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 citation-managementgit 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/citation-management)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/citation-management"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/citation-management/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/citation-management"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/citation-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- medium Data Exfiltration · line 269 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 272 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00072 | $0.03787 |
| Opus 5.5 | $0.00029 | $0.01515 |
| Sonnet 5.5 | $0.00014 | $0.00757 |
| Haiku 4.5 | $0.00007 | $0.00379 |
Grade A, and why
citation-management 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 14d 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
2 near-identical copies found in the catalogue:
- citation-management — 100% identical, 0 lines differ
- citation-management — 92% identical, 57 lines differ
How it starts
The opening of the file, as written. The whole thing — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Management
Overview
Manage citations systematically throughout the research and writing process. This skill provides tools and strategies for searching academic databases (Google Scholar, PubMed), extracting accurate metadata from multiple sources (CrossRef, PubMed, arXiv), validating citation information, and generating properly formatted BibTeX entries.
Critical for maintaining citation accuracy, avoiding reference errors, and ensuring reproducible research. Integrates seamlessly with the literature-review skill for comprehensive research workflows.
When to Use This Skill
Use this skill when:
- Searching for specific papers on Google Scholar or PubMed
- Converting DOIs, PMIDs, or arXiv IDs to properly formatted BibTeX
- Extracting complete metadata for citations (authors, title, journal, year, etc.)
- Validating existing citations for accuracy
- Cleaning and formatting BibTeX files
- Finding highly cited papers in a specific field
- Verifying that citation information matches the actual publication
- Building a bibliography for a manuscript or thesis
- Checking for duplicate citations
- Ensuring consistent citation formatting
If a document built from these citations needs a diagram, use the scientific-schematics skill.
Core Workflow
Citation management follows a systematic process. Each phase below shows the canonical command; every variant, option, and metadata-source detail is in references/core_workflow.md.
Phase 1: Paper Discovery and Search
Find relevant papers. Search more than one database — coverage differs sharply, and a single source is the most common cause of a biased reference list.
# OpenAlex: multidisciplinary REST API; optional OPENALEX_API_KEY for account quota
python scripts/search_openalex.py "CRISPR gene editing" --limit 50 --output results.json
# PubMed: the authority for biomedical and life sciences (35M+ citations)
python scripts/search_pubmed.py "Alzheimer's disease treatment" --limit 100 --output alz.json
# Google Scholar: broadest reach, but scraped -- rate-limited and prone to blocking
python scripts/search_google_scholar.py "CRISPR gene editing" --limit 50 --output scholar.json
What ships with it
21 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/bibtex_template.bib 9.1 KB
- assets/citation_checklist.md 10 KB
- references/api_contracts.md 6.6 KB
- references/best_practices.md 2.7 KB
- references/bibtex_formatting.md 19 KB
- references/citation_validation.md 19 KB
- references/core_workflow.md 20 KB
- references/example_workflows.md 3.3 KB
- references/google_scholar_search.md 18 KB
- references/metadata_extraction.md 21 KB
- references/pubmed_search.md 19 KB
- references/script_reference.md 7.4 KB
- references/search_strategies.md 2.7 KB
- scripts/_common.py 11 KB runs code
- scripts/doi_to_bibtex.py 6.6 KB runs code
- scripts/extract_metadata.py 29 KB runs code
- scripts/format_bibtex.py 11 KB runs code
- scripts/search_google_scholar.py 8.5 KB runs code
- scripts/search_openalex.py 12 KB runs code
- scripts/search_pubmed.py 15 KB runs code
- scripts/validate_citations.py 28 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.
- 14d ago Changed · +5 lines dc79fe1e8438
- 15d ago Changed · +8 lines 764674c49090
- 1mo ago Changed · +17 lines 24b73d1d84e3
- 1mo ago First seen · 330 lines · 72 tokens per session scan A e52459a2c78f
citation-management is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 3,787 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 2026-08-30.
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