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 peak-calling-output-interpretationgit 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/peak-calling-output-interpretation)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/peak-calling-output-interpretation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/peak-calling-output-interpretation/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/peak-calling-output-interpretation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/peak-calling-output-interpretation.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 | $0.00034 | $0.01665 |
| Opus 5 | $0.00017 | $0.00833 |
| Sonnet 5 | $0.00007 | $0.00333 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
peak-calling-output-interpretation 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
peak-calling-output-interpretation
Summary
Interpret and validate the BED-format output from sparse peak-calling tools (e.g. SEACR) applied to CUT&RUN or chromatin profiling data, understanding the semantic meaning of each output field and assessing peak quality based on signal composition and genomic span.
When to use
You have run a peak-calling algorithm on sparse CUT&RUN bedGraph data and received a BED-format output file; you need to understand what each column represents, verify that the peaks meet biological and statistical criteria (total signal, max signal, span), and decide whether to accept, filter, or re-threshold the peak set.
When NOT to use
- Input data is already a curated feature table or a manually annotated region set; peak calling and output interpretation are only needed when starting from raw or sparse bedGraph density.
- Your analysis goal does not require peak-level interpretation; e.g., if you only need summary statistics (total number of peaks, genome coverage), a direct query of the BED file suffices.
- Output is from a peak-calling tool with a different schema (e.g., narrowPeak with p-values and q-values); SEACR output lacks statistical significance estimates and uses signal-based metrics instead.
Inputs
- SEACR output BED file (6-column: chr, start, end, total_signal, max_signal, max_signal_region)
- Input bedGraph file (reference, for validation and cross-checking signal values)
Outputs
- Validated or filtered peak set (BED format)
- Quality report or metrics (peak count, signal distribution, pass/fail flags per peak)
How to apply
Open the output BED file (e.g., .stringent.bed or .relaxed.bed) and examine the six columns: chromosome, start, end, total signal, maximum bedgraph signal, and the coordinates of the maximum signal region. For each peak, verify that the total signal and max signal values are above the threshold used during calling (empirical control-based or numeric fractile). Check that the maximum signal region (field 6) falls within the peak coordinates (fields 2–3), indicating proper boundary detection. Cross-reference peaks against the input bedgraph to confirm that signal blocks were correctly merged and that zero-signal regions were properly omitted. Filter or flag peaks composed of very few input bedgraph lines (v1.2+ adds a line-count filter to remove artifacts from sparse composition). Use the choice of 'relaxed' versus 'stringent' mode (determined by the threshold applied: knee vs. peak of the total signal curve) to interpret expected sensitivity and specificity trade-offs.
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 · 98 lines · 34 tokens per session scan A ab7c6791a173
peak-calling-output-interpretation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 1,665 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 2026-09-06.
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…
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…