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 agentlas-ai/agentlas-desktop --skill dataladgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/datalad)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/datalad"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/datalad"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.03833 |
| Opus 5.5 | $0.00058 | $0.01533 |
| Sonnet 5.5 | $0.00029 | $0.00767 |
| Haiku 4.5 | $0.00015 | $0.00383 |
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 1mo 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
100% identical to datalad — 104 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 — 300 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.
- 1mo ago First seen · 300 lines · 146 tokens per session scan A 13b11667b0cb
datalad is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 146 tokens to every session and 3,833 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to datalad, differing in 104 lines, and is treated as a copy.
Other skills, from other repositories
github-copilot-upgrader
Use this to update the Github Copilot CLI/SDK.
commit
Commit staged or unstaged changes with an AI-generated commit message that matches the repository's existing commit style. Use when the user asks to 'commit', 'commit changes', 'create a commit', 'save my work', or 'check in code'.
manuscript-as-code
Treat manuscripts as software: version control, reproducible builds, figure pipelines, CI, and structured repo layout. Helps teams avoid 'finalv7' chaos and ensures submission-ready artifacts.
oz-report-pr
Report a pull request back to the Oz platform after creating one.
latex-diff
Compare two LaTeX files, project directories, or Git revisions and produce human-readable plus machine-readable severity-graded semantic changes. Use when determining what changed between manuscript versions or preparing a revision audit bundle. Not for proofreading one version; use $proofread.
commit
Use when making any git commit. Pass the failure, false claim, or risk the changes fix as the argument; for an addition or a filled gap, pass what it adds — never an invented why.