Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install onfire7777-universal-ai-skills-library-skills-deep-research-scholarly-analysis-enginegit clone https://github.com/onfire7777/universal-ai-skills-library.gitcp universal-ai-skills-library/SKILL.MD ~/.claude/skills/onfire7777-universal-ai-skills-library-skills-deep-research-scholarly-analysis-engine/SKILL.md--- name: deep-research-scholarly-analysis-engine description: Comprehensive research methodology combining scholarly research, scientific data analysis, evidence synthesis, source evaluation, and systematic review practices for producing research-grade outputs. license: Unspecified metadata: version: 1.0.0 author: Custom Meta-Skill tags: - research - scholarly - scientific - evidence-based - systematic-review - data-analysis - google-scholar - consensus --- # Deep Research & Scholarly Analysis Engine ## Purpose Enable rigorous, evidence-based research that meets academic standards. Combine multiple research methodologies, source evaluation frameworks, and data synthesis techniques to produce outputs grounded in the best available evidence. ## Research Methodology Hierarchy ### Level 1: Systematic Review (Highest Rigor) Use when: Critical decisions, health/safety, policy recommendations 1. Define a precise research question (PICO format for clinical: Population, Intervention, Comparison, Outcome) 2. Develop comprehensive search strategy across multiple databases 3. Apply inclusion/exclusion criteria systematically 4. Extract data using standardized forms 5. Assess quality of evidence (GRADE framework) 6. Synthesize findings with appropriate methods 7. Report following PRISMA guidelines ### Level 2: Structured Literature Review Use when: Technical decisions, architecture choices, best practices 1. Define scope and research questions 2. Search 3+ independent sources 3. Evaluate source quality (CRAAP test) 4. Identify themes and patterns 5. Synthesize with explicit methodology 6. Acknowledge limitations ### Level 3: Rapid Evidence Assessment Use when: Time-constrained decisions, initial exploration 1. Focused search on 2-3 key sources 2. Quick quality assessment 3. Extract key findings 4. Provide confidence-weighted conclusions ## Source Evaluation: The CRAAP Test For every source, evaluate: - **Currency**: When was it published/updated? Is it current enough for the topic? - **Relevance**: Does it directly address the research question? - **Authority**: Who is the author? What are their credentials? Is the publisher reputable? - **Accuracy**: Is the information supported by evidence? Can it be verified? Is it peer-reviewed? - **Purpose**: Why does this information exist? Is there bias? Is it trying to sell something? Score each dimension 1-5. Sources scoring below 15/25 should be used cautiously or discarded. ## Evidence Quality Pyramid (Strongest to Weakest) 1. **Systematic Reviews & Meta-Analyses** — Gold standard 2. **Randomized Controlled Trials** — Strong causal evidence 3. **Cohort Studies** — Good observational evidence 4. **Case-Control Studies** — Moderate evidence 5. **Case Series / Case Reports** — Weak evidence 6. **Expert Opinion / Editorials** — Lowest evidence level 7. **Anecdotal / Blog Posts** — Not evidence (but may suggest hypotheses) ## Search Strategy Best Practices ### Academic/Scholarly Search - **Google Scholar**: Use `site:`, `intitle:`, date ranges, cited-by chains - **Consensus.app**: For AI-synthesized scientific consensus on specific claims - **Semantic Scholar**: For citation graph exploration and related papers - **PubMed**: For biomedical and life sciences - **arXiv**: For preprints in CS, physics, math, AI/ML - **SSRN**: For social sciences and economics ### Search Technique: Citation Chaining 1. Find one highly relevant paper 2. **Forward chain**: Who cited this paper? (Google Scholar "Cited by") 3. **Backward chain**: What did this paper cite? (Reference list) 4. **Lateral chain**: What other papers do the same authors write? 