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I retrieve local documentation context for AI-driven tasks?▼

Retrieve local documentation context by enforcing a local-first lookup from the ai-docs hierarchy, formatting results into structured context blocks for sub-agents. This approach prioritizes targeted page summaries to inform AI-driven tasks efficiently.

What is the best way to consolidate multi-library context for cross-cutting development tasks?▼

Multi-library context consolidation gathers library docs and pages from local storage into a single context block. This minimizes token use while preserving key insights needed for cross-cutting development tasks.

Can I minimize token usage when feeding library documentation to sub-agents?▼

Yes, token-budget awareness is maintained by prioritizing targeted page summaries during local doc retrieval. This minimizes token use while preserving key insights required by sub-agents.

Does local-first documentation retrieval work for debugging errors and seeking library information?▼

Local-first documentation retrieval works for implementing features, debugging errors, and seeking library information. It enforces local-first lookup from the ai-docs hierarchy to inform tasks directly.

How do I prepare structured context blocks from local docs for sub-agent use?▼

Prepare structured context blocks by retrieving targeted local docs and returning a concise summary. The system formats results into structured context blocks ready for sub-agent use with token-budget awareness.

What are the limitations of local-first retrieval patterns for documentation?▼

Local-first retrieval patterns are limited to the documentation available within the local ai-docs hierarchy. If specific library pages or patterns are not stored locally, the retrieval process cannot access external documentation.