literature-review
by K-Dense-AIThe literature-review skill supports systematic literature-review workflows for Academic Research, including source discovery, citation verification, thematic synthesis, and polished markdown or PDF outputs. Use it for literature-review guide tasks, meta-analyses, scoping reviews, and research briefs across scientific and technical domains.
This skill scores 84/100, which means it is a solid directory candidate: users should have enough workflow guidance and citation-focused functionality to install it with confidence, though it is not fully self-contained. The repository evidence shows a substantial, non-placeholder skill aimed at systematic literature reviews and research synthesis, with enough structure to reduce guesswork compared with a generic prompt.
- Clear use cases for systematic reviews, meta-analyses, scoping reviews, and literature-review sections, making triggering straightforward.
- Substantial operational content: long SKILL.md, many headings, workflow and constraint signals, and explicit support for citation verification and document generation.
- Specific tool orientation for academic search and synthesis (parallel-web search plus specialized database access), which adds agent leverage beyond a general-purpose prompt.
- No install command, support files, or supplemental resources were provided, so adoption may require users to infer setup and tool availability.
- The excerpted repository evidence does not show end-to-end run examples or quick-start steps, so first-use confidence may depend on reading the full skill carefully.
Overview of literature-review skill
The literature-review skill helps you run a serious, source-backed literature-review workflow instead of relying on a generic prompt. It is best for Academic Research tasks where you need broad discovery, citation verification, thematic synthesis, and polished deliverables in markdown or PDF.
What this skill is for
Use the literature-review skill when you need to survey a topic across multiple scholarly sources, not just summarize a few papers. It is suited to systematic reviews, scoping reviews, meta-analyses, thesis chapters, and research briefs.
Who benefits most
It fits researchers, graduate students, analysts, and technical writers who need a reproducible literature-review guide with fewer missed sources and better citation discipline. If your goal is “find, verify, synthesize, and present,” this skill is a strong match.
Why it stands out
Unlike a simple literature-review prompt, this skill is designed around multi-database discovery, citation checking, and document generation. That makes it more useful when source quality, traceability, and format matter to the final output.
How to Use literature-review skill
Install and inspect the workflow
Install with npx skills add K-Dense-AI/claude-scientific-skills --skill literature-review, then read SKILL.md first. In this repo, there are no extra support files to unpack, so the core workflow lives in the skill document itself.
Give the skill a research-ready brief
A strong literature-review usage request should include the question, scope, date range, domain, and output type. For example: “Review 2020–2025 evidence on X in biomedical settings, focus on review-quality sources, compare findings thematically, and return APA citations with a short methods note.”
Use inputs that reduce ambiguity
If you only say “write a literature review on AI in healthcare,” results will be broad and uneven. Better inputs specify population, intervention, comparison, outcomes, exclusion rules, and whether you want a narrative review, systematic review draft, or source map.
Practical reading order in the repo
Start with SKILL.md, then inspect any linked references or inline workflow notes inside it. Because this repository has a shallow file tree, the main adoption question is less “where are the files?” and more “does the workflow fit my review protocol?”
literature-review skill FAQ
Is the literature-review skill only for academics?
No. It is useful for any literature-review for Academic Research, including product research, technical due diligence, and evidence summaries. The key requirement is that you need sourced synthesis rather than casual brainstorming.
How is this different from a normal prompt?
A normal prompt can summarize a few known papers, but the literature-review skill is built for search, verification, and structured synthesis. That matters when you need better coverage and fewer citation errors.
Is it beginner-friendly?
Yes, if you can define a clear question and accept a structured workflow. The main beginner risk is asking for too wide a topic without constraints, which usually produces shallow coverage.
When should I not use it?
Do not use the literature-review skill for quick opinions, single-source summaries, or non-academic brainstorming. If you do not need verified citations or a defensible search process, a simpler prompt is faster.
How to Improve literature-review skill
Narrow the question before you ask
The biggest quality gain comes from sharpening the topic. Replace “review machine learning in medicine” with something like “review transformer models for radiology report generation, 2021–2025, with emphasis on evaluation metrics and clinical limitations.”
Tell it what counts as evidence
The literature-review guide works better when you define acceptable source types: review articles, primary studies, clinical trials, preprints, or conference papers. If you care about recency, methods quality, or database coverage, say so explicitly.
Ask for synthesis shape, not just a dump of papers
Better outputs come when you specify the structure you want: themes, timeline, controversies, evidence table, gap analysis, or conclusions for a thesis section. That forces the literature-review skill to organize findings instead of merely collecting them.
Iterate with missing-source checks
After the first pass, ask what was excluded, which databases or clusters were thin, and whether key terms should be expanded. This is the fastest way to improve a literature-review install decision in practice: you see whether the workflow is thorough enough for your topic.