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 multiple-alignmentgit 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/multiple-alignment)<a href="https://agentmods.dev/skills/gptomics/bioskills/multiple-alignment"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/multiple-alignment/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/multiple-alignment"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/multiple-alignment.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 | $0.00066 | $0.08524 |
| Opus 5.5 | $0.00026 | $0.03410 |
| Sonnet 5.5 | $0.00013 | $0.01705 |
| Haiku 4.5 | $0.00007 | $0.00852 |
Grade A, and why
bio-alignment-multiple scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- Python: `subprocess.run()` wrapping CLI tools; BioPython `Bio.Align.Applications` was removed in BioPython 1.86 (verify with `pip show biopython`); use `subprocess` directly Copies of this mod
1 near-identical copy found in the catalogue:
- bio-alignment-multiple — 100% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
mafft --version,muscle -version,clustalo --version - Python:
pip show biopythonthenhelp(module.function)to check signatures
If code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying.
Multiple Sequence Alignment
"Align multiple sequences" -> Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods.
- CLI:
mafft(most versatile),muscle(highest accuracy),clustalo(scales well),t_coffee(consistency-based) - Python:
subprocess.run()wrapping CLI tools; BioPythonBio.Align.Applicationswas removed in BioPython 1.86 (verify withpip show biopython); usesubprocessdirectly
MSA Algorithm Taxonomy
When a tool is failing on a dataset, switch to a tool from a different algorithmic family rather than tuning flags. The six families and their characteristic failure modes:
| Family | Representative tools | Best at | Fails when |
|---|---|---|---|
| Progressive | ClustalW, MAFFT FFT-NS-2 | Fast, large datasets, similar lengths | Early-stage gap errors propagate; no recovery |
| Iterative refinement | MAFFT L-INS-i, MUSCLE3, PRRN | Recovers from progressive errors at <2000 seqs | Slow on >2000; still guide-tree dependent |
| Consistency-based | T-Coffee, ProbCons | Highest accuracy <100 seqs; integrates evidence | O(N^2 to N^4) scaling; heavy compute |
| HMM-based | HMMER hmmalign, ClustalOmega (HHalign), UPP, WITCH | Adding sequences to a curated profile; fragmentary input | Needs an existing high-quality profile or backbone |
| Divide-and-conquer | PASTA, MAGUS, MUSCLE5 super5 | Heterogeneous large datasets (>10k seqs) | Sub-alignment merges can introduce artefacts |
| Structure or pLM-informed | Foldmason, PROMALS3D, vcMSA, 3D-Coffee | Dark proteome, <15% identity, dataset has structures | Requires structures or a working pLM |
What ships with it
3 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.
- 1mo ago First seen · 477 lines · 66 tokens per session scan A 9990410daea0
bio-alignment-multiple is a skill published in the GitHub repository GPTomics/bioSkills (1,223 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 8,524 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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