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/gptomics/bioskills/msa-statisticsnpx skills add GPTomics/bioSkills --skill msa-statisticsgit 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/msa-statistics)<a href="https://agentmods.dev/skills/gptomics/bioskills/msa-statistics"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/msa-statistics.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 | $0.00040 | $0.05613 |
| Opus 5 | $0.00020 | $0.02806 |
| Sonnet 5 | $0.00008 | $0.01123 |
| Haiku 4.5 | $0.00004 | $0.00561 |
Grade A, and why
bio-alignment-msa-statistics 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 5d 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-alignment-msa-statistics — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 450 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: BioPython 1.83+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
MSA Statistics
Calculate sequence identity, conservation scores, substitution counts, and other alignment metrics.
Required Import
Goal: Load modules for alignment I/O, substitution scoring, and statistical calculations.
Approach: Import AlignIO for reading alignments, Counter for column analysis, numpy for matrix operations, and math for entropy calculations.
from Bio import AlignIO
from Bio.Align import substitution_matrices
from collections import Counter
import numpy as np
import math
Pairwise Identity
"Calculate percent identity" -> Compute the fraction of identical aligned residues between sequence pairs.
Goal: Measure sequence similarity as percent identity for individual pairs or across all sequences in an alignment.
Approach: Count matching non-gap positions divided by total aligned positions; optionally compute a full N-by-N identity matrix.
Percent Identity Definitions
There are four common denominators, producing up to 11.5% difference on the same alignment. Combined with different alignment algorithms, variation reaches 22%. Always report which method was used.
| Method | Denominator | Code |
|---|---|---|
| PID1 | Aligned positions including internal gaps | sum(a != '-' or b != '-' for a, b in zip(s1, s2)) |
| PID2 | Aligned residue pairs only (no gaps) | sum(a != '-' and b != '-' for a, b in zip(s1, s2)) |
| PID3 | Shorter sequence length (ungapped) | min(len(s1.replace('-', '')), len(s2.replace('-', ''))) |
| PID4 | Mean sequence length (ungapped) | (len(s1.replace('-', '')) + len(s2.replace('-', ''))) / 2 |
What ships with it
9 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.
- examples/capra_singh_jsd.py 2.9 KB runs code
- examples/conservation_profile.py 1.3 KB runs code
- examples/entropy_analysis.py 2.9 KB runs code
- examples/gap_statistics.py 1.7 KB runs code
- examples/identity_matrix.py 1.7 KB runs code
- examples/kimura_protein_distance.py 1.6 KB runs code
- examples/pssm.py 2.1 KB runs code
- examples/substitution_counts.py 1.4 KB runs code
- usage-guide.md 3.2 KB
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.
- 5d ago First seen · 450 lines · 40 tokens per session scan A d44f58ad40ef
bio-alignment-msa-statistics is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 20d ago), licensed MIT. It adds 40 tokens to every session and 5,613 once invoked, about $0.0002 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 2026-08-30.
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