Morty AgentSkill

Ultra Deep Research

Multi-agent, multi-provider research that outperforms single-pass AI research tools. Research quality is an architectural outcome, not a prompting problem.

10
Pipeline Steps
4
Search Providers
5–10
Parallel Agents
~$0.40
Avg Cost

⚖️ Three Laws

1
Never synthesize from memory when evidence is required.
2
Every important claim requires 2+ independent sources.
3
Always know what you don't know — track gaps explicitly.

🎯 Research Modes

Quick
1–2 agents
1–2 search rounds
30–90 seconds
~$0.05
📊
Standard
3–5 agents
2–3 rounds per stream
3–8 minutes
~$0.40
🔍
Exhaustive
5–10 agents
3–5 rounds + recursive
8–20 minutes
~$1.50
📡
Monitoring
2–3 agents
Delta-focused
2–5 minutes
delta

🔗 The 10-Step Pipeline

00
Classify Research Mode
Analyze query complexity to select Quick, Standard, Exhaustive, or Monitoring. Determines agent count, search depth, verification rigor, and budget.
01
Decompose the Query
Create a Research Plan with 3–12 independent workstreams, mandatory contrarian angle, and dependency mapping.
02
Route to Providers
Score providers (Tavily, Brave, Exa, Perplexity) per workstream. Assign primary + backup for cross-verification.
03
Spawn Parallel Subagents
Launch all independent workstreams as parallel Opus 4.6 subagents. Types: Scout, Extractor, Verifier, Contrarian, Entity.
04
Wait & Collect
Subagents auto-announce completion. No polling. All must return before proceeding.
05
Build Evidence Store
Deduplicate sources & claims. Score confidence (0–1). Map contradictions. Tag source tiers (1–4).
06
Gap Analysis & Recursive Deepening
Coverage → contradiction → diversity → freshness → adversarial checks. Spawn gap-fillers if needed. Up to 3 rounds.
07
Adversarial Challenge
Exhaustive mode: dedicated agent tries to break the thesis. Tests assumptions, searches for counter-evidence.
08
Synthesize Final Report
Opus 4.6 director reorganizes by theme. Inline citations, confidence assessment, mandatory contradictions section.
09
Output & Archive
Deliver report. Optional structured JSON for pipelines. Optional Obsidian save with YAML frontmatter.

🧭 Smart Provider Routing

Tavily
General research workhorse. AI-optimized results with citations. Site crawling & content extraction.
Brave
Fastest provider (<600ms). Independent non-Google/Bing index. Best for freshness and verification.
Exa
Semantic precision. 70M+ companies, 1B+ profiles. Academic papers. Grounded structured outputs.
Perplexity
Answer engine + raw search. Multi-query. Academic & SEC modes. Chain-of-thought reasoning.

📐 Architecture Flowchart

Ultra Deep Research — Full Pipeline Flowchart
Complete pipeline flow from query classification through recursive deepening to final synthesis

🏆 What Makes It Better

  • Multi-provider triangulation — Different search indexes surface different results. Agreement across independent sources = high confidence.
  • Recursive deepening — Doesn't stop after one pass. Identifies gaps and fills them with targeted follow-up agents (up to 3 rounds).
  • Adversarial testing — Actively tries to disprove its own conclusions. If the thesis survives, report credibility increases.
  • Structured evidence scoring — Every claim has a confidence score (0–1) and source tier (1–4). No vibes-based conclusions.
  • Mandatory uncertainty disclosure — Reports always include Contradictions & Open Questions. Its absence = low-quality research.
  • Opus-powered throughout — Opus 4.6 workers and director. No quality compromises — research is only as good as the agents gathering evidence.

🚫 Anti-Patterns

Spawning agents with vague directives like "look into this more"
Letting synthesis become summarization instead of reasoning over evidence
Trusting a single search provider for important claims
Skipping gap analysis — the highest-ROI step in the entire pipeline
Over-decomposing: 50 agents for a 3-agent question = noise and waste
Producing reports without the Contradictions & Open Questions section
Presenting vendor claims as facts without flagging source bias

🧪 Try It

Run a live research query — multi-provider, evidence-scored, adversarially tested

🔐

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