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Lit Review

Community
neuromechanist
lit-review

Use this skill for "literature review workflow", "multi-phase lit review", "direction paper", "review paper protocol", "strand-based literature review", "citation-grounded review", "systematic lit review with paper cards", "build a lit review corpus", "lit review pipeline", "orchestrate a literature review", "research directions document", "write a literature review", "synthesize papers", "thematic review", "narrative review", "systematic review", "scoping review", "gap analysis", or when the user wants either a rigorous multi-phase citation-traceable lit review or a single-pass thematic synthesis for an Introduction/Background section.

Overview

Publisherneuromechanist
Repositoryresearch-skills
Skill namelit-review
Stars
51
Forks
9
Bundled files
8
LicenseBSD-3-Clause
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.

  • 8 bundled files

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

  • Open source

    Published by neuromechanist on GitHub. Read the source before you install it.

Installation

Install the Lit 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/neuromechanist/research-skills/tree/main/plugins/manuscript/skills/lit-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/neuromechanist/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/plugins/manuscript/skills/lit-review .claude/skills/lit-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lit 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 Lit 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 Lit 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.

Multi-Phase Literature Review Workflow

Orchestrate a rigorous, citation-grounded literature review across phases: angles to briefs, parallel paper collection, taxonomic synthesis, direction papers, and self-review loops. Every claim in a final document is traceable back to a paper-card.

When to Use

  • Building a corpus-grounded review paper or a set of direction documents
  • Multi-strand reviews where breadth + depth must be coordinated across distinct angles (e.g. tools strand, data strand, science strand)
  • Reviews where claim-to-evidence traceability matters (grant lit reviews, position papers, white papers)
  • Iterative reviews where new papers, refined angles, or updated synthesis must roll forward without losing prior work

When NOT to Use

  • Original research IMRAD writing. Use manuscript:manuscript-writing.
  • Peer review of a submitted manuscript. Use manuscript:paper-review.
  • Journal formatting. Use manuscript:manuscript-formatting.

Two modes

This skill covers two workflows:

  • Express mode (single-pass synthesis): thematic synthesis for an Introduction, Background section, or quick standalone review. See references/single-pass-synthesis.md. Pair with references/review-frameworks.md for PRISMA, PICO, SPIDER, scoping protocol, and risk-of-bias tools.
  • Full protocol (multi-phase): citation-traceable corpus reviews and direction papers. Use the Phase 0-4 workflow below.

When in doubt: if the user wants flowing prose for one section, use express mode; if they want a corpus with claim-to-card traceability, use the full protocol.

Workflow Phases

Phase 0: Briefs            -> _briefs/strand-*.md (one brief per strand)
Phase 1: Collection        -> research/collection/<strand>/<slug>/{card.md, source.{pdf,md}, meta.json}
                              + INDEX.md + <strand>.bib per strand
Phase 2: Synthesis         -> research/synthesis/{<strand>-ontology, gap-analysis, scope-diagram, <domain>-map}.md
Phase 3: Direction papers  -> direction-papers/<topic>-direction.md
Phase 4: Review loop       -> revised direction; may reopen Phase 1 with new gaps

Each angle is realized as a strand; the brief is the strand's dispatch document. Iteration is expected: loop back from any phase. The directory layout is the persistence layer; treat it as the source of truth between sessions.

Phase 0: Define angles and write briefs

Inputs: prior work, gap statement, epic-dev findings (if any), grant call or thesis question.

Output: one brief per strand in _briefs/strand-<name>.md.

Each brief defines:

  • Goal (one sentence)
  • Scope categories (numbered list, breadth first)
  • Per-entry deliverable (cards + source + bib + index)
  • Seed material (existing prior-work documents to import)
  • Acceptance criteria (count thresholds, breadth thresholds, completeness gates)
  • Out-of-scope items
  • Sister skills to use (opencite:opencite, manuscript:manuscript-writing)

See references/brief-template.md for the full structure.

