Open Science is a local-first, model-agnostic workbench for reproducible scientific research. Scientists use its AI agents, Python and R execution, data connectors, and traceable outputs for tasks such as literature review, analysis, simulation, and visualization across macOS, Windows, and Linux.
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 aipoch/open-science --skill docx-generationgit clone --depth 1 https://github.com/aipoch/open-scienceWrote 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/aipoch/open-science/docx-generation)<a href="https://agentmods.dev/skills/aipoch/open-science/docx-generation"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/docx-generation/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/aipoch/open-science/docx-generation"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/docx-generation.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 | $0.00071 | $0.01012 |
| Opus 5.5 | $0.00028 | $0.00405 |
| Sonnet 5.5 | $0.00014 | $0.00202 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
docx-generation 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 2d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DOCX generation and editing
Choose a workflow that preserves the user's requested content, structure, and presentation. User requirements take precedence over defaults. Keep tables, headings, and lists as native document structures.
Markdown to a new DOCX
For ordinary reports, use the bundled converter with an existing Pandoc 3.12.1 executable and Python 3.9 or newer. It uses a versioned reference document, separates ordinary table paragraphs from the Compact style shared by lists, and records converter/template provenance inside the DOCX. It does not install software. Run this converter rather than a bare pandoc source.md -o output.docx command: that command bypasses the report template and its typography, page geometry, table spacing, and provenance.
In Open Science, first prepare the workflow through the registered Notebook helper:
{
"kernelSkillIds": ["docx-generation"],
"code": "docx_workflow = prepare_docx_conversion('.')\nprint(docx_workflow)"
}
Call the function directly, without importing it. Repeat kernelSkillIds in every cell using this function. The helper writes the fixed converter and reference into a new directory inside the authorized Notebook working directory and returns script_path, reference_path, and workflow_directory. This uses the registered skill transport; the Notebook and shell cannot necessarily read the framework's skill directory.
Then run the returned script_path through the session's approved shell tool:
python "<returned-script_path>" "<source.md>" "<output.docx>"
Use the interpreter configured for the session. If the helper is unavailable, report that prerequisite through the normal execution recovery flow; do not probe or copy from the isolated framework skill directory. In environments outside Open Science that already allow reading this package, the original scripts/convert.py is also a standalone CLI.
Optional arguments:
--reference-doc "<template.docx>": use the user's template. Its styles and page settings take precedence; the bundled TableBody normalization is not applied.--from gfmor--from markdown+pipe_tables: select the dialect that matches the source. The default is Pandoc Markdown.--pandoc "<absolute-executable-path>": select an existing executable.
What ships with it
7 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.
- 2d ago First seen · 55 lines · 71 tokens per session scan A 519810b35cf8
docx-generation is a skill published in the GitHub repository aipoch/open-science (5,398 stars, last pushed today), licensed Apache-2.0. It adds 71 tokens to every session and 1,012 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 2026-10-28.
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