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Skills/affaan-m/everything-claude-code/literature-review
literature-review logo

literature-review

affaan-m/everything-claude-code
1K installs216K stars
Run it on Hostinger, 20% off →Your friend gets 20% off too, using this linkFree API →|View on GitHub|Create your own skill →

Installation

npx skills add https://github.com/affaan-m/everything-claude-code --skill literature-review

Summary

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

SKILL.md

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.

Score

0–100
65/ 100

Grade

C

Popularity17/30

1,480 installs — growing adoption. Source repo has 215,671 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Literature Review skill score badge previewScore badge

Markdown

[![Literature Review skill](https://www.claudemarket.ai/skills/affaan-m/everything-claude-code/literature-review/badges/score.svg)](https://www.claudemarket.ai/skills/affaan-m/everything-claude-code/literature-review)

HTML

<a href="https://www.claudemarket.ai/skills/affaan-m/everything-claude-code/literature-review"><img src="https://www.claudemarket.ai/skills/affaan-m/everything-claude-code/literature-review/badges/score.svg" alt="Literature Review skill"/></a>

Literature Review FAQ

How do I install the Literature Review skill?

Run “npx skills add https://github.com/affaan-m/everything-claude-code --skill literature-review” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Literature Review skill do?

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Literature Review skill free?

Yes. Literature Review is a free, open-source skill published from affaan-m/everything-claude-code. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Literature Review work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Literature Review works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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