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 export_experiment_data_to_excelgit 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/export_experiment_data_to_excel)<a href="https://agentmods.dev/skills/andyzhuang/opentest/export_experiment_data_to_excel"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/export_experiment_data_to_excel/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/export_experiment_data_to_excel"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/export_experiment_data_to_excel.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 | $0.00079 | $0.03305 |
| Opus 5 | $0.00039 | $0.01653 |
| Sonnet 5 | $0.00016 | $0.00661 |
| Haiku 4.5 | $0.00008 | $0.00331 |
Grade A, and why
export_experiment_data_to_excel 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Export Experiment Data to Excel
Overview
export_experiment_data_to_excel transforms heterogeneous experimental data — JSON from video analysis pipelines, pandas DataFrames, time-series arrays, nested protocol outputs — into clean, human-readable Excel workbooks. The skill auto-assigns logical sheet names (e.g., Raw Data, Growth Curves, Cell Counts, Population Metrics), prepends unit and metadata annotation rows, applies consistent column widths and header styling, and emits a single .xlsx file ready for lab notebooks, ELN attachment, regulatory submission, or downstream statistical analysis. It bridges the gap between machine-generated structured data and the Excel-centric workflows that many wet-lab researchers and collaborators expect.
When to Use This Skill
Use this skill when any of the following conditions are present:
- LabOS pipeline export: Output from
extract_experiment_data_from_video,analyze_lab_video_cell_behavior, orgenerate_cell_analysis_chartsis in JSON or tabular form and must be shared as an Excel file for collaborators or PI review. - Multi-sheet report assembly: Several related datasets (raw counts, summary metrics, time-series curves, per-well results) must be organized into one workbook with clearly named sheets rather than scattered CSV files.
- Unit and provenance documentation: Data columns require explicit unit headers (µL, h⁻¹, µm/h, %) and annotation rows (experiment ID, date, protocol version) so recipients understand the data without reading separate metadata files.
- ELN or Benchling attachment: A Benchling ELN entry or protocols.io experiment record requires an Excel file as an attached data object; the skill produces a formatted file suitable for direct upload.
- Regulatory or audit trail: GLP/GMP or audit documentation requires data in a fixed, non-editable (or track-changes) format; Excel with frozen headers and annotation rows meets many lab compliance requirements.
- Collaborator handoff: A non-computational collaborator or external lab needs data in Excel for manual inspection, plotting in Excel/GraphPad, or import into proprietary analysis software.
- Statistical analysis prep: Data will be imported into R, SPSS, GraphPad Prism, or pandas; the skill ensures column names are valid, units are explicit, and missing values are consistently encoded (e.g.,
NA, empty, or—). - Batch experiment export: Multiple experiments or conditions are consolidated into one workbook with one sheet per condition, or one sheet per time point, for side-by-side comparison.
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 · 213 lines · 79 tokens per session scan A c3eb1181d293
export_experiment_data_to_excel is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 79 tokens to every session and 3,305 once invoked, about $0.0004 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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