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MACP Research Assistant

Tracks and recalls AI research across multiple assistants using the MACP protocol.

Updated Aug 23, 2026
Search Tags:#research#knowledge management#ai#provenance#traceability#macp
One-click install
npx skills add https://github.com/creator35lwb-web/macp-research-assistant --skill macp-research-assistant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: MACP Research Assistant
Source: https://github.com/creator35lwb-web/macp-research-assistant/tree/main
Command: npx skills add https://github.com/creator35lwb-web/macp-research-assistant --skill macp-research-assistant

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, jsonschema, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the problem of lost context, forgotten insights, and scattered citations when conducting research using multiple AI assistants.

Core Features & Use Cases

  • Track Research: Log every paper, insight, and citation with AI attribution.
  • Recall Knowledge: Search your entire research history with natural language queries.
  • Coordinate AI: Document handoffs between AI assistants for seamless collaboration.
  • Use Case: A researcher uses this Skill to track papers discovered by Hugging Face, analyzed by Claude, and cited in their project, ensuring complete traceability and easy recall of all learned information.

Quick Start

Use the MACP Research Assistant skill to discover papers on AI alignment.

Frequently Asked Questions about MACP Research Assistant

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

FAQPage Schema
How do I track AI research across multiple AI assistants to prevent lost context?▼

To track AI research across multiple assistants, you can automate logging papers, insights, and citations using the MACP protocol. This ensures complete provenance and traceability, preventing forgotten insights and scattered citations during multi-agent workflows.

What is provenance and traceability in multi-agent research workflows?▼

Provenance and traceability in multi-agent research workflows document the origin and handoffs of AI-powered insights and citations. Logging these elements via the MACP protocol ensures complete attribution and prevents lost context when coordinating multiple AI assistants.

Can I search my entire research history using natural language queries?▼

Yes, you can search your entire research history using natural language queries. This feature allows you to recall logged papers, AI-powered insights, and citations, ensuring easy retrieval of all learned information across multiple assistants.

Does this research tracking tool work with Python and the requests library?▼

Yes, this research tracking tool works with Python and requires the requests library. It uses Python scripts along with jsonschema to automate the multi-agent tracking, logging, and recall of research insights via the MACP protocol.

How do I document handoffs between AI assistants for seamless collaboration?▼

You document handoffs between AI assistants by using the MACP protocol to log every transition, paper, and insight. This coordinates AI collaboration, ensuring multi-agent workflows maintain complete traceability and prevent lost context.

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