Autoresearch
Turns Claude into an autonomous research agent that plans, searches, and synthesizes findings on a topic.
Before & after
Same prompt, same agent — the only difference is the skill.
Multi-agent systems is a broad topic in AI. I can do some research for you on different architectural approaches and what people are currently doing. There's a lot written about this, from academic papers to industry implementations. I could search for information on coordination patterns, communication protocols, and different design philosophies. It would probably help to break this down into more specific questions. Would you like me to focus on any particular aspect, like scalability, or specific use cases?
RESEARCH PLAN: Multi-Agent Systems Architectures SUB-QUESTIONS BREAKDOWN 1. What coordination patterns exist for multi-agent communication? - Search: 'multi-agent coordination protocols 2026' - Search: 'message broker vs direct communication agents' 2. Which agent architectures see production adoption? - Search: 'production multi-agent deployment patterns' - Search: 'swarm intelligence vs hierarchical agents' 3. What are current technical limitations and workarounds? - Search: 'multi-agent system scalability bottlenecks' - Search: 'token budget management multi-agent' 4. What does literature say about agent reliability? - Search: 'multi-agent consensus algorithms' - Search: 'Byzantine fault tolerance in AI agents' FINDINGS SYNTHESIS COORDINATION WINNERS: Pub/sub (Kafka, Redis) over direct HTTP; gossip protocols for resilience PRODUCTION PATTERNS: Hierarchical with specialist leaf nodes; central orchestrator remains bottleneck LIMITATIONS: Token budgets explode at 4+ agents; latency compounds; hallucinations replicate WORKAROUNDS: Tool-calling over agent-calling; shared memory cache (not regenerated per agent); deterministic task routing RELIABILITY: No byzantine consensus yet; majority voting on critical decisions; human-in-the-loop for drift detection RECOMMENDATION: Start with 2-agent specialist setup, add memory layer before adding 3rd agent SOURCES: 14 papers cited, 7 implementation guides, 3 video walkthroughs linked
About this skill
name: autoresearch description: Use when Turns Claude into an autonomous research agent that plans, searches, and synthesizes findings on a topic.
Autoresearch
Autoresearch, shared publicly by a renowned AI researcher, gives Claude a structured research loop: break a question into sub-questions, search for evidence on each, and synthesize the findings into a coherent write-up, without needing the user to manually chain each step.
What you get
- An autonomous multi-step research loop covering planning, search, and synthesis.
Customize your output
- Adjust the research depth, source preferences, or output format.
Example output
A structured research write-up with sourced findings on the requested topic.
Best for
Anyone using Claude Code for open-ended research tasks who wants less manual step-chaining.
SKILL.md preview
---
name: autoresearch
description: Use when a topic needs autonomous, multi-step research — breaking a question into sub-questions, gathering evidence, and synthesizing a coherent write-up without manual step chaining.
version: 1.0.0
category: Research & Data / Research
author: AgentVolt
license: proprietary
tags:
- research-data
- research
- standard
---
# Autoresearch
Runs a structured research loop: decompose the question, gather evidence per sub-question, then synthesize into one coherent write-up.
## When to use
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