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 stamp-antibody-freegit 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/stamp-antibody-free)<a href="https://agentmods.dev/skills/gptomics/bioskills/stamp-antibody-free"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/stamp-antibody-free/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/stamp-antibody-free"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/stamp-antibody-free.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 | $0.00126 | $0.05049 |
| Opus 5 | $0.00063 | $0.02524 |
| Sonnet 5 | $0.00025 | $0.01010 |
| Haiku 4.5 | $0.00013 | $0.00505 |
Grade A, and why
bio-clip-seq-stamp-antibody-free 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-stamp-antibody-free — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: STAMP / scSTAMP (Brannan 2021 Yeo lab github), Bullseye 1.0+, SAILOR 1.1+, samtools 1.19+, REDItools2 1.3+, JACUSA2 2.0+, scanpy 1.10+, anndata 0.10+, pysam 0.22+.
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 CLI rather than retrying.
STAMP / Antibody-Free RBP Profiling
"Profile RBP-RNA targets without UV crosslinking or immunoprecipitation" -> Express a fusion of the RBP-of-interest with a deaminase (APOBEC1 for STAMP, ADAR for TRIBE) in cells; the deaminase edits RNA nucleotides adjacent to where the RBP binds, producing a C-to-U (STAMP) or A-to-I (TRIBE, read as A-to-G) editing signature in standard RNA-seq. The targets are recovered computationally from the editing pattern. Three properties make this approach valuable: (a) no UV crosslinking required (works in tissue/in vivo); (b) no IP step (no antibody needed - the RBP itself targets the deaminase); (c) compatible with single-cell readout because the editing signal exists in standard scRNA-seq (scSTAMP, scTRIBE). Trade-off: editing is offset from the binding site (typically 0-50 nt away); resolution is approximate; off-target editing from deaminase alone must be subtracted.
- CLI (STAMP, bulk): standard RNA-seq pipeline + Bullseye or SAILOR for C-to-U edit detection vs APOBEC1-only control
- CLI (TRIBE, bulk): standard RNA-seq + REDItools2 or JACUSA2 for A-to-I edit detection vs ADAR-only control
- CLI (DART-seq for m6A): same as STAMP, with APOBEC1-YTH fusion (YTH is the m6A reader)
- Python (scSTAMP single-cell): 10x Genomics or Smart-seq2 pipeline + custom editing-rate quantification per cell + per-cell binding-target inference
- CLI (general edit-site detection):
JACUSA2 call-2 -r ref.fa -p 8 -F 1024 -A,B treated.bam,control.bam -t pileup.tsvthen filter for C-to-U or A-to-I
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 · 279 lines · 126 tokens per session scan A 48e32947e0df
bio-clip-seq-stamp-antibody-free is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 126 tokens to every session and 5,049 once invoked, about $0.0006 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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