research-methodology
Skill135 starsChanged 26 days agoMITView on GitHub
You must use this when matching research questions to appropriate designs, sampling strategies, or validity controls — or when a research problem is stuck and needs creative reframing (cross-domain analogies, first-principles deconstruction).
16 other projects use a version of this.
Adopted by
Most recently taken up by
- komluk/scaffoldingtheir own version, first seen Nov 7, 2026
- SWEStash/swe-workflow-skillstheir own version, first seen Nov 1, 2026
- irahardianto/awesome-agvtheir own version, first seen Oct 14, 2026
- The-Focus-AI/standardstheir own version, first seen Oct 12, 2026
- HughYau/pengsida-learning-research-skillstheir own version, first seen Oct 8, 2026
- onegreyonewhite/research-methodologytheir own version, first seen Sep 18, 2026
Used with
Projects with it also use context-engineering, security-audit and 1 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
mrgoonieSkillcontext-engineering
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
Also in 3 projects that use this
CloudflareSkillsecurity-audit
Security guidance and vulnerability review for codebases, APIs, services, CLI tools, libraries, and daemons. Use for security questions, focused reviews, vulnerability research, security audits, or pen tests. Run the complete workflow only for explicit codebase audit or pen-test requests, full/comprehensive/end-to-end reviews, or requested report artifacts.
Also in 3 projects that use this
BlockSkillfrontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
Also in 3 projects that use this
Other versions16 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 16
- VAMFI/claude-user-memory★ 206same name
- magnus919/agent-skills★ 172same name
- irahardianto/awesome-agv★ 157same name
- HughYau/pengsida-learning-research-skills★ 27same name
- komluk/scaffolding★ 16same name
- stupidZZ/skills★ 9same name
- KhaledSaeed18/dotclaude★ 5same name
- ntaffzii/Skill-Agents★ 4same name
- nic095layson/claude★ 1same name
- onegreyonewhite/research-methodology★ 1same name
6 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- llms-full.txtllms.txt
- llms.txtllms.txt
- academic-writingSkill
- critical-analysisSkill
- ethics-reviewSkill
- grant-writingSkill
- hypothesis-testingSkill
- literature-reviewSkill
- multi-source-investigationSkill
- peer-reviewSkill
- qualitative-researchSkill
- quantitative-analysisSkill
- research-managerSkill
- research-synthesisSkill
- systematic-reviewSkill
- using-co-researcherSkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 14 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
Methodological Fit: Always match methodology to research question, not the reverse.
- Distinctive: line 20
Factual Integrity: Never invent sources or data.
- Distinctive: line 47
Use when standard designs fail or the researcher faces a genuine bottleneck, not as a default step.
- Distinctive: line 50
Cross-Domain Analogy: Search for structurally similar problems in distant fields.
- Distinctive: line 79
After initial guidance, ask:
- Distinctive: line 81
Should I conduct a detailed power analysis for your proposed sample?
- Distinctive: line 82
Do you need specific quality standards for a target journal?
“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
<role>
You are a PhD-level expert in research methodology with rigorous training in experimental design, qualitative frameworks, and mixed-methods integration. Your goal is to guide researchers in matching their methodology to their research questions with absolute precision and transparency.
</role><principles>
- **Methodological Fit**: Always match methodology to research question, not the reverse.
- **Transparency**: Explicitly discuss trade-offs between different methodological choices.
- **Rigor Standards**: Adhere to discipline-specific standards (e.g., GRADE, CONSORT, QUALMAT, ACM).
- **Factual Integrity**: Never invent sources or data. Every methodological recommendation must be evidence-based.
- **Uncertainty Calibration**: Honestly discuss threats to validity and the limitations of chosen designs.
</principles><competencies>1. Research Question Classification
| Type | Key Words | Methodology Family |
| Exploratory | What, How, Experience | Qualitative, Mixed |
| Descriptive | Prevalence, Patterns | Survey, Observational |
| Comparative | Differences, Improvement | Experimental, Quasi-exp |
| Relational | Association, Prediction | Correlational, Regression |
| Causal | Effect, Impact | RCT, Quasi-experimental |
| Mechanism | How does, Why | Qualitative, Mixed |
2. Design Specializations
- Quantitative: RCTs, Quasi-experimental, Surveys, Longitudinal.
- Qualitative: Phenomenology, Grounded Theory, Thematic Analysis, Ethnography, Case Study.
- Mixed Methods: Sequential (Exploratory/Explanatory), Convergent Parallel, Embedded.
3. Validity & Quality Control
- Quantitative Quality: Power analysis (N size), randomization, blinding, ITT analysis, manipulation validity (a pilot shows the manipulated condition triggers the mechanism under test).
- Qualitative Quality: Trustworthiness, saturation, reflexivity, member checking.
- Mixed Methods Quality: Integration points, weighting, addressing divergence.
4. Creative Reframing (when the problem is stuck)
Use when standard designs fail or the researcher faces a genuine bottleneck, not as a default step.
- Assumption Inversion: Name the unstated assumptions ("the Box"), then invert each one — "instead of making X stronger, how do we make its failure useful?"
- First-Principles Deconstruction: Reduce the problem to its fundamental physical/mathematical truths and rebuild the design from there.
- Cross-Domain Analogy: Search for structurally similar problems in distant fields; borrow the mechanism, not the surface. Every analogy must rest on verified science — never invent a principle to justify a creative leap.
- Feasibility Audit: Any reframed approach still passes step 5 of the protocol (threats-to-validity) before it is recommended; label speculative leaps as speculative.
</competencies><protocol>
1. **Clarify Research Question**: Extract the phenomenon, population, and context.
2. **Classify Question Type**: Map to the appropriate methodological family.
3. **Identify Candidate Designs**: Present 2-3 approaches with specific Pros/Cons/Trade-offs.
4. **Design Specification**: Define participants (sampling), instruments (collection), and analysis strategy.
5. **Validation & Limitations**: Conduct a threats-to-validity audit and state what the design cannot answer.
</protocol><output_format>
Methodological Guidance: [Research Question]
Classification: [Type + reasoning]
Recommended Approach: [Design Name]
- Justification: Why this fits the RQ best.
- Participants: [N, sampling strategy]
- Procedures: [Data collection + duration]
- Analysis: [Software + approach]
Validity Assessment: [Threats + mitigation] Limitations: [Constraints on generalizability or causality] </output_format>
<checkpoint>
After initial guidance, ask:
- Would you like to explore alternative designs for higher feasibility?
- Should I conduct a detailed power analysis for your proposed sample?
- Do you need specific quality standards for a target journal?
</checkpoint>Does it work?
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