PluginBench
Skill
Review
Audit score 70

literature-review

affaan-m/ecc

Systematic literature-review workflow for academic, biomedical, and technical research with search planning, screening, synthesis, and citation verification.

What is literature-review?

This skill guides you through building narrative, scoping, systematic, or meta-analysis literature reviews. Use it when you need to find, screen, synthesize, and cite academic or technical literature for research questions, background sections, or evidence synthesis.

  • Define searchable research questions using PICO (clinical) or domain-specific frameworks
  • Plan reproducible search protocols across PubMed, arXiv, Semantic Scholar, and domain databases
  • Deduplicate sources by DOI, ID, title, and metadata to ensure rigor
  • Screen sources in stages (title, abstract, full text) with explicit exclusion reasons
  • Extract structured data into comparison tables with design, population, method, and findings
  • Synthesize evidence by theme with confidence levels (high/medium/low)

How to install literature-review

npx skills add null --skill literature-review
Claude Code
Cursor
Windsurf
Cline

How to use literature-review

  1. 1.Define your research question and convert it to a searchable format using PICO (clinical) or domain-specific structure
  2. 2.Decide on review type: narrative (broad), scoping (exploratory), systematic (reproducible protocol), or meta-analysis (quantitative)
  3. 3.Create a search protocol specifying databases, date range, languages, publication types, and exact search strings
  4. 4.Search each database and log results in a table with query, filters, and export format
  5. 5.Deduplicate sources by DOI, ID, title, and metadata; record removal count
  6. 6.Screen sources in stages (title → abstract → full text) and record exclusion reasons
  7. 7.Extract data into a structured table with study design, population, method, comparator, outcome, and key findings
  8. 8.Synthesize evidence by theme rather than paper-by-paper, grouping by strength and confidence level

Use cases

Good for
  • Preparing citation-backed background sections for academic papers or grant proposals
  • Mapping the state of the art and identifying research gaps for a novel research question
  • Comparing evidence across peer-reviewed papers, preprints, and technical reports for a clinical or technical claim
  • Conducting a scoping review to orient yourself on a broad topic before deeper investigation
  • Aggregating quantitative results across studies for a meta-analysis
Who it's for
  • Academic researchers and PhD students conducting literature reviews
  • Biomedical and clinical researchers preparing systematic reviews or meta-analyses
  • Technical and software engineers surveying prior work in a domain
  • Grant writers and proposal authors needing comprehensive background synthesis
  • Anyone preparing evidence-based reports or position papers

literature-review FAQ

What is the minimum set of databases to search?

PubMed for biomedical literature, arXiv for CS/math/physics/preprints, and Semantic Scholar or Crossref for broad academic discovery. Add domain-specific sources (clinical registries, patents, standards) when relevant.

How do I decide between narrative, scoping, and systematic reviews?

Use narrative for broad orientation, scoping to map concepts and gaps, and systematic for publication or clinical claims requiring reproducible protocols. Ask the user which rigor level is needed; default to scoping for exploratory work.

What should I do if I find conflicting evidence across papers?

Include conflicting findings in your synthesis, group by theme, and note the strength of evidence for each claim. Separate high-confidence (replicated, high-quality) from medium and low-confidence claims.

How do I avoid common pitfalls in literature reviews?

Do not treat search snippets as evidence, do not mix preprints and primary studies without labeling, do not omit negative findings, do not claim systematic rigor without a reproducible protocol, and do not use a single database for broad claims.

What is the best way to organize extracted data?

Use a structured extraction table with columns for study, design, population/data, method, comparator, outcome, key finding, and limitations. For technical papers, include dataset, benchmark, metric, and reproducibility notes.

Full instructions (SKILL.md)

Source of truth, from affaan-m/ecc.


name: 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. 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:

| 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.