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 dataladgit 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/datalad)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/datalad"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/datalad/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/datalad"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/datalad.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00147 | $0.04231 |
| Opus 5.5 | $0.00059 | $0.01692 |
| Sonnet 5.5 | $0.00029 | $0.00846 |
| Haiku 4.5 | $0.00015 | $0.00423 |
Grade A, and why
datalad 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.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataLad
Overview
DataLad is a data management layer over Git and git-annex. Git tracks the dataset structure, small text files, and the history. git-annex tracks the content of large files, storing each file as a key and keeping the bytes somewhere that is not necessarily the local repository.
A normal clone retrieves Git history and the top-level file listing while leaving
annexed bytes unfetched. Installed subdatasets have their own histories; a clone does not
automatically populate them. Clone cost depends on Git history and file count, not just
the data volume. Retrieve annexed bytes selectively with datalad get.
The second thing DataLad adds is provenance. datalad run executes a command and commits
the result together with a machine-readable record of the command, its inputs, and its
outputs. datalad rerun reads that record back and re-executes it. This turns "how was
this figure produced" from an archaeology problem into a command.
When to use DataLad instead of plain Git
Use DataLad when any of the following holds:
- Files are too large for Git to handle comfortably, or the total exceeds what every collaborator wants on disk.
- Data lives in more than one place (a lab server, a cluster scratch, S3, a supercomputer) and you need to know which copies exist.
- The analysis must be re-executable, and a plain commit message is not enough evidence.
- You are consuming published datasets from OpenNeuro, DANDI, or
datasets.datalad.org, which are distributed as DataLad datasets. - The project nests other datasets inside it and you want each one to keep its own independent history.
Use plain Git when the repository is code and text only, everything fits comfortably in Git, and nobody needs partial checkouts. DataLad on top of a small pure-code repository adds indirection without buying anything.
Installation
# git-annex is NOT written in Python but is available from PyPI if you already
# have git itself installed:
uv pip install git-annex
# You can also install it first from the system
# (Debian/Ubuntu: apt install git-annex; macOS: brew install git-annex;
# conda-forge: conda install -c conda-forge git-annex)
uv pip install "datalad==1.6.5"
uv pip install "datalad-container==1.2.6" # only for containers-run
datalad wtf --section dependencies # confirm git-annex version is visible
What ships with it
3 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.
- 25d ago Changed · +28 lines · +1 tokens per session bb902427db0c
- 26d ago Changed · +2 lines 3b30ff0b4860
- 1mo ago First seen · 300 lines · 146 tokens per session scan A 13b11667b0cb
datalad is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed yesterday), licensed MIT. It adds 147 tokens to every session and 4,231 once invoked, about $0.0006 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 2026-09-12.
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…
git-flow
Use when committing, branching, opening PRs, or deciding merge/branch strategy.
post-commit
Use when a code commit just landed (not wip/amend/undo) and CHANGELOG or plugin-version post-commit actions are needed.
commit-detection
Detects optimal commit type from git changes. Use when analyzing commits, determining commit type, or before committing.
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.