Research Methodology

Research topic: $ARGUMENTS

Overview

Help turn research requests into thorough codebase understanding through natural collaborative dialogue.

Start by understanding what the user needs to learn, then ask questions one at a time to refine the scope. Once you understand what you're researching, explore the codebase systematically, presenting findings in digestible sections and validating as you go.

The Iron Law

Ask questions BEFORE exploring code.

Do not touch the codebase until the problem is understood. Resist the urge to immediately search for files or read code.

Phase 1: Understanding the Request

Your first action must be asking a clarifying question.

Do NOT:

• Read any files
• Search the codebase
• Use Glob or Grep
• Explore anything
• Make assumptions about what the user wants

Ask questions one at a time using AskUserQuestion:

• Prefer multiple choice questions when possible, but open-ended is fine too
• Only one question per message
• If a topic needs more exploration, break it into multiple questions

Focus on understanding:

• Purpose: What are they trying to accomplish? (build, change, fix, learn)
• Specifics: What exactly should happen or change?
• Scope: How big is this? (one file, multiple files, architectural)
• Constraints: Any requirements around performance, compatibility, security?
• Context: Have they already looked at anything or have hunches?

When you believe you understand, confirm:

Summarize your understanding and ask if it's accurate before proceeding. If anything needs clarification, ask follow-up questions.

Phase 2: Exploration

Only proceed after confirming understanding with the user.

Locate Relevant Files

Use the file-finder agent to locate files relevant to the research objective:

Task tool with subagent_type: "file-finder"
Prompt: "Find files related to [topic from interrogation]. Goal: [user's stated purpose]"

The file-finder will return a structured report with:

• Core files to examine first
• Supporting files and utilities
• Test files
• Configuration files
• Suggested reading order

Explore the Discovered Files

Use TaskCreate to track exploration based on the file-finder report. Create one task per file category (core, supporting, test, config) and update via TaskUpdate as you examine each.

Examine core files first:

• Read files in the suggested order
• Understand the main flow and architecture
• Note patterns and conventions

Trace relevant data flow:

• Follow data through the identified files
• Identify inputs, transformations, outputs
• Document state changes and side effects

Review supporting files:

• Examine utilities and helpers
• Note reusable patterns
• Document conventions for testing and error handling

Identify technical constraints:

• External dependencies
• Performance considerations
• Security implications

Deepen Understanding with LSP

After identifying relevant files, use the LSP tool for deeper structural understanding:

• goToDefinition — trace how functions and types connect across files
• findReferences — understand where symbols are used throughout the codebase
• documentSymbol — get a structured overview of a file's exports and structure
• incomingCalls / outgoingCalls — map call hierarchies to understand data flow

If LSP is unavailable (no configured language server), skip this step and rely on Grep-based content search.

Research External Context (When Needed)

For single-page lookups (e.g., checking a library's API docs or a specific GitHub issue), use WebFetch directly instead of spawning a web-researcher agent. Reserve the web-researcher for multi-source research requiring synthesis.

If codebase exploration reveals external factors that need broader investigation, use the web-researcher agent:

Task tool with subagent_type: "web-researcher"
Prompt: "[specific research question about external topic]"

Use web research for:

• Understanding external libraries or APIs the code depends on
• Comparing implementation approaches or best practices
• Investigating third-party service documentation
• Researching security implications or known issues

The web-researcher returns findings with source citations and confidence assessments.

Present findings incrementally:

• Share what you find in digestible sections
• Ask if findings align with expectations or if you should look elsewhere
• Be ready to redirect based on feedback

Phase 3: Document Findings

Create research document at: docs/plans/YYYY-MM-DD-<topic>-research.md

(Use today's date in YYYY-MM-DD format)

# Research: <Topic> (YYYY-MM-DD)

## Problem Statement

[What the user wants to accomplish]

## Requirements

[Key requirements gathered during interrogation]

## Findings

### Relevant Files

| File            | Purpose     | Key Lines |
| --------------- | ----------- | --------- |
| path/to/file.ts | Description | 42-87     |

### Existing Patterns

[Patterns discovered that inform implementation]

### Dependencies

[External and internal dependencies]

### External Research

[Findings from web research, if conducted - include sources]

### Technical Constraints

[Limitations discovered during exploration]

## Open Questions

[Questions that remain unanswered]

## Recommendations

[Initial thoughts on approach]

Phase 4: Transition

Ask what the user wants to do next:

• Create an implementation plan
• Continue researching
• End for now

Questioning Techniques

Funnel questions - Start broad, narrow based on answers:

1. "What are you trying to accomplish?" (broad)
2. "Which part is most important?" (narrowing)
3. "What would success look like?" (specific)

Assumption surfacing - Make assumptions explicit:

I'm assuming this needs to work with the existing auth system. Is that correct?

Trade-off questions - When multiple approaches exist:

There's a trade-off: Option A is faster to build but less flexible. Option B is more flexible but more complex. Which matters more here?

Clarification through examples - When requirements are vague:

Can you give me an example of what you'd expect to happen?

Anti-Patterns

Key Principles

• One question at a time - Don't overwhelm with multiple questions
• Multiple choice preferred - Easier to answer than open-ended
• Confirm before exploring - Validate understanding first
• Incremental findings - Present discoveries in sections, validate each
• Be flexible - Go back and clarify when something doesn't fit