Literature Review logo

Literature Review

Community
Mathews-Tom
literature-review

Systematic literature review workflow: scope, search (arXiv, Semantic Scholar, Google Scholar), screen, extract, synthesize, and identify gaps. Triggers on: "literature review", "survey the literature", "related work", "systematic review", "synthesize the research", "find papers about", "research gap analysis".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameliterature-review
Stars
318
Forks
47
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Literature Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/literature-review .claude/skills/literature-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Literature Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Literature Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Literature Review

Systematic discovery, extraction, and synthesis of academic research on a defined topic.

When to use this skill vs. others

NeedSkill
Survey a research area, synthesize multiple papersliterature-review (this skill)
Critique a single paper's methodology and claimsresearch-critique
Audit a manuscript's formatting, structure, citationsmanuscript-review
Verify a manuscript's numbers trace to codemanuscript-provenance
Search arXiv for papers matching a queryarxiv-search (utility)

Workflow

Phase 1: Scope Definition

Before searching, establish the review boundaries:

  1. Research question — What specific question does the review answer? Vague topics produce vague reviews. "What techniques exist for X" is weaker than "How do methods for X compare on metric Y across domains Z?"
  2. Inclusion criteria — Define what counts:
    • Date range (e.g., 2020–present)
    • Publication type (peer-reviewed, preprints, both)
    • Domains/categories (e.g., cs.CL, cs.AI)
    • Minimum relevance threshold
  3. Exclusion criteria — Define what does not count:
    • Tangentially related work
    • Non-primary sources (blog posts, tutorials) unless explicitly included
    • Duplicate or superseded versions
  4. Expected output — What form should the review take? Narrative synthesis, tabular comparison, gap analysis, annotated bibliography, or related-work section?

Present the scope to the user for confirmation before proceeding.

Phase 2: Search & Discovery

Execute searches across available sources. Use multiple queries with varying specificity to avoid single-query blind spots.

Primary source: arXiv (via arxiv-search utility)

bash
uv run --with arxiv python scripts/arxiv_search.py "QUERY" --max-results 30 --sort-by relevance

Vary queries systematically:

  • Broad topic query: "retrieval augmented generation"
  • Field-scoped query: ti:retrieval AND abs:generation AND cat:cs.CL
  • Author-anchored query: au:lewis AND abs:retrieval (when key authors are known)
  • Recency query: same terms with --sort-by submitted

Secondary sources (via web search/fetch):

  • Semantic Scholar API: https://api.semanticscholar.org/graph/v1/paper/search?query=QUERY&limit=20&fields=title,authors,abstract,year,citationCount,externalIds
  • Google Scholar (via web search): site:scholar.google.com QUERY
  • Connected Papers (for citation graph exploration): https://www.connectedpapers.com/search?q=QUERY

Snowball strategy:

  • Forward snowball: find papers that cite a key paper (Semantic Scholar citations endpoint)
  • Backward snowball: follow the references of key papers
  • Use citation count as a signal for influence, not quality

Phase 3: Screening & Filtering

For each discovered paper, apply the inclusion/exclusion criteria from Phase 1.

Produce a screening table:

#IDTitleAuthorsYearRelevant?Reason
12301.07041Paper TitleAuthor et al.2023YesDirectly addresses RQ
22302.12345Other PaperAuthor B2023NoTangential — focuses on X not Y

Rules:

  • Screen on title + abstract first. Read full paper only for borderline cases.
  • When uncertain, include. It is cheaper to drop a paper later than to miss it.
  • Track exclusion reasons — they inform the review's limitations section.
  • Flag papers that appear in multiple search queries as likely high-relevance.

Phase 4: Data Extraction

For each included paper, extract a structured record:

yaml
- id: "2301.07041"
  title: "Paper Title"
  authors: ["Author One", "Author Two"]
  year: 2023
  venue: "NeurIPS 2023"
  research_question: "How does X affect Y?"
  methodology: "Controlled experiment with N=1000"
  key_findings:
    - "Finding 1 with quantitative result"
    - "Finding 2 with effect size"
  limitations: "Single-domain evaluation"
  relevance_to_rq: "Directly compares methods A and B on metric Y"
  citation_count: 142

Extraction discipline:

  • Record what the paper demonstrates, not what it claims to demonstrate.
  • Distinguish empirical findings (data-backed) from interpretive claims (author's framing).
  • Note methodology details that enable cross-paper comparison (datasets, metrics, baselines).
  • If the paper is available via pdf_url, read it for extraction. Do not extract from abstracts alone for included papers.

