Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add K-Dense-AI/scientific-agent-skills --skill literature-reviewgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/literature-review)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/literature-review"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/literature-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/literature-review"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/literature-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Supply Chain · line 224 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Tool Misuse · line 224 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
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 | $0.00082 | $0.02841 |
| Opus 5.5 | $0.00033 | $0.01136 |
| Sonnet 5.5 | $0.00016 | $0.00568 |
| Haiku 4.5 | $0.00008 | $0.00284 |
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 26d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- literature-review — 100% identical, 153 lines differ
- literature-review — 89% identical, 153 lines differ
- literature-review — 89% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 230 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.
Use discipline-appropriate bibliographic databases for the reproducible search, with parallel-web (parallel-cli search) for scoping and supplementary discovery. Ranked web results and extracted excerpts cannot establish exhaustive coverage or substitute for full-text assessment. Bundled scripts process normalized records, check DOI registration, and render documents; they do not run a complete systematic review automatically.
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
Figures and PRISMA reporting
For systematic reviews, use the appropriate PRISMA 2020 flow template and reconciled screening counts. Distinguish records, reports, and studies, including reports not retrieved and reasons for full-text exclusions. Use tables or deterministic plotting for exact numbers, effect estimates, and risk-of-bias results.
Conceptual schematics are optional. The bundled generator needs OPENROUTER_API_KEY and creates a PNG draft with up to two generation/review iterations:
python scripts/generate_schematic.py "Conceptual evidence map; labels from the reviewed extraction table" \
-o figures/evidence_map.png --doc-type journal --iterations 2
Generation uses OpenRouter's POST /api/v1/images with
google/gemini-3.1-flash-image; review uses POST /api/v1/chat/completions with
google/gemini-3.7-flash. The output path must end in .png. Inspect labels,
counts, arrows, and accessibility yourself: success means an image was saved,
whereas quality_met, final_reviewed, and termination_reason in the review
log describe the automated review. Neither proves scientific accuracy. A failed
refinement preserves the previous draft. Check current model availability before
running paid generation; do not fabricate study counts in an image prompt.
What ships with it
11 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.
- assets/review_template.md 13 KB
- references/citation_styles.md 7.4 KB
- references/core_workflow.md 12 KB
- references/database_strategies.md 10 KB
- references/example_workflow.md 3.0 KB
- references/search_and_citation.md 4.6 KB
- scripts/generate_pdf.py 4.9 KB runs code
- scripts/generate_schematic_ai.py 35 KB runs code
- scripts/generate_schematic.py 7.6 KB runs code
- scripts/search_databases.py 8.6 KB runs code
- scripts/verify_citations.py 9.7 KB runs code
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.
- 26d ago Changed · -50 lines · -4 tokens per session scan C → A 16a9e85bcdcb
- 27d ago Changed · -1 lines b19b0015d0ee
- 1mo ago First seen · 281 lines · 86 tokens per session scan C 950a6d0863ef
literature-review is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 2,841 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
scholion
Second-opinion support for individuals and clinicians, one person's data at a time — genome, laboratory history, prescriptions, wearables. It reads them locally and states what the data cannot support. No data is baked into the skill: everyone supplies their own. Use it when you need to check a physician's…
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.