If the user names a project domain (neuro tools, clinical trials, ML methods, etc.), generate strands by partitioning the topic on dimensions that cannot be merged later without information loss. Common partitions: methods vs. infrastructure vs. application; tools vs. data vs. theory; modality A vs. modality B.

Phase 1: Collection (parallel strand agents)

For each strand, build a corpus of paper-cards under research/collection/<strand>/. Each entry is a folder with card.md plus source.md plus meta.json, and source.pdf only when redistributable. Per-strand aggregates are INDEX.md and <strand>.bib. Use opencite:opencite for all paper operations.

Schema and storage rules: references/paper-card-schema.md. License-to-redistribution policy and CI rule: references/license-rules.md.

When parallelizing strands, dispatch one agent per strand. Use the brief as the agent's full context. Do not let strand agents synthesize across strands; that is Phase 2's job.

Phase 2: Synthesis (whole-corpus integration)

Inputs: full collection across all strands.

Outputs (in research/synthesis/):

  • <strand>-ontology.md per strand: hierarchical category tree of corpus entries
  • <domain>-map.md (e.g. science-map.md): theme-by-theme inventory of analytic / methodological themes
  • <data>-hierarchy.md (e.g. dataset-hierarchy.md): the data layer, when applicable
  • gap-analysis.md: three-column comparison of what is covered by prior efforts vs. what the synthesis reveals as uncovered. The third column is the input to Phase 3
  • scope-diagram.md: prose plus optional Mermaid/ASCII diagram of corpus boundaries

Bias rules (enforced):

  • Gap analysis must list what the corpus does NOT support, not just what it does
  • Inter-strand contradictions must be named, not papered over
  • Frequency-of-mention is not evidence weight; cite single primary sources where appropriate

See references/synthesis-templates.md.

Phase 3: Direction papers (citation-grounded essays)

Output: direction-papers/<topic>-direction.md per direction (typically one per strand or per gap cluster).

Each direction paper:

  • Defends a thesis grounded in the corpus
  • Cites every claim back to a specific card path: [<slug>](../research/collection/<strand>/<slug>/card.md)
  • Closes with a flat references section keyed to BibTeX in the strand .bib
  • Follows review-paper IMRAD structure and prose discipline (delegate to manuscript:manuscript-writing): no em-dashes, abbreviations defined on first use, descriptive voice not exhortatory

The cite-card cross-link is the load-bearing convention. A claim that does not link to a card is a claim that has not yet been grounded; either ground it (add the card) or remove the claim.

See references/direction-paper-template.md.

For LaTeX export of a direction paper to a journal review template, delegate to manuscript:manuscript-formatting. The base format is markdown.

Phase 4: Review loop

Self-review the direction paper using manuscript:paper-review. Treat it as a peer review of one's own draft.

Common loop-back triggers:

  • Reviewer (self or other) names a claim as ungrounded -> Phase 1 (add cards) or Phase 3 (drop claim)
  • Reviewer flags a missing theme -> Phase 0 (new strand or expanded scope) -> Phase 1 (collect)
  • Reviewer flags a contradiction in synthesis -> Phase 2 (revise gap analysis or ontology)
  • Reviewer flags storyline incoherence -> Phase 3 (restructure with manuscript:manuscript-writing)

Apply references/rigor-checklist.md before declaring a direction paper done.

Bootstrapping a new lit review

When the user starts fresh, propose a top-level layout in the working directory or a subdirectory:

<root>/
├── _briefs/
├── research/
│   ├── collection/
│   │   ├── _schema/paper-card.md
│   │   └── <strand>/
│   └── synthesis/
└── direction-papers/

Create directories on demand as work progresses; do not pre-create empty trees. The _schema/paper-card.md should be a copy of references/paper-card-schema.md so that strand agents have a local reference.

