synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and 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 synthetic-sciences/openscience --skill dataladgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/datalad)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/datalad"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/datalad"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/datalad.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00146 | $0.03903 |
| Opus 5.5 | $0.00058 | $0.01561 |
| Sonnet 5.5 | $0.00029 | $0.00781 |
| Haiku 4.5 | $0.00015 | $0.00390 |
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 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.
This is a copy
94% identical to datalad — 11 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 — 307 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.
That split is the single most important thing to internalise, because it means a freshly
cloned dataset contains the full history and the full file listing while containing almost
none of the data. A 100 TB dataset clones in seconds and occupies a few megabytes. The
bytes arrive only when asked for, per file, 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
uv pip install datalad-container # 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.
- 14d ago Changed · +5 lines afbded5855e6
- 17d ago First seen · 302 lines · 146 tokens per session scan A 64aaa440efb9
datalad is a skill published in the GitHub repository synthetic-sciences/openscience (3,874 stars, last pushed yesterday), licensed Apache-2.0. It adds 146 tokens to every session and 3,903 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to datalad, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
knowledge-shell-process-and-worktree
Shell and Git worktree support for running commands, managing long-lived processes, and giving sessions isolated working directories. A Git worktree is a separate checkout linked to the same repository.
github-pr-workflow
GitHub PR lifecycle: branch, commit, open, CI, merge.
resolve-publish
Commit the validated fix and follow local-only, workflow-owned, or direct PR publication.
meta-pre-commit-quality-gate
Run three quality gates (ruff + mypy + pytest) in parallel over the staged diff, then arbitrate a single BLOCK/APPROVE verdict. Use before committing changes locally when you want a comprehensive pre-commit gate beyond per-file linting — exactly the same gate set CI enforces.
commit
Create git commits with good messages. Use when user says "commit", "create commit", or asks to commit changes.
commit-pr
Mandatory Codex/Copilot publication adapter for opencode-swarm. Use for every GitHub issue assignment that results in code changes, commits, pushes, draft PRs, PR body edits, PR readying, release notes, or CI closeout. Must be loaded before git push, gh pr create, gh pr edit, or gh pr ready. Routes to the single…