Agent skill

Literature Review

by xuzhougeng in xuzhougeng/wisp-science

Retrieve, verify, and synthesize scientific literature. An agent skill from xuzhougeng/wisp-science.

Apache-2.0Auto-check passedResearch & Science

Install Literature Review

skills CLI
$ npx skills add xuzhougeng/wisp-science --skill literature-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xuzhougeng/wisp-science literature-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xuzhougeng/wisp-science.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/literature-review .claude/skills/literature-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
literature-review
GitHub stars
1k
Token cost
~1.7k tokens
SKILL.md length
802 words
Files
2
Skills in repo
25
Repo updated
First seen
Licence
Apache-2.0

At a glance

Retrieve, verify, and synthesize scientific literature. An agent skill from xuzhougeng/wisp-science.

  • Works in 6 steps: Scope the request → Sweep → Expand along the citation graph → …
  • Seminal-paper lookups
  • SKILL.md covers 1. Scope the request, 2. Sweep, 3. Expand along the citation… and 4. Verify, plus 2 more sections
  • Runs Python scripts from its folder; reaches doi.org

What it does

Literature Review is an agent skill from xuzhougeng/wisp-science. 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.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `runtime.py`).

It sits in Research & Science, covering Academic paper search, Literature review and Citation management. The repository describes itself as: Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models. The licence is Apache-2.0.

When your agent uses it

  • Seminal-paper lookups
  • Evidence summaries
  • Method comparisons

Example prompts

  • “/literature-review”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Scope the request
  2. Sweep
  3. Expand along the citation graph
  4. Verify
  5. Write the synthesis
  6. Deliver and lint

What it can do on your machine

Read from SKILL.md and the folder at commit 2ba143b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • doi.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Literature Review loads about 1.7k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 802 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xuzhougeng/wisp-science at commit 2ba143b, republished under its Apache-2.0 licence (© xuzhougeng). 802 words, ~1,690 tokens.

Download SKILL.mdSave it as .claude/skills/literature-review/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
literature-review
description
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.
license
Apache-2.0
wisp.schema_version
1
wisp.domains
scientific-literature
wisp.research_stages
retrieval, validation, synthesis
wisp.roles
retrieval, critic, synthesizer
wisp.evidence_types
literature
wisp.outputs
literature-review, evidence-matrix
wisp.side_effects
network

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.

Show full SKILL.md (326 more words)Show less

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.

© xuzhougeng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/literature-review of xuzhougeng/wisp-science.

  • SKILL.md
  • runtime.py

Open the folder on GitHubat commit 2ba143b

Compare with similar skills

Literature Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Literature Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Literature Review this skillxuzhougeng/wisp-science1k—~1.7kAutomated safety check: PassApache-2.0
Literature Reviewneflibata-feng/MyArxiv-Agent12620 repos~5.9kAutomated safety check: NotesMIT
Preprint Search on bioRxivLigphiDonk/Oh-my--paper73912 repos~3.7kAutomated safety check: PassMIT
Systematic Literature Review Builderbytedance/deer-flow84k2 repos~4.3kAutomated safety check: PassMIT
Paper Research on arXivXiaomiMiMo/MiMo-Code14k—~1.5kAutomated safety check: PassMIT
Literature Review AgentAr9av/PaperOrchestra6791 repos~5.2kAutomated safety check: PassCustom licence

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Questions about Literature Review

What does Literature Review do?

Retrieve, verify, and synthesize scientific literature. An agent skill from xuzhougeng/wisp-science. Literature Review is an agent skill from xuzhougeng/wisp-science. Retrieve, verify, and synthesize scientific literature.

When should I use Literature Review?

Literature Review fits situations like: seminal-paper lookups; evidence summaries; method comparisons.

How do I install Literature Review in Claude Code?

Run `npx skills add xuzhougeng/wisp-science --skill literature-review -a claude-code`. Or copy the skill folder (skills/literature-review in xuzhougeng/wisp-science) into .claude/skills/literature-review in your project. Claude Code loads it when a task matches its description.

How do I install Literature Review in Codex?

Run `npx skills add xuzhougeng/wisp-science --skill literature-review -a codex`. Or copy the skill folder (skills/literature-review in xuzhougeng/wisp-science) into .agents/skills/literature-review in your project. Codex loads it when a task matches its description.

Can I use Literature Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xuzhougeng/wisp-science --skill literature-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/literature-review, .gemini/skills/literature-review, .github/skills/literature-review and .opencode/skills/literature-review in your project.

What does Literature Review need to run?

Going by SKILL.md and its folder, Literature Review needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Literature Review access the network?

SKILL.md names 1 domain. In commands or code: doi.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Literature Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Literature Review use?

Literature Review is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Literature Review use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Literature Review?

Skills that share tags, products or a category with Literature Review: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Preprint Search on bioRxiv (LigphiDonk/Oh-my--paper, 739 stars), Systematic Literature Review Builder (bytedance/deer-flow, 84k stars) and Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Literature Review?

xuzhougeng (a GitHub user) maintains it in xuzhougeng/wisp-science, which has 1,026 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 10, 2026.

Source: xuzhougeng/wisp-science on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.