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 thesecondfox/skill --skill bio-sequence-manipulation-sequence-propertiesgit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/bio-sequence-manipulation-sequence-properties)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-sequence-manipulation-sequence-properties"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-sequence-manipulation-sequence-properties.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 5.1 | $0.00042 | $0.03007 |
| Opus 5 | $0.00021 | $0.01503 |
| Sonnet 5 | $0.00008 | $0.00601 |
| Haiku 4.5 | $0.00004 | $0.00301 |
Grade A, and why
bio-sequence-properties 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 3d 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 — 402 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+, samtools 1.19+
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.
Sequence Properties
Calculate physical and chemical properties of biological sequences using Biopython.
"Calculate GC content" → Compute the fraction of G+C bases in a nucleotide sequence.
- Python:
gc_fraction(seq)(BioPython SeqUtils)
"Analyze protein properties" → Compute MW, pI, stability, hydrophobicity from an amino acid sequence.
- Python:
ProteinAnalysis(str_seq)(BioPython ProtParam)
Required Imports
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight, GC123, GC_skew, nt_search, seq1, seq3
from Bio.SeqUtils.ProtParam import ProteinAnalysis
DNA/RNA Properties
GC Content
from Bio.SeqUtils import gc_fraction
seq = Seq('ATGCGATCGATCGATCGATCG')
gc = gc_fraction(seq) # Returns 0.476... (fraction)
gc_percent = gc * 100 # Convert to percentage
Handle ambiguous bases:
gc = gc_fraction(seq, ambiguous='ignore') # Ignore N bases in calculation
gc = gc_fraction(seq, ambiguous='weighted') # Weight by probability
GC at Codon Positions (GC123)
Analyze GC content at each codon position (useful for codon bias analysis):
from Bio.SeqUtils import GC123
seq = Seq('ATGCGATCGATCGATCGATCG')
gc_total, gc_pos1, gc_pos2, gc_pos3 = GC123(seq)
# gc_total: overall GC%
# gc_pos1: GC% at 1st codon position
# gc_pos2: GC% at 2nd codon position
# gc_pos3: GC% at 3rd codon position (wobble)
GC Skew
Calculate (G-C)/(G+C) in sliding windows to identify replication origins:
from Bio.SeqUtils import GC_skew
seq = Seq('ATGCGATCGATCGATCGATCG' * 10)
skew_values = GC_skew(seq, window=100) # Returns list of skew values
What ships with it
1 file 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.
- 3d ago First seen · 402 lines · 42 tokens per session scan A b33656bb658a
bio-sequence-properties is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 42 tokens to every session and 3,007 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 2026-09-03.
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