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-epidemiological-genomics-transmission-inferencegit 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-epidemiological-genomics-transmission-inference)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference/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/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference.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 | $0.00056 | $0.01826 |
| Opus 5 | $0.00028 | $0.00913 |
| Sonnet 5 | $0.00011 | $0.00365 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
bio-epidemiological-genomics-transmission-inference 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.
How it starts
The opening of the file, as written. The whole thing — 238 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+, TreeTime 0.11+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Transmission Inference
"Infer who infected whom in my outbreak" → Reconstruct transmission networks from genomic and epidemiological data to identify transmission pairs, superspreaders, and unsampled cases.
- R:
TransPhylo::inferTTree()for Bayesian transmission tree inference
TransPhylo in R
library(TransPhylo)
library(ape)
# Load dated phylogeny (from BEAST/TreeTime)
tree <- read.nexus('dated_tree.nexus')
# Convert to TransPhylo format
ptree <- ptreeFromPhylo(tree, dateLastSample = 2020.5)
# Estimate transmission tree
# Uses MCMC to sample from posterior distribution
res <- inferTTree(
ptree,
mcmcIterations = 100000,
startNeg = 0.1, # Initial within-host effective population
startOff.r = 2, # Initial R0 estimate
startOff.p = 0.5, # Initial sampling probability
startPi = 0.9, # Initial probability of being sampled
dateT = 2020.6 # End of outbreak observation
)
# Extract consensus transmission tree
ttree <- extractTTree(res)
# Get transmission pairs
pairs <- ttree$ttree[, c('infector', 'infectee', 'time')]
Prepare Data
def prepare_for_transphylo(dated_tree_file, sample_dates, output_prefix):
'''Prepare inputs for TransPhylo analysis
Requirements:
- Time-scaled phylogeny (from TreeTime or BEAST)
- Sample collection dates
- Tips must have matching names
TransPhylo estimates:
- Who infected whom
- Unsampled cases in the transmission chain
- R0 and generation time
'''
from Bio import Phylo
import pandas as pd
tree = Phylo.read(dated_tree_file, 'nexus')
# Verify all tips have dates
dates_df = pd.read_csv(sample_dates, sep='\t')
tip_names = {clade.name for clade in tree.get_terminals()}
dated_names = set(dates_df['name'])
missing = tip_names - dated_names
if missing:
print(f'Warning: {len(missing)} tips without dates: {missing}')
return {'tree': dated_tree_file, 'dates': sample_dates}
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.
- 12d ago First seen · 238 lines · 56 tokens per session scan A c35ca461b102
bio-epidemiological-genomics-transmission-inference is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 1,826 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 2026-08-31.
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