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SKILL.md
In-Depth Research Methodology
Overview
In-depth research goes beyond surface-level literature review to conduct exhaustive, multi-source investigations that synthesize evidence from academic papers, grey literature, industry reports, datasets, and primary sources. This methodology is used when a research question requires comprehensive coverage — for systematic reviews, policy briefs, competitive analyses, or foundational literature surveys in a new research direction.
The 5-Phase Investigation Framework
Phase 1: Scope Definition (10% of effort)
Before searching, define boundaries explicitly:
## Research Brief Template
**Central Question**: [One sentence, specific and falsifiable]
**Sub-Questions** (3-5):
1. [Decomposed aspect 1]
2. [Decomposed aspect 2]
3. [Decomposed aspect 3]
**Inclusion Criteria**:
- Time range: [e.g., 2018-present]
- Languages: [e.g., English, Chinese]
- Document types: [peer-reviewed, preprints, reports, patents]
- Disciplines: [e.g., CS, cognitive science, linguistics]
**Exclusion Criteria**:
- [Opinion pieces, blog posts without data]
- [Studies with n < 30 unless qualitative]
- [Duplicate publications of same study]
**Expected Deliverable**: [Literature review / Evidence map / Policy brief / State-of-art report]
**Depth Target**: [Exhaustive / Representative / Exploratory]
Phase 2: Multi-Source Collection (30% of effort)
Search systematically across source tiers:
Tier
Source Type
Examples
Purpose
1
Academic databases
OpenAlex, PubMed, Scopus, Web of Science
Peer-reviewed primary research
2
Preprint servers
arXiv, bioRxiv, SSRN, medRxiv
Cutting-edge, not yet reviewed
3
Grey literature
WHO reports, World Bank, NBER working papers
Policy and institutional knowledge
4
Patents and standards
Google Patents, USPTO, IEEE standards
Technical implementations
5
Data repositories
Zenodo, Figshare, Kaggle, ICPSR
Raw data and reproducibility
6
Expert knowledge
Conference talks, interviews, personal communication
Tacit knowledge, emerging trends
Search strategy per source:
For each source:
1. Construct 3-5 query variants (synonyms, related terms, translated terms)
2. Apply inclusion/exclusion filters
3. Record: query string, date, results count, relevant hits
4. Download and tag all relevant items
5. Snowball: check references of key papers (backward) and citing papers (forward)
Phase 3: Source Evaluation (20% of effort)
Rate each source on a standardized evidence hierarchy:
Level 1: Systematic reviews and meta-analyses
Level 2: Randomized controlled trials / controlled experiments
Level 3: Cohort studies / quasi-experimental designs
Level 4: Case-control studies / cross-sectional surveys
Level 5: Case reports / case series / expert opinion
Level 6: Anecdotal evidence / grey literature without methodology
Credibility checklist per source:
□ Author credentials and affiliation
□ Publication venue (impact factor, peer-review process)
□ Methodology transparency (can you replicate it?)
□ Sample size and representativeness
□ Conflict of interest disclosure
□ Recency (is the data still relevant?)
□ Citation count and reception (supportive vs. critical citations)
□ Consistency with other sources (does it converge or contradict?)
Phase 4: Evidence Synthesis (30% of effort)
Organize findings into structured artifacts:
Evidence Matrix
Finding
Source(s)
Evidence Level
Strength
Notes
LLMs improve code quality by 20-40%
[A], [B], [C]
Level 2-3
Strong (convergent)
Effect varies by task complexity
Developers trust AI suggestions less for security-critical code
[D], [E]
Level 4
Moderate
Small sample sizes
No significant effect on debugging time
[F]
Level 2
Weak (single study)
Contradicts [A] — needs reconciliation
Contradiction Log
When sources disagree, document systematically:
## Contradiction: Effect of X on Y
**Position A**: X increases Y (Smith 2023, Jones 2024)
- Evidence: RCT with n=500, effect size d=0.4
- Context: University students, controlled setting
**Position B**: X has no effect on Y (Lee 2024)
- Evidence: Field study with n=1200, p=0.34
- Context: Industry practitioners, naturalistic setting
**Resolution hypothesis**: The effect is moderated by expertise level.
Position A's sample (students) shows the effect;
Position B's sample (practitioners) does not.
→ Need: Study that measures expertise as a moderator.
Compile findings into the target deliverable format:
For a Literature Review:
Organize by themes (not chronologically)
Synthesize across sources (not paper-by-paper summaries)
Identify gaps explicitly ("No studies have examined...")
State implications for your research
For a State-of-the-Art Report:
Current landscape with taxonomy
Key advances and timelines
Open problems and active debates
Future directions with evidence basis
For a Policy Brief:
Executive summary (1 paragraph)
Evidence summary (1-2 pages)
Policy options with trade-offs
Recommended action with justification
Iteration Protocol
Deep research is inherently iterative. After Phase 4, reassess:
After synthesis:
□ Are all sub-questions adequately answered?
□ Are there new sub-questions that emerged?
□ Are there critical gaps requiring additional search?
□ Are contradictions resolved or at least documented?
If gaps remain:
→ Return to Phase 2 with refined queries
→ Maximum 3 iteration cycles before declaring scope complete
→ Document what remains unknown (future work)
Quality Indicators
A well-executed in-depth investigation should demonstrate:
Breadth: Multiple source tiers consulted (not just Google Scholar)
Depth: Key papers read in full, not just abstracts
