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 AndyZhuang/Opentest --skill generate_scientific_method_sectiongit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/generate_scientific_method_section)<a href="https://agentmods.dev/skills/andyzhuang/opentest/generate_scientific_method_section"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/generate_scientific_method_section/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/andyzhuang/opentest/generate_scientific_method_section"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/generate_scientific_method_section.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 | $0.00102 | $0.05513 |
| Opus 5 | $0.00051 | $0.02756 |
| Sonnet 5 | $0.00020 | $0.01103 |
| Haiku 4.5 | $0.00010 | $0.00551 |
Grade A, and why
generate_scientific_method_section 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 8d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Scientific Method Section
Overview
generate_scientific_method_section closes the LabOS "from bench to paper" loop by automatically drafting the Methods section of a SCI manuscript directly from machine-readable experiment records. It ingests heterogeneous upstream artifacts — LabOS skill execution logs, structured JSON from video analysis pipelines, protocols.io or Benchling ELN entries, reagent inventory metadata, and statistical analysis outputs — extracts every parameter, reagent, instrument, and procedural decision, and synthesizes them into complete, journal-ready Methods prose following IMRAD conventions. Output is LaTeX or Markdown with numbered subsections, in-text citations formatted for a target journal style, and a reproducibility checklist, eliminating the most time-consuming transcription step between bench work and manuscript submission.
When to Use This Skill
Use this skill when any of the following conditions are present:
- Post-experiment write-up: An experiment has been completed and its execution records (LabOS logs, ELN entries, video analysis JSONs) are available; the next step is to draft the Methods section without manually transcribing every parameter and reagent.
- LabOS pipeline completion: A multi-skill LabOS execution chain (
extract_experiment_data_from_video→analyze_lab_video_cell_behavior→generate_cell_analysis_charts) has finished and the agent must now document what was done in manuscript form. - Protocol-to-manuscript conversion: A structured protocols.io or Benchling protocol was followed (with or without deviations logged by
protocol_video_matching) and must be converted from step-list format to flowing SCI-style prose. - Compliance-driven documentation: A regulated workflow (GLP/GMP, clinical research) requires that the exact executed procedure — including any deviations — be documented in a standardized textual format for submission or audit.
- Reproducibility package preparation: A paper is being submitted with a reproducibility requirement (Nature Methods, eLife, PLOS ONE) and the Methods section must contain every parameter needed to fully replicate the experiment.
- Multi-experiment manuscript: Several related experiments were run across different sessions; their individual logs must be merged into a coherent, unified Methods section with appropriate cross-references.
- Revision round: A reviewer requests more detail in the Methods; the original execution logs are mined to surface omitted parameters, instrument settings, or statistical choices.
- Collaborative lab writing: A trainee performed the experiment; the skill auto-drafts the Methods from their ELN entry so a senior author can review and annotate rather than write from scratch.
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.
- 8d ago First seen · 310 lines · 102 tokens per session scan A 0155eb2a0cce
generate_scientific_method_section is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 102 tokens to every session and 5,513 once invoked, about $0.0005 per session on Opus 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.
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