bio-epidemiological-genomics-transmission-inference

bio-epidemiological-genomics-transmission-inference is a skill for Claude Code, Codex from thesecondfox/skill. It costs 56 tokens per session (1,826 once invoked), scanned A, original, MIT.

A workflow for estimating who infected whom during an outbreak from pathogen genomes and case information. It builds a transmission network that can include likely transmission pairs, unsampled cases, and people who infected many others.

In plain words
What is it for?
Use it to reconstruct transmission chains, examine likely infection pairs, identify possible superspreaders, and account for cases that were not sampled.
Why use it?
Genetic relatedness alone does not directly show the direction of infection, while case records may be incomplete. Combining both sources helps estimate the possible spread of an outbreak.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to reconstruct transmission chains, examine likely infection pairs, identify possible superspreaders, and account for cases that were not sampled.

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Install with agentmods
npx agentmods add skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference
Install

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.

Any agent
npx skills add thesecondfox/skill --skill bio-epidemiological-genomics-transmission-inference
Clone the repo
git clone --depth 1 https://github.com/thesecondfox/skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bio-epidemiological-genomics-transmission-inference

README.md
[![agentmods](https://agentmods.dev/badge/skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference/github.svg)](https://agentmods.dev/skills/thesecondfox/skill/bio-epidemiological-genomics-transmission-inference)
Your own site
<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.

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Your own site · 80×15
<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>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,826 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash c35ca461b102, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Common_Skills/bio-epidemiological-genomics-transmission-inference/SKILL.md · 238 lines

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> then help(module.function) to check signatures
  • R: packageVersion('<pkg>') then ?function_name to 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}

Read the full file on GitHub · 238 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 238 lines · 56 tokens per session scan A c35ca461b102

Subscribe to this mod's changes

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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