research-methodology

name	research-methodology
description	This skill should be used when starting any research task, decomposing a research query, planning research strategy, deciding how many sub-topics to investigate, scaling research effort to query complexity, determining when to stop researching, or dynamically re-planning based on intermediate findings. Covers query analysis, decomposition techniques (Self-Ask, Least-to-Most, DAG-based), effort scaling, plan representations, stopping criteria, and research anti-patterns.
version	1.2.0

Research Methodology

Effective research requires deliberate planning before execution. Without decomposition, complex queries overwhelm LLMs — the compositionality gap means models answer sub-questions correctly but fail to compose them into correct multi-hop answers, and this gap does not shrink with model scale alone (Press et al., EMNLP 2023).

Query Analysis

Before decomposing, analyze the query along three dimensions:

Complexity classification:

Level	Characteristics	Example	Approach
Simple	Single fact, one source sufficient	"What is the GAIA benchmark?"	Direct search, no decomposition
Moderate	2-4 facets, comparison or analysis	"How does LangGraph compare to CrewAI?"	2-4 parallel subtopics
Complex	Multi-faceted, requires synthesis across domains	"How should we architect a deep research agent?"	Full decomposition with dynamic replanning

Scope narrowing: If a query is vague or overly broad, ask 2-3 clarifying questions before researching. Model this on Claude's desktop deep research flow — refine scope before committing resources.

Questions to consider:

• What specific aspect matters most?
• What is the intended use of this research?
• Are there known constraints (domain, time period, technology)?

Decomposition Strategies

Decomposition strategy should emerge from the query, not from a preset template. The number of subtopics is a function of query complexity, not a fixed constant.

Self-Ask pattern — For multi-hop factual queries. Ask explicit follow-up sub-questions, answer each independently, then compose. Each sub-question becomes a natural insertion point for web search (Press et al., 2023).

Parallel decomposition — For queries with independent facets. Identify subtopics that can be researched simultaneously without dependency. ParallelSearch research shows 12.7% improvement on parallelizable questions using only 69.6% of LLM calls versus sequential approaches (Zhao et al., 2025).

Iterative discovery — For exploratory queries. Start with broad searches, discover subtopics from results, spawn follow-up searches based on what is found. The plan emerges from the research itself.

DAG-based decomposition — For queries with inter-dependent sub-questions. Model decomposition as a directed acyclic graph where some sub-questions depend on answers to others. MindSearch processes 300+ web pages in 3 minutes using this approach (Chen et al., ICLR 2025).

Choosing a Strategy

Query Type	Strategy	Why
"What is X?"	Direct search	Single-hop, no decomposition needed
"Compare X and Y"	Parallel decomposition	Independent facets, search simultaneously
"How does X work and what are its implications?"	Iterative discovery	Second part depends on first
"Comprehensive survey of X"	DAG-based	Multiple inter-dependent threads

Effort Scaling

Match research depth to query complexity. Over-researching simple queries wastes tokens; under-researching complex queries produces shallow results.

Complexity	Workers	Searches per Worker	Total Effort
Simple	1-2	3-5	Light
Moderate	3-4	5-10	Medium
Complex	5-8	10-20	Heavy

The number of workers emerges from decomposition — do not prescribe a fixed count before analyzing the query.

Dynamic Re-Planning

Research plans are hypotheses, not contracts. Re-plan when:

• Knowledge gaps emerge — Intermediate results reveal missing information not anticipated in the original plan
• Assumptions are invalidated — A planned subtopic turns out to be irrelevant or already well-covered by another subtopic
• New threads appear — Discovered information opens important sub-questions not in the original plan
• Sources conflict — Contradictions between sources require additional targeted searches to resolve

When re-planning, persist the updated plan externally (not just in context) to survive context window truncation.

Stopping Criteria

Combine multiple signals — no single criterion is sufficient:

1. Plan completion — All planned subtopics have been investigated
2. Diminishing returns — New searches yield information already covered
3. Budget limits — Maximum searches, tokens, or time reached
4. Gap check — Explicit review: "What important aspects remain uninvestigated?"
5. Sufficiency judgment — Can the research question be answered with current findings?

Stop when at least 3 of these 5 criteria are satisfied.

Research Anti-Patterns

Anti-Pattern	Symptom	Fix
Over-decomposition	15+ subtopics for a moderate query	Let complexity drive decomposition, not ambition
Under-decomposition	Single monolithic search for a complex query	Analyze facets before searching
Plan rigidity	Following the original plan despite contradicting evidence	Re-plan when assumptions break
Circular decomposition	Sub-questions that restate the original question	Each sub-question must be independently answerable
Premature depth	Deep-diving first subtopic before broad coverage	Breadth-first for initial pass, then depth

Reference Files

For detailed decomposition techniques and research:

• references/decomposition-techniques.md — Self-Ask, Least-to-Most, Plan-and-Solve, DAG-based decomposition with examples and research citations