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 personamanagmentlayer/pcl --skill biological-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/biological-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/biological-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/biological-expert/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/personamanagmentlayer/pcl/biological-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/biological-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.02491 |
| Opus 5 | $0.00030 | $0.01246 |
| Sonnet 5 | $0.00012 | $0.00498 |
| Haiku 4.5 | $0.00006 | $0.00249 |
Grade A, and why
biological-expert 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.
How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Biological Sciences Expert
Expert guidance for biology, biotechnology, genetics, bioinformatics, and computational biology applications.
Core Concepts
Molecular Biology
- DNA, RNA, and protein structure
- Central dogma (transcription, translation)
- Gene expression and regulation
- Genetic mutations and variations
- CRISPR and gene editing
- Protein folding and structure
Genomics & Bioinformatics
- DNA sequencing (Sanger, NGS, long-read)
- Genome assembly and annotation
- Sequence alignment (BLAST, BLAT)
- Variant calling and analysis
- RNA-seq analysis
- Phylogenetic analysis
Systems Biology
- Metabolic pathways
- Protein-protein interactions
- Gene regulatory networks
- Mathematical modeling
- Pathway analysis
- Network biology
DNA Sequence Analysis
from Bio import SeqIO, Seq
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight
from typing import Dict, List
class DNAAnalyzer:
"""Analyze DNA sequences"""
def __init__(self, sequence: str):
self.sequence = Seq(sequence.upper())
def basic_stats(self) -> Dict:
"""Calculate basic sequence statistics"""
return {
"length": len(self.sequence),
"gc_content": gc_fraction(self.sequence) * 100,
"molecular_weight": molecular_weight(self.sequence, "DNA"),
"nucleotide_counts": self._count_nucleotides()
}
def _count_nucleotides(self) -> Dict[str, int]:
"""Count each nucleotide"""
return {
'A': self.sequence.count('A'),
'T': self.sequence.count('T'),
'G': self.sequence.count('G'),
'C': self.sequence.count('C')
}
def transcribe(self) -> str:
"""Transcribe DNA to RNA"""
return str(self.sequence.transcribe())
def translate(self, table: int = 1) -> str:
"""Translate DNA to protein"""
return str(self.sequence.translate(table=table))
def reverse_complement(self) -> str:
"""Get reverse complement"""
return str(self.sequence.reverse_complement())
def find_orfs(self, min_length: int = 100) -> List[Dict]:
"""Find Open Reading Frames"""
orfs = []
for strand, seq in [(+1, self.sequence), (-1, self.sequence.reverse_complement())]:
for frame in range(3):
trans = seq[frame:].translate(to_stop=False)
for i, aa in enumerate(trans):
if aa == 'M': # Start codon
for j in range(i + 1, len(trans)):
if trans[j] == '*': # Stop codon
orf_len = (j - i) * 3
if orf_len >= min_length:
orfs.append({
"strand": strand,
"frame": frame,
"start": i * 3 + frame,
"end": j * 3 + frame,
"length": orf_len,
"protein": str(trans[i:j])
})
break
return orfs
def find_motif(self, motif: str) -> List[int]:
"""Find motif positions in sequence"""
positions = []
motif = motif.upper()
for i in range(len(self.sequence) - len(motif) + 1):
if str(self.sequence[i:i+len(motif)]) == motif:
positions.append(i)
return positions
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 Changed · +10 lines · +40 tokens per session 1bda479496e5
- 8d ago First seen · 365 lines · 19 tokens per session scan A 501c2cf1ca28
biological-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 2,491 once invoked, about $0.0003 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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