Formalize phases with project:epic-dev

The Phase 0-4 workflow maps cleanly onto the epic/sprint model. When the lit review lives in a git repository with a GitHub remote, delegate phase orchestration (issues, sub-issues, branches, worktrees, state file) to project:epic-dev so the review has the same tracking discipline as feature work. This is opt-in; skip it for non-git note folders or solo scratch reviews.

Recommended mapping:

Lit-review artifactepic-dev artifact
The lit review itselfEpic issue + epic branch (feature/issue-{N}-epic-litreview-{slug})
Phase 0 (briefs)Sub-issue, single phase branch; output _briefs/strand-*.md
Phase 1 (collection)One sub-issue + worktree per strand for parallel agents; outputs under research/collection/<strand>/
Phase 2 (synthesis)Sub-issue depending on all Phase 1 phases; outputs under research/synthesis/
Phase 3 (direction papers)One sub-issue per direction paper, can run in parallel; outputs under direction-papers/
Phase 4 (review loop)Sub-issue per review pass; loop-back triggers reopen earlier-phase issues rather than mutating closed ones
.claude/epic.local.mdPhase tracker that survives sessions; complements the directory-as-source-of-truth convention

When to invoke epic-dev:

  • At the start of a fresh multi-strand lit review, after the user agrees on the strand list. Delegate epic + sub-issue creation to project:epic-dev; it will produce the issue tree, worktrees, and state file. Continue Phase 0 brief drafting inside the resulting epic worktree.
  • When a Phase 4 review loop spawns new gaps that justify reopening Phase 1 with new strands, create new sub-issues under the existing epic via project:epic-dev --next-phase rather than starting a parallel epic.
  • When resuming a stalled review across sessions, project:epic-dev --resume reads .claude/epic.local.md and routes back to the active phase worktree.

When NOT to invoke epic-dev:

  • The review is express-mode (single-pass synthesis); the overhead exceeds the value.
  • The output lives in a non-git directory (private notes, OneDrive, Notion export staging).
  • The user declines GitHub issue tracking for the review.

The cite-card cross-link convention from Phase 3 still applies regardless of whether phases are tracked as epic-dev sub-issues; epic-dev tracks the process, not the traceability.

Sister skills

Invoke each installed skill by its fully qualified <plugin>:<skill> name through the host's skill-invocation mechanism. Keep host-specific command or tool syntax in the host adapter, not in shared skill instructions.

SkillUsed for
opencite:openciteDOI lookup, PDF retrieval, PDF -> markdown, BibTeX export
manuscript:manuscript-writingIMRAD / review-paper structure plus prose discipline (abbreviations, voice, transitions)
manuscript:manuscript-formattingJournal formatting / LaTeX export
manuscript:paper-reviewSelf-review loops on direction-paper drafts
manuscript:humanizerFinal natural-writing pass on synthesized prose. Lit-review synthesis sections are particularly prone to "evolving landscape", "growing body of work", and significance-inflation patterns; run humanizer before declaring a direction paper or Phase 2 synthesis complete.
project:epic-devFormalize the lit-review phases (epic + sub-issues + worktrees + state file); also an upstream source of research angles when reviewing one's own project

References

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 Lit Review AI skill do?

Use this skill for "literature review workflow", "multi-phase lit review", "direction paper", "review paper protocol", "strand-based literature review", "citation-grounded review", "systematic lit review with paper cards", "build a lit review corpus", "lit review pipeline", "orchestrate a literature review", "research directions document", "write a literature review", "synthesize papers", "thematic review", "narrative review", "systematic review", "scoping review", "gap analysis", or when the user wants either a rigorous multi-phase citation-traceable lit review or a single-pass thematic sy...

Why use Lit Review on TypingMind?

Because you install it once and use it with any model. Lit 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 Lit Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neuromechanist/research-skills/tree/main/plugins/manuscript/skills/lit-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 Lit 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 Lit Review?

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

Is the Lit Review AI skill free?

Yes. It is published on GitHub by neuromechanist under the BSD-3-Clause 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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