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 GPTomics/bioSkills --skill clip-motif-analysisgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/clip-motif-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/clip-motif-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/clip-motif-analysis/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/gptomics/bioskills/clip-motif-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/clip-motif-analysis.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 | $0.00138 | $0.05796 |
| Opus 5 | $0.00069 | $0.02898 |
| Sonnet 5 | $0.00028 | $0.01159 |
| Haiku 4.5 | $0.00014 | $0.00580 |
Grade A, and why
bio-clip-seq-clip-motif-analysis 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-clip-seq-clip-motif-analysis — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: HOMER 4.11+, MEME Suite 5.5+ (STREME, MEME-ChIP, FIMO), bedtools 2.31+, kpLogo 1.1+, mCross v1+, PEKA v1+, RBPamp 0.9+, ggseqlogo 0.1+, biopython 1.83+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
<tool> --versionthen<tool> --helpto confirm flags - Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws unexpected errors, introspect the installed tool and adapt the example to match the actual API rather than retrying.
CLIP-seq Motif Analysis
"Find enriched RNA motifs at my RBP binding sites" -> Discover the in vivo sequence preference of an RNA-binding protein from CLIP-seq peaks or single-nucleotide crosslink sites. The fundamental confound is the uracil bias of UV254 crosslinking: U is the most-crosslinked base, so naive motif logos centered on CL positions are U-enriched even for non-U-binding RBPs. Modern tools (mCross, PEKA) register motifs relative to the CL position and correct for this bias; legacy tools (HOMER, MEME) need careful background selection.
- CLI (de novo, peak-based, HOMER RNA mode):
findMotifs.pl peaks.fa fasta motif_out -rna -len 5,6,7,8 -p 4 - CLI (de novo, peak-based, MEME-ChIP / STREME):
streme --rna --oc streme_out -p peaks.fa -n background.fa --minw 5 --maxw 10 - CLI (positional, single-nt CL-registered, mCross):
mCross -i crosslinks.bed -g genome.fa -k 7 -o mcross_out(jointly models motif + CL position) - CLI (positional k-mer, no input control needed, PEKA):
peka --peak_file_name peaks.bed --crosslinks_file_name crosslinks.bed --genome_file_name genome.fa --regions_file_name regions.bed --kmer_length 5 --percentile 30 --outpath peka_out(the short flags-i/-x/-g/-r/-k/-pshown in earlier docs are not all stable; use the long forms or checkpeka --help) - CLI (affinity-weighted, RBPamp):
rbpamp run -i peaks.fa -k 7 -o rbpamp_out(joint affinity + motif model) - CLI (positional logo, kpLogo):
kpLogo crosslinks_with_kmer_scores.txt -o kplogo_out(position-specific significance)
What ships with it
2 files 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.
- 7d ago First seen · 289 lines · 138 tokens per session scan A cc1ccb88baa1
bio-clip-seq-clip-motif-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 138 tokens to every session and 5,796 once invoked, about $0.0007 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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