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 dependency-installation-and-verificationgit 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/dependency-installation-and-verification)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dependency-installation-and-verification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dependency-installation-and-verification/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/dependency-installation-and-verification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dependency-installation-and-verification.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 | $0.00032 | $0.01997 |
| Opus 5 | $0.00016 | $0.00999 |
| Sonnet 5 | $0.00006 | $0.00399 |
| Haiku 4.5 | $0.00003 | $0.00200 |
Grade A, and why
dependency-installation-and-verification 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 9d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dependency-installation-and-verification
Summary
Automatically detect, configure, and validate critical bioinformatics tool dependencies (samtools, bowtie2, Python libraries) before executing a multi-stage Hi-C processing pipeline. This skill ensures reproducibility by recording resolved tool paths and versions in a system configuration file.
When to use
When setting up HiC-Pro or similar multi-tool pipelines where tool availability and version constraints are prerequisites for downstream analysis. Use this skill at pipeline initialization time, before any data processing steps (alignment, SAM post-processing, normalization) begin, to avoid runtime failures due to missing or incompatible dependencies.
When NOT to use
- When all dependencies are already manually installed and their paths are hardcoded in an existing config-system.txt file — skip this skill and proceed directly to pipeline execution.
- When running HiC-Pro via a pre-built container (Docker, Singularity, conda environment) — dependency management is handled by the container image itself.
- When working with a subset of the pipeline that does not require the full dependency chain (e.g., post-hoc normalization scripts that only need R and iced, not bowtie2).
Inputs
- config-install.txt configuration template file
- system PATH environment variable
- installed tool binaries (samtools, bowtie2, Python interpreter)
Outputs
- config-system.txt — resolved system configuration file with validated tool paths and versions
- installation log or status report indicating which dependencies were auto-installed vs. detected
How to apply
Edit the config-install.txt file to specify full paths to required tools (samtools >=1.9, bowtie2, Python >3.7 with pysam, bx-python, numpy, scipy) or leave paths unset to trigger automatic detection via system PATH using the 'which' command. Execute 'make CONFIG_SYS=config-install.txt install' to invoke the dependency checking and configuration workflow. This command validates tool availability, resolves version requirements, and generates a config-system.txt file containing the confirmed paths and versions of all detected tools. Verify the generated config-system.txt records all required tools with appropriate versions (samtools >=1.9), confirming readiness for downstream SAM/BAM post-processing and Hi-C data normalization operations.
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.
- 9d ago First seen · 109 lines · 32 tokens per session scan A 4292b030e991
dependency-installation-and-verification is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 1,997 once invoked, about $0.0002 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
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…
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…
elastic-net-feature-selection
Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…