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Literature Review

CommunityPopular
xuzhougeng
literature-review

Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.

Overview

Publisherxuzhougeng
Repositorywisp-science
Skill nameliterature-review
Stars
1K
Forks
104
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

    Published by xuzhougeng 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/xuzhougeng/wisp-science/tree/main/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/xuzhougeng/wisp-science.git /tmp/wisp-science
mkdir -p .claude/skills
cp -r /tmp/wisp-science/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

Work through six steps: scope, sweep, expand, verify, write, lint. The failure modes this skill exists to prevent are all silent — a fabricated DOI, a retracted headline result, a reading list dressed up as a synthesis — so each step below names the check that catches it.

1. Scope the request

Different phrasings want different deliverables:

Request shapeDeliverable
"the paper for X" / "the original/seminal…"one or two primary citations
"what's the evidence on X"thematic synthesis
"compare A and B"trade-off analysis ending in a recommendation
"where are the gaps"named gaps, each anchored to what establishes it

A vague lay query gets the scope a domain expert would default to, stated explicitly ("taking this as human RCT evidence; animal work is separate"). Clarify with the user only when the answer would change what you retrieve.

2. Sweep

Never write from recall. Recall chooses the framing and the search terms; retrieval supplies every citation. Start with search_openalex / crossref_lookup from this skill's runtime.py, a PubMed query, or any literature connector advertised in the session (search_skills with {"query":"literature PubMed Semantic Scholar bioRxiv ClinicalTrials"} finds installed guidance; load matches with use_skill).

For a named-paper lookup, the target is the highly cited primary publication that later work cites — not a review of it, not a news piece. Even when you know the paper cold, resolving its DOI is one tool call; skipping it turns a citation into a claim about a citation.

3. Expand along the citation graph

Keyword sweeps miss two things systematically: the foundational paper a field builds on, and the newest work that extends or contests your top hits. Take the two or three most relevant results and run expand_citations(doi) — it returns references (backward) and cited-by (forward) from OpenAlex. Fold the on-topic finds back into the working set before drafting. A survey-grade answer typically rests on fifteen or more distinct primary-paper DOIs; a handful of reviews is a reading list.

The Python OpenAlex helpers raise on HTTP errors, timeouts, or malformed responses. Empty results are valid only after successful retrieval. If either citation direction fails, report the retrieval failure rather than treating the partial graph as complete. Do not convert an exception into an empty list.

4. Verify

Run verify_dois on everything you intend to cite. Distinguish registered, not resolving, and unverified (ok=None, e.g. network failure) results. A registered DOI still requires reading the paper to check whether it supports the claim; a failed request is not evidence of fabrication. When you have author/year/journal but no DOI, look it up; never pattern-complete one. For surprising or high-profile findings, check Crossref's update-to field: sensational papers are findable because they were sensational, and some were retracted. When the requested paper does not exist — the claim collapsed or was never established — say exactly that and point at what the evidence actually shows, instead of substituting the nearest-matching citation.

5. Write the synthesis

Organize by question or theme, never paper-by-paper. The value is the layer on top of the papers: what replicated, what didn't, where the field agrees on effect but splits on mechanism, which older result a newer one superseded. Two tests for the draft:

  • First-sentence test. Read only each paragraph's opening sentence. In sequence they should form your argument; if they form a list of author names, you have an annotated bibliography.
  • Bullet test. Consecutive lines starting - Author Year showed… are a paragraph you haven't written. Bullets are for genuinely enumerable things (a reference appendix, a comparison table); the argument itself is prose.

Calibrate stated confidence to the evidence: a phase-3 RCT is stated plainly, a single-cohort finding is "one group reported", preprints are flagged as preprints, contested areas get both sides plus an honest "unresolved". Engage a contested premise rather than building on it.

Cite inline as [Author Year](https://doi.org/10.xxxx/...) so prose renders as (Author Year) with the DOI in the href. URL-encode parentheses inside a DOI as %28/%29. No numbered [1] references — they desync on reorder. Headings are short noun phrases; with five or more topics, group under two or three ## and demote the rest to ###.

6. Deliver and lint

The answer lives in the chat reply: open on the finding itself, lay out the evidence with inline DOIs, close on what remains open. For anything beyond a one-paper lookup, also save the full review to a project-relative Markdown file and link it at the end of the reply. Process narration — "all DOIs verified", "no retraction flags", "report saved" — belongs nowhere: not as opener, footer, or subtitle. Verification lives in the tool trace.

Before saving, run style_pass(draft) from runtime.py once on the full markdown, fix what it lists in one editing pass, and save. It is a lint, not a gate — do not loop on it. If style_pass is not defined in the kernel, read this skill's runtime.py and exec it first.

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?

Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.

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/xuzhougeng/wisp-science/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 xuzhougeng under the Apache-2.0 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.

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