Auto Research

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 6ea1a6ff2160 · 14 Oct 2026Report an issue or request a re-scan

• Publisherhuytieu GitHub userOwner of the repository this skill is read from
• Source folderhuytieu/COG-second-brain/.claude/skills/auto-researchThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict6ea1a6ff2160A new audit runs when the source changes
• Repository statusNot archivedLast push 11 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis

---
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:

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

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 6ea1a6ff2160

Questions about Auto Research

• github.com/huytieu/COG-second-brain/tree/main/.claude/skills/aut — the audited source at commit 6ea1a6ff2160 and every finding aboveObserved · 14 Oct 2026

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