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 agentmods add skills/thesecondfox/skill/bio-epitranscriptomics-m6a-peak-callingnpx skills add thesecondfox/skill --skill bio-epitranscriptomics-m6a-peak-callinggit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/bio-epitranscriptomics-m6a-peak-calling)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-epitranscriptomics-m6a-peak-calling"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-epitranscriptomics-m6a-peak-calling.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.00718 |
| Opus 5 | $0.00025 | $0.00359 |
| Sonnet 5 | $0.00010 | $0.00144 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
bio-epitranscriptomics-m6a-peak-calling 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 yesterday.
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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MACS3 3.0+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
m6A Peak Calling
"Call m6A peaks from my MeRIP-seq data" → Identify m6A-modified RNA regions by comparing immunoprecipitated (IP) and input samples using statistical enrichment testing.
- R:
exomePeak2::exomePeak2()for GC-bias aware peak calling - CLI:
macs3 callpeakas an alternative broad peak caller
exomePeak2 (Recommended)
Goal: Identify m6A-enriched regions by comparing IP and input samples with GC-bias correction and replicate-aware statistical testing.
Approach: Provide IP and input BAM files along with a gene annotation to exomePeak2, which models read counts in sliding windows across the transcriptome and calls significant enrichment peaks.
library(exomePeak2)
# Peak calling with biological replicates
result <- exomePeak2(
bam_ip = c('IP_rep1.bam', 'IP_rep2.bam'),
bam_input = c('Input_rep1.bam', 'Input_rep2.bam'),
gff = 'genes.gtf',
genome = 'hg38',
paired_end = TRUE
)
# Export peaks
exportResults(result, format = 'BED')
MACS3 Alternative
# Call peaks treating input as control
macs3 callpeak \
-t IP_rep1.bam IP_rep2.bam \
-c Input_rep1.bam Input_rep2.bam \
-f BAMPE \
-g hs \
-n m6a_peaks \
--nomodel \
--extsize 150 \
-q 0.05
MeTPeak
library(MeTPeak)
# GTF-aware peak calling
metpeak(
IP_BAM = c('IP_rep1.bam', 'IP_rep2.bam'),
INPUT_BAM = c('Input_rep1.bam', 'Input_rep2.bam'),
GENE_ANNO_GTF = 'genes.gtf',
OUTPUT_DIR = 'metpeak_output'
)
Peak Filtering
# Filter by fold enrichment and q-value
# FC > 2, q < 0.05 typical thresholds
awk '$7 > 2 && $9 < 0.05' peaks.xls > filtered_peaks.bed
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 89 lines · 50 tokens per session scan A 3753afb541b5
bio-epitranscriptomics-m6a-peak-calling is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 718 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 2026-09-03.
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