/auto-research

name: auto-research
description: Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
roles: [product-manager, engineering-lead, founder, all]
integrations: []

COG Auto Research Skill

When to Invoke

• User asks a strategic question requiring deep research
• User says "research", "auto-research", "investigate", "strategic analysis", "deep dive into [topic]"
• User wants to understand market forces, competitive dynamics, technology trajectories, or strategic options
• User needs evidence-based analysis with real sources to support decision-making

Inspired by Karpathy's autoresearch — but for strategic thinking instead of ML training.

Agent Mode Awareness

**Check `agent_mode` in `00-inbox/MY-PROFILE.md` frontmatter:**

• If `agent_mode: team` — use the full parallel agent execution strategy (5-7 agents). This skill benefits massively from team mode.
• If `agent_mode: solo` — run 2-3 sequential research passes with WebSearch/WebFetch, produce a lighter analysis without the full multi-thread structure.

Command: `/auto-research`

Input

The user provides a strategic question or topic as the command argument. Examples:

• "If foundation models commoditize, what happens to LLM wrapper companies like Katalon/Scout?"
• "Future of the testing industry as AI capabilities expand"
• "Should we build vs buy vs partner for our AI layer?"
• "What are the strategic options for Scout if OpenAI launches a testing product?"

---

Execution Strategy

Phase 1: Question Decomposition (Orchestrator — 2 minutes)

Break the user's strategic question into 5-7 **research threads** that together will provide a comprehensive answer. Each thread should be:

• **Independent** — can be researched in parallel
• **Specific** — has a clear research objective
• **Complementary** — together they cover the full strategic landscape

**Decomposition framework:** 1. **Market forces** — what macro trends drive this question? 2. **Historical precedent** — has this pattern played out before in other industries? 3. **Player analysis** — who are the key players and what are they doing? 4. **Technology trajectory** — where is the underlying tech heading? 5. **Customer behavior** — what do end-users actually want/do? 6. **Economic model** — what are the unit economics and value capture dynamics? 7. **Emerging tech & architectures** — what concepts, projects, or frameworks are still in development/discussion (pre-mainstream) that could be foundational? Research open-source projects, research papers, GitHub repos, Discord/forum discussions, conference talks, and early-stage tools that are relevant. Examples: novel agent architectures, new testing paradigms, experimental frameworks. These may not have polished docs — dig into READMEs, GitHub issues, Twitter/X threads, blog posts from builders, and academic preprints. 8. **Contrarian view** — what's the strongest argument against the consensus?

Not all threads apply to every question. Pick the 5-7 most relevant. **Thread 7 (Emerging tech) should ALWAYS be included** — the user specifically wants to stay ahead of concepts that aren't mainstream yet.

**Before spawning agents:** 1. Read relevant files from the vault for existing context:

• `05-knowledge/` for existing frameworks and mental models
• `04-projects/` for project-specific context if relevant
• Recent braindumps for the user's existing thinking on this topic

2. State the decomposition to the user so they can course-correct before agents launch

Phase 2: Parallel Deep Research (Spawn 5-7 Agents Simultaneously)

**CRITICAL: Launch ALL agents in a single message.** Use `run_in_background: true` for all agents.

Each agent gets a detailed prompt following this template:

You are a strategic research analyst investigating a specific thread of a larger strategic question.

MAIN QUESTION: [user's original question]
YOUR THREAD: [specific research thread]
EXISTING CONTEXT: [any relevant vault context]

RESEARCH METHODOLOGY:
1. WebSearch for 8-12 high-quality sources (prioritize: research reports, expert analyses, company filings, academic papers, industry publications — NOT listicles or superficial blog posts)
2. For each source found, WebFetch to read the full content and extract key arguments, data points, and frameworks
3. Look for CONFLICTING viewpoints — don't just confirm one narrative
4. Identify specific data points, statistics, and concrete examples
5. Note the credibility and potential bias of each source
6. FOR EMERGING TECH THREADS: Go beyond polished sources. Search GitHub repos (README, issues, discussions), Twitter/X threads from builders, Discord/forum discussions, conference talk summaries, arXiv preprints, and early blog posts. The goal is to surface concepts that are pre-mainstream but technically promising. For each concept found, assess: maturity level, technical approach, relevance to the user's use case, and what it would take to adopt/integrate.

OUTPUT FORMAT (return ALL of this):

## Thread: [thread name]

### Key Findings (3-5 bullet points)
- Finding with source attribution

### Evidence & Data Points
- Specific statistics, market data, examples with sources

### Expert/Notable Perspectives
- Named perspectives from credible voices

### Implications for [user's context]
- What this means specifically for the user's situation

### Confidence Level
- HIGH / MEDIUM / LOW with reasoning

### Sources
- Numbered list of actual URLs consulted

**Agent naming convention:** `research-[thread-slug]` (e.g., `research-market-forces`, `research-historical-precedent`)

Phase 3: Synthesis (Orchestrator — after all agents complete)

Once all agents return, synthesize into a single strategic analysis document:

Document Structure:

---
type: strategic-research
domain: [auto-detect from question]
date: YYYY-MM-DD
question: "[original question]"
thread