Phase 5: Synthesis

Transform extracted records into structured analysis. The synthesis method depends on the output format requested in Phase 1.

Thematic synthesis — Group papers by theme, approach, or finding:

  • Identify recurring themes across papers
  • Note where papers agree, disagree, or address different aspects
  • Highlight methodological trends (what approaches are gaining/losing traction)

Comparative synthesis — Build comparison tables:

MethodPaper(s)DatasetMetricResultLimitations
Method A[1], [3]D1F10.85Domain-specific
Method B[2], [4]D1, D2F10.82Requires X

Chronological synthesis — Map the evolution of the field:

  • What was the state of knowledge at time T?
  • What shifted and why?
  • Where is the field heading?

Gap analysis — Identify what is missing:

  • Questions raised but not answered by existing work
  • Methodological gaps (no one has tried approach X on problem Y)
  • Domain gaps (studied in domain A but not B)
  • Contradictions between studies that remain unresolved

Phase 6: Output

Produce the review document in the format specified in Phase 1.

Standard structure for a narrative review:

  1. Introduction — Research question, scope, and motivation for the review
  2. Search methodology — Databases searched, queries used, inclusion/exclusion criteria, screening results (N found → N screened → N included)
  3. Findings — Thematic or chronological synthesis of included papers
  4. Discussion — Cross-cutting analysis, trends, contradictions, gaps
  5. Limitations of this review — Search scope restrictions, potential biases, papers not accessible
  6. References — Full citation list for all included papers

Standard structure for a tabular review:

  1. Summary table (all included papers with key metadata)
  2. Comparison matrix (methods × metrics × results)
  3. Gap analysis table (questions × coverage)
  4. Reference list

Citation format: Default to Author et al. (Year) in-text with full references at the end. Adapt to the user's specified format (APA, Chicago, IEEE) if requested.

Quality Checks

Before delivering the review, verify:

  • Every included paper has a structured extraction record
  • Every claim in the synthesis is traceable to at least one extracted finding
  • The gap analysis identifies at least one concrete research opportunity
  • The review acknowledges its own limitations (search scope, access, biases)
  • Citation format is consistent throughout
  • No paper is cited that was excluded during screening
  • Contradictions between papers are noted, not silently resolved by picking a side

Edge Cases

SituationAdaptation
Very few papers found (<5)The field may be nascent. Note this explicitly. Broaden search terms or check if the topic goes by different terminology. Consider adjacent fields.
Too many papers found (>100)Tighten inclusion criteria. Consider limiting to top venues, recent years, or high-citation papers. Produce a scoping review rather than exhaustive review.
User provides a paper list instead of a topicSkip Phase 2 (Search). Start from Phase 3 (Screening) with the provided list.
User wants a related-work section for their own paperTailor synthesis to position the user's contribution. Organize by approaches the user's work builds on, alternatives it competes with, and gaps it fills.
No full-text access to key papersExtract from abstracts and note the limitation. Do not fabricate methodology details. Flag which papers were abstract-only in the extraction records.
Interdisciplinary topicSearch across multiple category prefixes. Note when different fields use different terminology for the same concept.
User asks for a "quick" literature reviewReduce Phase 2 to a single search query, Phase 3 to title-only screening, Phase 4 to abstract-only extraction. Label the output as a preliminary survey, not a systematic review.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Literature Review AI skill do?

Systematic literature review workflow: scope, search (arXiv, Semantic Scholar, Google Scholar), screen, extract, synthesize, and identify gaps. Triggers on: "literature review", "survey the literature", "related work", "systematic review", "synthesize the research", "find papers about", "research gap analysis".

Why use Literature Review on TypingMind?

Because you install it once and use it with any model. Literature Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Literature Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/literature-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Literature Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Literature Review?

As many as you like. As long as a model supports skills, you can use Literature Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Literature Review AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