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/research — Exhaustive Investigation

The /research workflow activates maximum-depth investigation mode. This is NOT a quick lookup — it’s exhaustive, multi-source research that follows rabbit holes 3+ levels deep.

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When to Use

  • Market research (competitors, industries)
  • Regulatory deep-dives (MAS, legal frameworks)
  • Academic literature surveys
  • Due diligence investigations
  • Complex multi-stakeholder problems
  • Anything where surface answers are insufficient

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Behavior

When /research is invoked, Athena goes maximum depth:

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Depth Levels (All Mandatory)

Layer 1: Initial Survey (5-10 searches)
  • Run 5-10 related web searches covering different angles
  • Include: definitions, recent news, academic sources, industry sources, contrarian views
  • Save all source URLs for citation
Layer 2: Source Deep-Dive (Read top sources)
  • Use read_url_content on the 5-10 most promising sources
  • Extract key claims, data points, quotes
  • Flag contradictions between sources
Layer 3: Follow the Rabbit Hole
  • Identify references/links within sources
  • Follow 2-3 levels of secondary references
  • Surface information the initial searches missed
Layer 4: Cross-Domain Synthesis
  • Connect findings across different domains (legal, economic, psychological, technical)
  • Identify isomorphic patterns
  • Map stakeholder incentives
Layer 5: Adversarial Stress-Test
  • Steelman the opposing view
  • What would a critic say about these findings?
  • What’s missing? What’s the blind spot?
Layer 6: Actionable Distillation
  • Compress findings into decision-relevant format
  • Clear recommendations with confidence levels
  • Deposit key insights to Codex

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Output Format

Every research output includes these mandatory sections: 
## Research Target
[What we're investigating]

## Executive Summary
[3-5 bullet points of key findings]

## Source Inventory
| # | Source | Type | Key Claim | Confidence |
|---|--------|------|-----------|------------|
| 1 | [URL]  | [Academic/News/Industry/Gov] | [Claim] | [H/M/L] |
...

## Deep Analysis
[Full analysis with all phases]

## Contradictions & Gaps
[Where sources disagree, what's unknown]

## Rabbit Hole Findings
[Secondary/tertiary sources discovered]

## Actionable Recommendations
[What to DO with this information]

## Codex Deposit
[What should be saved permanently]

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Guardrails

  • Minimum searches: 5 (no less)
  • Minimum sources read: 3 full articles
  • Maximum time: No limit (go as deep as needed)
  • Citation density: Every factual claim cited
  • Contradiction flagging: Mandatory

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Usage Example

User: /research What are the viable AI-powered trading education business models in SEA?

Athena:
[Runs 8 searches: competitors, regulations, pricing models, customer segments,
tech stack, failure cases, success cases, adjacent industries]
→ Reads 6 full articles
→ Follows 4 secondary references
→ Synthesizes into full analysis
→ Deposits key findings to Codex

## Research Target
AI-powered trading education business models in Southeast Asia

## Executive Summary
- Market size: $2.1B SEA fintech education (2024), 18% CAGR
- Regulatory: MAS requires licensing for financial advice (not education)
- Top models: SaaS subscription (65%), freemium (25%), B2B2C (10%)
- Key competitors: StashAway Learn (free), FSMOne Academy (hybrid)
- Risk: Differentiation challenge in crowded market

[... continues with full analysis ...]

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Triple Crown Mode (DEFCON 1)

For maximum-stakes research, combine all three power modes: 
/think /search /research
This activates Nuclear Research Mode:
  • 10-20+ exhaustive searches
  • Deep-read 5-10 full articles
  • Follow rabbit holes 3+ levels
  • Full Phase 0-VII analysis on synthesized findings
  • Adversarial stress-test (steelman + red-team)
  • Confrontation phase on conclusions
  • Permanent deposit to Codex
  • Multi-iteration refinement if gaps found
Expect extended response time and high token usage, but maximum information density. Use Case: Life-altering decisions, major financial moves, complex multi-stakeholder problems, when “I need to know EVERYTHING before I act.”

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Anti-Patterns (Lies of Omission Prevention)

Each model has access to a subset of sources — any one will leave out information. To prevent this:

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Pre-Search Checklist

  1. Broad First, Narrow Later
    • ❌ “Find A21 LED bulbs with 15,000+ lumens” ← Too specific, misses variants
    • ✅ “Survey high-lumen LED bulbs, then filter” ← Catches edge cases
  2. Generate Prompt → Refine → Execute
    • Before running 5-10 searches, first draft the research plan
    • Ask: “What angles might I miss?”
    • Then execute the refined plan
  3. Date Range Awareness
    • ❌ “Only 2025 releases” ← Misses Dec 2024 that’s still relevant
    • ✅ “Most recent as of [date]” ← Captures boundary cases
  4. Multi-Source Triangulation
    • If high-stakes: run parallel queries on multiple search engines
    • Compare what each surface vs. omits
    • Synthesize across sources

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Comparison

ModeSearchesSources ReadDepthPhasesTime
/search2-30-2MediumOptionalFast
/think0-10High (reasoning)AllMedium
/research5-10+3-10+MaximumAll + Rabbit HoleExtended

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