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 pydicomgit 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/pydicom)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pydicom"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pydicom/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/pydicom"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pydicom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Excessive Agency · line 295 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00052 | $0.04593 |
| Opus 5.5 | $0.00021 | $0.01837 |
| Sonnet 5.5 | $0.00010 | $0.00919 |
| Haiku 4.5 | $0.00005 | $0.00459 |
Grade A, and why
pydicom 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 25d 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
1 near-identical copy found in the catalogue:
- pydicom — 100% identical, 66 lines differ
How it starts
The opening of the file, as written. The whole thing — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pydicom
Use pydicom for DICOM dataset I/O and pixel processing. Version 3.0.2 is the
current stable release reviewed here. It fixes CVE-2026-32711, a crafted
DICOMDIR path-traversal issue. pydicom 3.0.2 declares Python >=3.10; its
bundled DICOM dictionary is 2024c; the current DICOM Standard reviewed here is
2026d. No PACS, DIMSE, DICOMweb endpoint, or authenticated service is exercised
by this local-file skill.
Mandatory safety boundary
- Work only with local data that the user is authorized to access.
- DICOM metadata, file names, private elements, overlays, structured content, and pixels may contain protected health information (PHI).
- Never print
Dataset, export full metadata/JSON, or log element values by default. Use a documented allowlist and aggregate output. - pydicom is a general DICOM framework, not a diagnostic viewer. Pixel output, validation, conversion, and plugin availability are not diagnostic claims.
- De-identification is profile-, purpose-, recipient-, jurisdiction-, and threat-context-specific. It requires privacy/DICOM expert verification.
- Never claim that a tag-removal script is DICOM PS3.15, HIPAA, GDPR, or other compliance. Preserve originals and audit derived outputs.
- Treat deterministic pseudonymization keys and UID maps as re-identification secrets: use least privilege and encrypted/managed secret storage, never commit, sync, log, or share them with derivatives, and define backup, rotation, revocation, and destruction procedures. A leaked key invalidates the intended separation; rotation also changes deterministic mappings.
- Set explicit input-file, file-count, frame-count, decoded-byte, and output limits before parsing untrusted or unusually large datasets.
Installation
Create or activate an isolated environment, then install the reviewed release (the pixel-stack pins below require Python 3.12+ because of NumPy):
uv pip install "pydicom==3.0.2"
Uncompressed pixel arrays and image rendering:
What ships with it
12 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.
- references/common_tags.md 13 KB
- references/transfer_syntaxes.md 15 KB
- scripts/__init__.py 60 B runs code
- scripts/_common.py 29 KB runs code
- scripts/anonymize_dicom.py 25 KB runs code
- scripts/deidentification_audit.py 13 KB runs code
- scripts/dicom_inventory.py 13 KB runs code
- scripts/dicom_to_image.py 17 KB runs code
- scripts/extract_metadata.py 12 KB runs code
- scripts/pixel_frame_planner.py 10 KB runs code
- scripts/transfer_syntax_inspector.py 7.7 KB runs code
- scripts/uid_mapping_validator.py 8.5 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.
- 25d ago Changed · +22 lines · -4 tokens per session e42c35688a3d
- 26d ago Changed · +8 lines 217d0a040765
- 1mo ago First seen · 399 lines · 56 tokens per session scan A 3c8d3a5880d5
pydicom is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 4,593 once invoked, about $0.0002 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
digital-twin-discharge-drafter
Use when drafting patient discharge summaries, creating personalized discharge instructions, simulating post-discharge outcomes, reducing hospital readmissions, or optimizing care transitions. Generates AI-enhanced discharge documentation with digital twin predictions for improved patient safety.
discharge-summary-writer
Generate hospital discharge summaries from admission data, hospital course, diagnoses, procedures, and follow-up plans; use when a structured discharge document is required from clinical notes.
paper-figure
Generate publication-quality figures and tables from experiment results. Use when user says "plot this", "make a figure", "generate figures", "paper figures", or needs plots for a paper. Also invoked by /auto's Ledger Figures hook to produce per-claim figures embedded into CLAIMSLEDGER.md.
ape-write-paper
Writes a polished, standard two-column research paper (LaTeX to PDF) from any source material -- code, docs, experiment logs, benchmark files, notes -- optimising for correctness, readability, structure, figures, tables and references. Supports both standalone papers and conference templates (ACM SIGMOD/VLDB, IEEE…
LaTeX工具
A tool for creating, compiling, and checking mathematical modelling papers written in LaTeX, a document system often used for technical writing.
thesis-consistency-audit
A consistency audit for quantitative master’s and doctoral theses in management, finance, or strategy. It checks whether numbers, tables, analyses, and claims agree, and can inspect hidden author information in office documents.