5. Repeat until saturation (no new relevant papers found) ### Search Technique: Boolean Strategy - Use AND to narrow: `"machine learning" AND "healthcare" AND "diagnosis"` - Use OR to broaden: `"deep learning" OR "neural network"` - Use NOT to exclude: `"AI" NOT "artificial insemination"` - Use quotes for exact phrases: `"transformer architecture"` - Use wildcards: `optim*` matches optimize, optimization, optimal ## Data Analysis Framework ### Quantitative Analysis Protocol 1. **Data Cleaning**: Check for missing values, outliers, inconsistencies 2. **Descriptive Statistics**: Mean, median, mode, SD, range, distribution shape 3. **Exploratory Analysis**: Visualizations, correlations, patterns 4. **Inferential Statistics**: Hypothesis testing, confidence intervals, effect sizes 5. **Interpretation**: What do the numbers actually mean in context? ### Statistical Reasoning Checklist - Is the sample size adequate? - Is the sample representative? - Are the statistical tests appropriate for the data type? - Is statistical significance confused with practical significance? - Are confidence intervals reported (not just p-values)? - Is the effect size meaningful? - Could there be confounding variables? - Is correlation being confused with causation? ### Qualitative Analysis Protocol 1. **Thematic Analysis**: Identify recurring themes across sources 2. **Content Analysis**: Systematic categorization of textual data 3. **Comparative Analysis**: How do different sources agree/disagree? 4. **Gap Analysis**: What questions remain unanswered? ## Evidence Synthesis Methods ### Narrative Synthesis - Organize findings by theme, not by source - Identify areas of agreement and disagreement - Weight findings by evidence quality - Explicitly state the strength of evidence for each conclusion ### Vote Counting - How many studies support conclusion A vs B? - Weight by study quality and sample size - Report the ratio with confidence assessment ### Triangulation - Do multiple independent sources/methods converge on the same conclusion? - If yes: High confidence - If mixed: Moderate confidence, report the disagreement - If contradictory: Low confidence, investigate why ## Research Output Standards ### Every Research Output Must Include: 1. **Clear Research Question**: What exactly are we investigating? 2. **Methodology Statement**: How did we search and what criteria did we use? 3. **Source Documentation**: All sources cited with full references 4. **Evidence Quality Assessment**: How strong is the evidence? 5. **Confidence Level**: How confident are we in each conclusion? 6. **Limitations**: What are the gaps and weaknesses? 7. **Recommendations**: What actions does the evidence support? ### Citation Standards - Always cite specific sources for factual claims - Prefer primary sources over secondary - Include publication date for currency assessment - Note if a source is pre-print, peer-reviewed, or grey literature - Use inline numeric citations with reference list ## Internet Parsing & Search Mastery ### Web Content Extraction Hierarchy 1. **Structured APIs** (best): Use official APIs when available 2. **Structured Data**: Look for JSON-LD, schema.org markup, RSS feeds 3. **Clean HTML Parsing**: Extract from semantic HTML elements 4. **Full Page Rendering**: For JavaScript-heavy sites 5. **Screenshot + OCR**: Last resort for complex layouts ### Source Triangulation Protocol For any factual claim from the internet: 1. Find the **primary source** (original study, official announcement, raw data) 2. Find **2+ independent confirmations** from reputable sources 3. Check for **contradicting evidence** actively 4. Assess **recency** — is this still current? 5. Check for **corrections or retractions** ### OSINT Best Practices - Start broad, narrow progressively - Use multiple search engines (results differ) - Check the Wayback Machine for historical context - Verify images with reverse image search - Cross-reference social media claims with official sources - Be aware of information warfare and deliberate misinformation ## Anti-Patterns in Research - **Cherry-picking**: Selecting only evidence that supports a predetermined conclusion - **Appeal to Authority**: Accepting claims because of who said them, not the evidence - **Recency Bias**: Assuming newer = better without evaluation - **Survivorship Bias**: Only looking at successful cases - **Publication Bias**: Published studies skew positive; negative results are underreported - **P-hacking**: Statistical manipulation to achieve significance - **HARKing**: Hypothesizing After Results are Known - **Ecological Fallacy**: Applying group-level findings to individuals