skills/research/deep-research/in-depth-research-guide/SKILL.md
Structured methodology for conducting exhaustive multi-source investigations
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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.
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]
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)
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?)
Organize findings into structured artifacts:
| 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 |
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.
Visualize the landscape of your findings:
Central Question
├── Sub-Q1: [Strong evidence — 8 sources, convergent]
│ ├── Finding 1.1 (Level 2, 3 sources)
│ ├── Finding 1.2 (Level 3, 2 sources)
│ └── Finding 1.3 (Level 4, 3 sources)
├── Sub-Q2: [Mixed evidence — 5 sources, 1 contradiction]
│ ├── Finding 2.1 (Level 2, 2 sources)
│ └── Finding 2.2 ⚠️ CONTRADICTED by Finding 2.3
├── Sub-Q3: [Weak evidence — 2 sources, emerging area]
│ └── Finding 3.1 (Level 5, 2 sources)
└── Unexpected: [Theme that emerged during research]
└── Finding 4.1 (Level 3, 1 source) → needs further investigation
Compile findings into the target deliverable format:
For a Literature Review:
For a State-of-the-Art Report:
For a Policy Brief:
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)
A well-executed in-depth investigation should demonstrate:
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