Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/x-cmd/skillnpx agentmods add skills/x-cmd/skill/literature-reviewWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/x-cmd/skill/literature-review)<a href="https://agentmods.dev/skills/x-cmd/skill/literature-review"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/literature-review.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.05700 |
| Opus 5 | $0.00043 | $0.02850 |
| Sonnet 5 | $0.00017 | $0.01140 |
| Haiku 4.5 | $0.00009 | $0.00570 |
Grade A, and why
literature-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to literature-review — 107 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 639 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Review
Overview
Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.
This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.
When to Use This Skill
Use this skill when:
- Conducting a systematic literature review for research or publication
- Synthesizing current knowledge on a specific topic across multiple sources
- Performing meta-analysis or scoping reviews
- Writing the literature review section of a research paper or thesis
- Investigating the state of the art in a research domain
- Identifying research gaps and future directions
- Requiring verified citations and professional formatting
Visual Enhancement with Scientific Schematics
⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.
This is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:
- Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews)
- Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)
How to generate figures:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 639 lines · 86 tokens per session scan A c8bf7cdb2374
literature-review is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 86 tokens to every session and 5,700 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to literature-review, differing in 107 lines, and is treated as a copy.
Other skills, from other repositories
kaggle-research-compute
Use when a research or engineering task needs automatic heavy-compute routing to free Kaggle Kernels through the local broker, with agent-driven push, poll, fetch, and a multi-run resume loop across concurrent kernels; free CPU (quota-free) and GPU under a self-imposed weekly GPU-hour cap.
lean-strict-verification-gate
Use when checking whether a Lean artifact can safely support a research claim.
modal-research-compute
Use when a research or engineering task needs automatic heavy-compute routing through the unified local broker, including Modal-backed remote CPU, high-memory CPU, or GPU execution.
digest-bridge
Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests.
lean-research-library
Use when any Lean formalization task starts (reuse Mathlib and the personal research library before proving anything new) and when it ends (gate finished results into the library and flag mathlib-PR candidates, always asking the user first). Also scaffolds and publishes paper artifacts from the personal template.
venue-ranking-evidence
Use when identifying a journal, conference, or proceedings series from a partial name, acronym, alias, ISSN, or source ID; preserving source-specific rank, quartile, metric, classification, membership, or coverage observations; or proving that the public ICORE detail page displayed one ICORE claim. Live bulk paths are…