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_cell_analysis_chartsgit 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_cell_analysis_charts)<a href="https://agentmods.dev/skills/andyzhuang/opentest/generate_cell_analysis_charts"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/generate_cell_analysis_charts/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_cell_analysis_charts"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/generate_cell_analysis_charts.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 | $0.00094 | $0.03906 |
| Opus 5 | $0.00047 | $0.01953 |
| Sonnet 5 | $0.00019 | $0.00781 |
| Haiku 4.5 | $0.00009 | $0.00391 |
Grade A, and why
generate_cell_analysis_charts 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Cell Analysis Charts
Overview
generate_cell_analysis_charts is the visualization layer of the LabOS cell-video analysis pipeline. It ingests the structured JSON payload produced by analyze_lab_video_cell_behavior (or any schema-compatible source) and renders a curated set of cell-biology-specific figures using matplotlib and seaborn — from population growth curves with 95% CI bands to color-coded single-cell trajectory overlays and 96-well compliance heatmaps — then saves each figure as a print-ready PNG or vector PDF suitable for journal submission, ELN attachment, or real-time XR spatial display.
When to Use This Skill
Use this skill when any of the following conditions are present:
- Downstream of cell video analysis:
analyze_lab_video_cell_behavior(or an equivalent tracking pipeline) has produced a structured JSON result and the next step is to visualize it — without writing ad-hoc plotting code from scratch. - Publication figure preparation: A manuscript or poster requires one or more standard cell biology figures (growth curve, trajectory map, phenotype distribution, MSD plot) at 300 DPI with colorblind-safe palettes and clean axes styling.
- ELN / Benchling figure attachment: A post-experiment summary must include standardized charts appended to a Benchling ELN entry or protocols.io run record.
- Multi-well plate visualization: A high-content screening experiment (96- or 384-well) yielded per-well metrics that need to be rendered as a plate heatmap for quick hit identification.
- Drug dose-response reporting: Per-well doubling times or apoptosis rates from a cytotoxicity experiment need to be plotted on a log-dose axis with a sigmoidal fit and IC50 annotation.
- XR spatial dashboard: Live or post-hoc cell metrics need to be rendered as lightweight PNG panels to embed in an XR overlay above the microscope stage via LabOS.
- Batch multi-experiment comparison: Several JSON files from different conditions, cell lines, or time points need to be overlaid on a single comparison figure with automatic legend and color assignment.
- Report or slide deck generation: Downstream skills (
pptx-generation,scientific-writing,latex-posters) need pre-rendered figure files with predictable filenames and standardized aspect ratios.
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 · 251 lines · 94 tokens per session scan A a0d45431b9e5
generate_cell_analysis_charts is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 94 tokens to every session and 3,906 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…