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 PKU-YuanGroup/OpenAI4S --skill bio-clip-seq-ago-clip-mirna-targetsgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-clip-seq-ago-clip-mirna-targets)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clip-seq-ago-clip-mirna-targets"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-ago-clip-mirna-targets/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/pku-yuangroup/openai4s/bio-clip-seq-ago-clip-mirna-targets"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-ago-clip-mirna-targets.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 | $0.00152 | $0.05473 |
| Opus 5 | $0.00076 | $0.02736 |
| Sonnet 5 | $0.00030 | $0.01095 |
| Haiku 4.5 | $0.00015 | $0.00547 |
Grade A, and why
bio-clip-seq-ago-clip-mirna-targets 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.
This is a copy
97% identical to bio-clip-seq-ago-clip-mirna-targets — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: eCLIP pipeline (Yeo lab), chimeric eCLIP analysis scripts (Yeo lab), HEAP pipeline (Li 2020), Hyb pipeline (Travis 2014), TargetScanHuman 8.0, miRDB 6.0, samtools 1.19+, bedtools 2.31+, pyHyb 0.4+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying.
AGO-CLIP and miRNA Target Identification
"Identify direct miRNA-target interactions experimentally" -> Use Argonaute (AGO1-4) CLIP-seq variants to map miRNA-binding sites on mRNAs, then resolve which miRNA pairs with each site. Three approaches: (a) standard AGO-CLIP recovers AGO-bound sites but cannot say which miRNA; (b) chimeric methods (CLEAR-CLIP, chimeric eCLIP / miR-eCLIP) ligate the miRNA to its target during library prep, producing miRNA-mRNA chimeric reads that unambiguously assign miRNA-target pairs; (c) HEAP uses HaloTag-Ago2 for in vivo profiling. The chimeric methods are the gold standard for direct miRNA-target identification; standard AGO-CLIP must be combined with computational seed-matching (TargetScan, miRDB) to infer miRNA pairing. Resolution: chimeric reads pinpoint single miRNA-target pairs; AGO-only CLIP identifies "AGO-binding sites" of which a subset are miRNA targets.
- CLI (chimeric eCLIP / miR-eCLIP processing): custom pipeline starting from eCLIP-style preprocessing + chimeric-read identification + miRNA-mRNA junction extraction
- CLI (CLEAR-CLIP custom Moore 2015 pipeline): Hyb (Travis 2014) for chimera analysis
- CLI (Hyb pipeline):
hyb run_hyb peaks.bam mature_miRNA.fa human.tab.gzto find miRNA-mRNA chimeras - CLI (HEAP analysis): standard HITS-CLIP processing pipeline + Halo-Ago2 capture details
- Python (seed-pairing analysis on AGO CLIP peaks): scan peaks for canonical 7mer-m8, 7mer-1A, 8mer, 6mer seeds + 3' UTR position + miRNA expression filter
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.
- 9d ago First seen · 320 lines · 152 tokens per session scan A b09362961ed3
bio-clip-seq-ago-clip-mirna-targets is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 152 tokens to every session and 5,473 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-clip-seq-ago-clip-mirna-targets, differing in 12 lines, and is treated as a copy.
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