Autonomous Evidence Research and Synthesis
Get a sourced research report that resolves conflicting evidence, exposes gaps, and answers your question.
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
You asked for research and got a stack of links, repeated summaries, and a confident conclusion that skips the hard parts. This skill takes a question through planning, evidence gathering, contradiction checks, and synthesis, then tells you what the evidence supports and what remains unresolved.
What it does
- Breaks broad questions into answerable subquestions tied to your decision or research goal.
- Chooses follow-up searches from evidence gaps instead of repeating the initial query.
- Traces reported claims to original documents and distinguishes independent evidence from repeated coverage.
- Reconciles disagreements by checking definitions, dates, populations, methods, and incentives.
- Attaches citations to specific findings and separates documented facts from interpretation.
- Stops when further research is unlikely to change the answer, while naming remaining uncertainties.
What you get
A synthesized report with a direct answer, sourced findings, a treatment of conflicting evidence, and the limits of the conclusion. For narrow requests, you get a focused answer without the full report structure.
Best for
Analysts, operators, writers, and researchers investigating broad or contested questions who want Claude to manage the research sequence without prompting each next step.
SKILL.md preview
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name: autonomous-evidence-research-and-synthesis
description: Investigates questions through an adaptive loop of subquestion planning, targeted retrieval, source verification, contradiction checks, and cited synthesis. Use when the user says "research this topic", "give me a sourced report", "keep digging until you can answer", "these sources contradict each other", "my research keeps turning into a pile of links", "what does the evidence actually show", or "find the gaps in this research".
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# Autonomous Evidence Research and Synthesis
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