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 HolobiomicsLab/asb-skill-collections --skill simulation-dataset-validation-benchmarkinggit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collectionsWrote 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/holobiomicslab/asb-skill-collections/simulation-dataset-validation-benchmarking)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/simulation-dataset-validation-benchmarking"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/simulation-dataset-validation-benchmarking/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/holobiomicslab/asb-skill-collections/simulation-dataset-validation-benchmarking"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/simulation-dataset-validation-benchmarking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.01249 |
| Opus 5 | $0.00026 | $0.00624 |
| Sonnet 5 | $0.00010 | $0.00250 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
simulation-dataset-validation-benchmarking 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 6d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
simulation-dataset-validation-benchmarking
Summary
Validate that a DNA methylation analysis tool produces expected results when applied to a known simulation dataset with documented behavior. This skill ensures tool functionality and correct parameter settings by comparing observed outputs against established ground truth for synthetic data.
When to use
When you have installed or updated a DNA methylation analysis tool (e.g., ChAMP) and need to verify that it produces documented expected outputs on a reference simulation dataset before applying it to real experimental data. Specifically useful after version upgrades, parameter changes, or when integrating a tool into a new analysis pipeline.
When NOT to use
- When analyzing real experimental methylation data—simulation datasets are synthetic and do not represent true biological variation; use them only for tool validation, not for biological interpretation.
- When the simulation dataset's documented expected behavior is unknown or unavailable—validation requires an explicit ground-truth specification.
Inputs
- Simulation methylation dataset (e.g., EPICSimData loaded via data() in R)
- Array-type identifier string (e.g., 'EPIC', '450k')
Outputs
- Block detection results object or report
- Interactive visualization (e.g., Block.GUI() output)
- Validation pass/fail confirmation against expected behavior
How to apply
Load the simulation dataset (e.g., EPICSimData for EPIC array type) into the R environment using the appropriate data() call. Execute the analysis function with explicitly declared array-type parameters matching the simulation dataset's design (arraytype='EPIC'). Run any associated interactive visualization or inspection tools (e.g., Block.GUI()) to examine results. Verify that the observed output matches the documented expected behavior for that synthetic dataset—in this case, confirming the absence of differentially methylated blocks when none should be detected. If outputs deviate from expectations, investigate parameter settings or tool version compatibility before proceeding to real data.
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.
- 6d ago First seen · 92 lines · 52 tokens per session scan A 198497c6f91e
simulation-dataset-validation-benchmarking is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,249 once invoked, about $0.0003 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 2026-09-06.
Other skills, from other repositories
external-model-validation
Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis…
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
medical-research-literature-reader-pro
A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…
adverse-event-narrative
Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple.
decision-curve-analysis
Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…