research

Orchestrate parallel web, docs, and code research with confidence-weighted synthesis.

8|1|Updated Jul 11, 2025

Search Tags:#research #multi-source #synthesis #docs #confidence #code #web

Ready to run

npx skills add https://github.com/Consiliency/treesitter-chunker --skill research-consiliency

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research
Source: https://github.com/Consiliency/treesitter-chunker/tree/main/.ai-dev-kit/skills/research
Command: npx skills add https://github.com/Consiliency/treesitter-chunker --skill research-consiliency

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates multi-source research by orchestrating parallel collection and confidence-weighted synthesis from web, documentation, and code sources, delivering structured insights with traceable sources.

Core Features & Use Cases

Parallel Research: Simultaneously gather information from diverse sources (Web, Docs, Code) to save time and reduce bias.
Confidence-based Synthesis: Cross-validate findings with a built-in confidence framework to highlight consensus and gaps.
Structured Outputs: Produce synthesised summaries, executive briefs, and source maps suitable for decision-making and documentation.
Use Case: A product team needs a quick competitive landscape with evidence from latest articles, standards docs, and implementation patterns across languages.

Quick Start

Run a standard-mode query with a target topic, for example: /ai-dev-kit:research standard "latest Tree-sitter compatible parsers patterns"

Frequently Asked Questions about research

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

FAQPage Schema

How do I gather information from multiple sources at once for research?▼

Multi-source research coordinates parallel collection from web, documentation, and code sources simultaneously. This approach saves time by pulling findings from diverse channels at once rather than checking each source sequentially, reducing research bias and surfacing consensus across sources.

What's the best way to synthesize findings from web, docs, and code into a structured summary?▼

Confidence-based synthesis cross-validates findings across sources and weights results by agreement level. It produces structured outputs like executive briefs and source maps that highlight where sources align, where gaps exist, and which evidence is strongest for decision-making.

Can I run research queries across documentation and code repositories together?▼

Yes. Multi-source research orchestrates web, documentation, and code sources in parallel queries. This lets you gather implementation patterns, standards documentation, and real-world examples in a single structured workflow rather than searching each repository separately.

How do I verify research findings across different sources?▼

Confidence-based synthesis cross-validates findings by comparing results across web, docs, and code sources. The framework highlights consensus between sources and flags gaps, so you see which conclusions are supported by multiple sources versus isolated claims.

What kind of output does multi-source research produce?▼

Multi-source research delivers structured outputs including synthesised summaries, executive briefs, and source maps with traceable evidence. Each output includes confidence scoring and source attribution so findings are documented for reporting and decision-making.