--- name: scientific-thinking-literature-review description: Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging. Use when the task is to find, screen, synthesize, and cite a body of academic or technical literature. metadata: origin: community --- # Literature Review Use this skill when the task is to find, screen, synthesize, and cite a body of academic or technical literature. ## When to Use - Building a systematic, scoping, or narrative literature review. - Synthesizing the state of the art for a research question. - Finding gaps, contradictions, or future-work directions. - Preparing citation-backed background sections for papers or reports. - Comparing evidence across peer-reviewed papers, preprints, patents, and technical reports. ## Review Types - **Narrative review**: broad synthesis; useful for orientation. - **Scoping review**: maps concepts, methods, and evidence gaps. - **Systematic review**: predefined protocol, reproducible search, explicit screening and exclusion. - **Meta-analysis**: systematic review plus quantitative effect aggregation. Ask the user which level of rigor is needed. If unspecified, default to a scoping review for exploratory work and a systematic review for publication or clinical claims. ## Workflow ### 1. Define the Question Convert the prompt into a searchable research question. For clinical or biomedical work, use PICO: - Population - Intervention or exposure - Comparator - Outcome For technical work, use: - system or domain - method or intervention - comparison baseline - evaluation metric ### 2. Plan the Search Create a search protocol before collecting sources: - databases to search - date range - languages - publication types - inclusion criteria - exclusion criteria - exact search strings Minimum useful database set: - PubMed for biomedical and life-sciences literature. - arXiv for CS, math, physics, quantitative biology, and preprints. - Semantic Scholar or Crossref for broad academic discovery. - Domain-specific sources when relevant, such as clinical-trial registries, patent databases, standards bodies, or official technical docs. ### 3. Search and Log Evidence Keep a search log that makes the review reproducible: ```markdown | Database | Date searched | Query | Filters | Results | Export | | --- | --- | --- | --- | ---: | --- | | PubMed | 2026-05-11 | `("CRISPR"[tiab] OR "Cas9"[tiab]) AND "sickle cell"[tiab]` | 2020:2026, English | 86 | PMID list | | arXiv | 2026-05-11 | `CRISPR sickle cell gene editing` | q-bio, 2020:2026 | 9 | BibTeX | ``` Save raw IDs, URLs, DOIs, abstracts, and notes separately from the final prose. ### 4. Deduplicate Deduplicate in this order: 1. DOI 2. PMID or arXiv ID 3. exact title 4. normalized title plus first author and year Record how many duplicates were removed. ### 5. Screen Sources Screen in stages: 1. title 2. abstract 3. full text For systematic work, record exclusion reasons: - wrong population - wrong intervention - wrong outcome - not primary research - duplicate - unavailable full text - outside date range ### 6. Extract Data Use a structured extraction table: ```markdown | Study | Design | Population/Data | Method | Comparator | Outcome | Key finding | Limitations | | --- | --- | --- | --- | --- | --- | --- | --- | | Author Year | RCT/cohort/review/etc. | sample or corpus | method | baseline | measured outcome | result | caveat | ``` For technical papers, include dataset, benchmark, metric, baseline, and reproducibility notes. ### 7. Synthesize Group evidence by theme rather than summarizing papers one by one. Useful synthesis lenses: - strongest evidence - conflicting evidence - methodological weaknesses - population or dataset limits - recency and replication - practical implications - unanswered questions Separate claims by confidence: - **High confidence**: replicated, high-quality evidence across sources. - **Medium confidence**: plausible but limited by sample, method, or recency. - **Low confidence**: early, speculative, single-source, or weakly measured. ### 8. Verify Citations Before finalizing: - verify DOI, PMID, arXiv ID, or official URL - check author names and publication year - do not cite a paper for a claim it does not make - mark preprints as preprints - distinguish reviews from primary evidence ## Output Template ```markdown # Literature Review: Generated: Review type: Search window: Databases: ## Research Question ## Search Strategy ## Inclusion and Exclusion Criteria ## Evidence Summary ## Thematic Synthesis ## Gaps and Limitations ## References ## Search Log ``` ## Pitfalls - Do not treat search snippets as evidence. - Do not mix preprints, reviews, and primary studies without labeling them. - Do not omit negative or conflicting findings. - Do not claim systematic-review rigor without a reproducible protocol. - Do not use a single database for a broad claim unless the scope is explicitly limited to that database. --- Source attribution: affaan-m Original project: https://github.com/affaan-m/everything-claude-code/blob/25278e7d7b6cf6e6a4e7a6c8e10f14ff22f0eb5b/skills/scientific-thinking-literature-review/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Affaan Mustafa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: affaan-m Original project: https://github.com/affaan-m/everything-claude-code/blob/25278e7d7b6cf6e6a4e7a6c8e10f14ff22f0eb5b/skills/scientific-thinking-literature-review/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Academic Writer, Clinical Research, Data Analyst, Market Research Analyst, Researcher Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 380918f545c7e9b297b23bee4a0432deba977acbdb217232bf1ab9d5aa3c562e