Research

Overview

Structured research process that works for coding tasks (evaluating libraries, understanding APIs) and non-coding work (market research, content creation, learning new domains). Produces organized, actionable artifacts.

Process

1. Scope

Define what we're researching and why:

• What question(s) need answering?

• What decisions will this inform?

• What's out of scope?

• What format should the output take?

Confirm scope with user before proceeding.

2. Prior Work

Before gathering new information, check for existing knowledge:

• Search any available knowledge base for prior research, decisions, or patterns on this topic

• Review project docs, CLAUDE.md, and existing codebase patterns

• Summarize what's already known and identify gaps that still need research

• Note any prior work that's outdated or low confidence and may need updating

If nothing relevant exists, proceed to Gather.

3. Gather

Collect information from available sources:

• Existing knowledge: Prior decisions, patterns, and research (from step 2)

• Code: Read relevant files, grep for patterns, check dependencies

• Web: WebSearch for docs, articles, comparisons. WebFetch for specific pages

• Docs: Framework docs via context7 MCP if available

Use parallel subagents for independent research threads.

4. Organize

Structure findings:

• Group by theme, not by source

• Flag contradictions between sources and existing knowledge

• Note confidence level (verified, likely, uncertain)

• Separate facts from opinions

5. Synthesize

Draw conclusions:

• Answer the original questions directly

• Provide recommendation with reasoning

• List trade-offs explicitly

• Note what remains unknown

6. Artifact

Deliver in the agreed format:

• Decision: Recommendation + alternatives + trade-offs table

• Comparison: Feature matrix with weighted criteria

• Summary: Key findings + action items

• Brief: Background + analysis + recommendation (for sharing with others)

7. Persist

After the user accepts findings, save them for future retrieval:

• If a knowledge base is available, deposit the findings with appropriate metadata (type, tags, confidence, related notes)

• If research supersedes prior work, mark the old artifact as outdated

• Skip for trivial lookups or if the user declines

Quick Reference

Research Type Key Sources Typical Output

Library eval npm/pypi, GitHub stars/issues, docs Comparison matrix

Bug investigation Code, logs, issue trackers Root cause + fix options

Architecture Existing decisions, code, web patterns Decision document

Content/topic Web search, articles, papers Structured summary

API integration API docs, examples, SDKs Integration guide

Common Mistakes

• Starting to gather before defining scope (wastes time on irrelevant info)

• Skipping prior work check (re-researching what's already known)

• Presenting raw findings without synthesis (user wants answers, not data dumps)

• Not confirming scope with user (researching the wrong thing)

• Single-source conclusions (verify across multiple sources)

• Forgetting to persist findings (research evaporates after the session)