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affaan-m/scientific-thinking-literature-review

affaan-m

scientific-thinking-literature-review

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.

v1.0LATEST
NewUpdated Sep 30, 2026

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

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:

| 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:

| 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

# Literature Review: <Topic>

Generated: <date>
Review type: <narrative | scoping | systematic | meta-analysis>
Search window: <dates>
Databases: <list>

## 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.
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Overall Score

88/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

95

Quality

85

Clarity

88

Completeness

82

Summary

This skill guides AI agents through a systematic literature review workflow for academic, biomedical, technical, and scientific topics. It provides structured methodology for defining research questions, planning database searches, screening sources, extracting data, synthesizing findings thematically, and verifying citations. The skill emphasizes reproducibility through search logging, deduplication protocols, and explicit confidence levels for evidence claims.

Detected Capabilities

information retrievalsource screening and filteringdata extraction and organizationcitation verificationthematic synthesismarkdown documentation creationstructured table generation

Trigger Keywords

Phrases that agents use to match this skill to user intent.

literature reviewsystematic reviewresearch synthesiscitation mappingscoping reviewevidence synthesisacademic research

Use Cases

  • Building systematic, scoping, or narrative literature reviews for research projects
  • Synthesizing state-of-the-art findings to answer specific research questions
  • Identifying gaps and contradictions in academic literature across multiple databases
  • Preparing citation-backed background sections for academic papers or technical reports
  • Comparing evidence across peer-reviewed papers, preprints, patents, and technical documentation

Quality Notes

  • Comprehensive workflow covers all major stages of systematic literature review from question formulation through final synthesis
  • Clear guidance on review types (narrative, scoping, systematic, meta-analysis) with decision criteria for when to use each
  • Well-documented search protocol requirements including database selection, filters, and reproducibility logging
  • Strong emphasis on avoiding common pitfalls (e.g., not claiming systematic rigor without protocol, not omitting negative findings)
  • Includes concrete examples of search queries, deduplication methods, and data extraction tables that agents can reference
  • Delineates confidence levels for evidence (high/medium/low) to help readers understand evidence quality
  • Provides domain-specific guidance for both clinical (PICO framework) and technical research questions
  • Output template gives clear structure for final deliverable
  • No executable operations or file system access required—skill is instructional and advisory only
Model: claude-haiku-4-5-20251001Analyzed: Sep 30, 2026

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