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Literature Review

Community
scdenney
literature-review

Builds or audits a literature review, producing an evidence map, the closest prior work, source clusters, a gap verdict, and a synthesis plan for the introduction. Use when the user asks whether a contribution is novel, who has studied a question, what the literature establishes, or how to organize a review, or wants a reading list, Zotero export, or pile of papers turned into one. For systematic reviews or meta-analyses it produces the protocol scaffold and routes registration (PROSPERO or OSF) and screening to dedicated tools.

Overview

Publisherscdenney
Repositoryopen-science-skills
Skill nameliterature-review
Stars
69
Forks
3
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by scdenney 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.
    https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/literature-review
  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/scdenney/open-science-skills.git /tmp/open-science-skills
mkdir -p .claude/skills
cp -r /tmp/open-science-skills/plugin/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 Evidence Mapper

Heritage and scope

This is an original Open Science Skills workflow for experimental and computational social science. It remixes high-level ideas from Cheng-I Wu's Academic Research Skills for Claude Code (CC BY-NC 4.0), especially evidence mapping, source verification, and the separation between narrative literature review and formal systematic review. It is not a full ARS pipeline.

Instructions

1. Classify the review task

Decide which of these the user needs:

  • Narrative/theory review: organize concepts, mechanisms, and debates for an introduction.
  • Design precedent review: identify prior treatments, measures, samples, estimands, or analysis strategies.
  • Contribution audit: test whether the claimed gap survives contact with the closest prior work.
  • Evidence map: summarize what each study establishes, where it applies, and what remains unresolved.
  • Systematic-review escalation: when the user needs exhaustive search, screening, risk-of-bias, and PRISMA reporting.

Default to a narrative/evidence-map review unless the user explicitly asks for a systematic review, meta-analysis, or PRISMA-compliant output.

For a systematic review, tell the user what this skill covers: steps 2-7 produce the protocol scaffold (question, boundaries, search strings, inclusion/exclusion criteria, evidence-map structure), but exhaustive multi-database screening, dual-coder risk-of-bias assessment, and PRISMA flow accounting need dedicated tooling and human coders. Deliver the scaffold, recommend registering it (PROSPERO or OSF), and point to screening tools (e.g., Covidence, ASReview) for the systematic phase.

2. Define the question and boundaries

Before summarizing papers, specify:

  • Research question or review question.
  • Population, setting, outcome, treatment/exposure, and mechanism scope.
  • Disciplines and literatures that must be included.
  • Time window and language restrictions, if any.
  • Inclusion/exclusion logic for sources.
  • What counts as "closest prior work."

If the user only gives a broad topic, first produce a short scoping memo with 2-4 possible review boundaries rather than writing a generic review.

3. Build the source base

Use the user's supplied sources first. Then identify obvious missing source classes:

  • Seminal theoretical anchors.
  • Most recent directly related empirical work.
  • Meta-analyses, registered reports, study registries, or working-paper series.
  • Methods papers that justify the research design.
  • Null findings, failed replications, or unpublished registered studies when visible.
  • Adjacent literatures that use different vocabulary for the same construct.

Run citation-check when the source list is large, messy, DOI-heavy, or likely to contain stale working papers.

4. Extract evidence, not summaries

For each important source, record:

  • Claim actually supported: one sentence, no inflation.
  • Design and identification: sample, setting, treatment/exposure, comparison, outcome, estimand, and key limitations.
  • Observable implication: what the source lets a reader expect in the user's setting.
  • Boundary condition: where the finding may fail.
  • Use in the user's paper: background, theory, design precedent, measurement precedent, competing explanation, or gap support.

Organize by claim, not by author; chronological "Author A says X, Author B says Y" prose belongs only where chronology is theoretically important.

5. Cluster the literature

Organize sources into 3-6 clusters. Prefer conceptual or mechanism clusters over method-only clusters:

  • Mechanism families.
  • Competing theories.
  • Measurement traditions.
  • Empirical settings or populations.
  • Identification strategies.
  • Evidence-quality tiers.

For each cluster, state what is settled, what is contested, and what would change the interpretation.

6. Test the claimed gap

Write a gap verdict:

  • Holds: no close prior work answers the same question with the same population/mechanism/design.
  • Partly holds: prior work answers part of it; the contribution must be narrowed.
  • Does not hold: the claimed contribution is already established; reframe as replication, extension, boundary test, or synthesis.
  • Cannot assess: missing sources or inaccessible source text prevent judgment.

When the gap is weak, propose a better contribution frame rather than only criticizing it.

7. Compose with sibling skills

  • Use narrative-building after the evidence map exists to turn the review into the "Why-to-If-Then" funnel.
  • Use hypothesis-building when the review implies falsifiable expectations and estimands.
  • Use pre-registration-writing when the review supports confirmatory hypotheses.
  • Use methods-reporting when reviewing how prior studies report designs, sample flow, and transparency.
  • Use journal-review when auditing someone else's manuscript for novelty and placement.

Output

Produce a Literature Review Evidence Map:

# Literature Review Evidence Map

Review question:
Scope and exclusions:
Search/source base:
Gap verdict: Holds / Partly holds / Does not hold / Cannot assess

## Closest Prior Work
| Source | What it actually establishes | Boundary | Relation to user's claim |

## Evidence Clusters
### Cluster 1: <name>
Settled:
Contested:
Missing:
Key sources:

## Contribution Diagnosis
Claimed gap:
Verdict:
Better contribution frame:

## Literature Review Architecture
1. <section purpose>
2. <section purpose>
3. <section purpose>

## Sentences the Review Must Earn
- <sentence-level claim that needs source support>

## Sources Needing Verification
| Source | Why |

Quality checks

  • The review question and scope are explicit.
  • Closest prior work is identified before novelty is judged.
  • Each key source is tied to an observable implication or boundary condition.
  • The output distinguishes settled evidence from contested evidence.
  • The gap verdict is calibrated, not inflated.
  • Publication bias, registered studies, and null findings were considered when relevant.
  • citation-check was invoked or recommended when source integrity was uncertain.
  • The synthesis plan can feed directly into narrative-building.

Frequently asked questions

What does the Literature Review AI skill do?

Builds or audits a literature review, producing an evidence map, the closest prior work, source clusters, a gap verdict, and a synthesis plan for the introduction. Use when the user asks whether a contribution is novel, who has studied a question, what the literature establishes, or how to organize a review, or wants a reading list, Zotero export, or pile of papers turned into one. For systematic reviews or meta-analyses it produces the protocol scaffold and routes registration (PROSPERO or OSF) and screening to dedicated tools.

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/scdenney/open-science-skills/tree/main/plugin/skills/literature-review. TypingMind reads its SKILL.md 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?

It is published on GitHub by scdenney. Check the repository for licensing terms. 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.

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