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-alignment-msa-parsinggit 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-alignment-msa-parsing)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-alignment-msa-parsing"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-alignment-msa-parsing/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-alignment-msa-parsing"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-alignment-msa-parsing.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 | $0.00052 | $0.05279 |
| Opus 5 | $0.00026 | $0.02639 |
| Sonnet 5 | $0.00010 | $0.01056 |
| Haiku 4.5 | $0.00005 | $0.00528 |
Grade A, and why
bio-alignment-msa-parsing 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 12d 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
86% identical to bio-alignment-msa-parsing — 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 — 461 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 Parsing and Analysis
Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
Required Import
Goal: Load modules for parsing, analyzing, and manipulating multiple sequence alignments.
Approach: Import AlignIO for reading, Counter for column analysis, and alignment classes for constructing modified alignments.
from Bio import AlignIO
from Bio.Align import MultipleSeqAlignment
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq
from collections import Counter
import numpy as np
import pandas as pd
Optional for streaming and Easel-based weighting:
import pyhmmer
Loading Alignments
Goal: Read an MSA file and inspect its dimensions.
Approach: Use AlignIO.read() specifying the file and format.
from Bio import AlignIO
alignment = AlignIO.read('alignment.fasta', 'fasta')
print(f'{len(alignment)} sequences, {alignment.get_alignment_length()} columns')
Extracting Sequence Information
Get All Sequence IDs
seq_ids = [record.id for record in alignment]
Get Sequences as Strings
sequences = [str(record.seq) for record in alignment]
Get Sequence by ID
def get_sequence_by_id(alignment, seq_id):
for record in alignment:
if record.id == seq_id:
return record
return None
target = get_sequence_by_id(alignment, 'species_A')
Access Descriptions and Annotations
for record in alignment:
print(f'ID: {record.id}')
print(f'Description: {record.description}')
print(f'Annotations: {record.annotations}')
What ships with it
10 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.
- references/usage-guide.md 3.7 KB
- scripts/a2m_a3m_io.py 1.0 KB runs code
- scripts/analyze_alignment.py 738 B runs code
- scripts/clean_alignment.py 2.1 KB runs code
- scripts/consensus_sequence.py 1.5 KB runs code
- scripts/find_conserved.py 1.1 KB runs code
- scripts/gap_analysis.py 861 B runs code
- scripts/henikoff_weights.py 1.7 KB runs code
- scripts/mi_apc.py 2.7 KB runs code
- scripts/neff.py 1.6 KB runs code
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.
- 12d ago First seen · 461 lines · 52 tokens per session scan A c22de96eab32
bio-alignment-msa-parsing is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 5,279 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to bio-alignment-msa-parsing, differing in 12 lines, and is treated as a copy.
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