j-brandon-dixon

j-brandon-dixon is a skill for Claude Code, Codex from K-Dense-AI/mimeo. It costs 123 tokens per session (1,806 once invoked), scanned A, original, MIT.

A reasoning guide based on J. Brandon Dixon's work on lymphatic biomechanics, fluid movement, and tissue engineering. It treats lymphatic vessels as active muscle-driven pumps rather than simple passive pipes.

In plain words
What is it for?
Studying lymphatic flow, vessel pumping, fluid transport, lymphatics-on-a-chip, computational models, and medical-device design.
Why use it?
It helps analyze how lymphatic systems compensate for damage, change over time, and affect conditions such as lymphedema.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Studying lymphatic flow, vessel pumping, fluid transport, lymphatics-on-a-chip, computational models, and medical-device design.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/mimeo/j-brandon-dixon
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 K-Dense-AI/mimeo --skill j-brandon-dixon
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/mimeo

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 j-brandon-dixon

README.md
[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/mimeo/j-brandon-dixon/github.svg)](https://agentmods.dev/skills/k-dense-ai/mimeo/j-brandon-dixon)
Your own site
<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/j-brandon-dixon"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/j-brandon-dixon/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.

agentmods 80×15 button for j-brandon-dixon

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/mimeo/j-brandon-dixon"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeo/j-brandon-dixon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,806 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00123 $0.01806
Opus 5 $0.00062 $0.00903
Sonnet 5 $0.00025 $0.00361
Haiku 4.5 $0.00012 $0.00181

Measured 8d ago against content hash deb824c7d0c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

j-brandon-dixon 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 8d 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.

output/j-brandon-dixon/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Thinking like J. Brandon Dixon

J. Brandon Dixon's work operates at the intersection of biomechanics, fluid dynamics, cell biology, and microfluidics. Rather than viewing vascular systems as simple passive plumbing, Dixon frames self-pumping biological networks—specifically lymphatic collecting vessels—as dynamic, autonomous, cardiac-like muscle pumps. Central to his thinking is the realization that long-term pathological outcomes (such as secondary lymphedema following cancer surgery) stem from biomechanical compensation: intact vessels hyper-pump under elevated afterload, masking acute damage while accumulating oxidative stress, smooth muscle remodeling, and eventual pump failure.

To unravel these complex biofluidic systems, Dixon champions multi-scale engineering integration: coupling high-speed functional imaging, lumped-parameter computational modeling, microfluidic lymphatics-on-a-chip, and user-centered device design. He rigorously privileges active functional performance over static structural presence, demanding tools and models that quantify flow rate, occlusion pressure, and pump metrics rather than vessel counts or histology alone.

Reach for this skill whenever you are designing microfluidic devices, evaluating biofluidic or peristaltic transport systems, modeling vascular mechanobiology, framing preclinical animal disease models, or developing targeted biomedical diagnostics and therapeutics.

Core principles

  • Compensatory Hyper-Pumping Masks and Accelerates Pump Failure: Acute surgical or structural loss forces remaining intact vessels to increase contractile frequency and force; this short-term compensation induces long-term oxidative stress, smooth muscle remodeling, and delayed pump breakdown.
  • Integrate Multidisciplinary Engineering with Mechanobiology: Elucidating self-pumping vascular systems requires tightly coupling molecular biology, fluid biomechanics, high-speed dynamic imaging, computer signal processing, and computational modeling.
  • Measure Active Functional Transport, Not Static Architecture: Diagnostic and therapeutic success must be evaluated by dynamic pumping pressure, clearance velocity, and contractile mechanics rather than structural vessel presence or static staining.
  • Target Multiple Pathological Pathways Simultaneously: Chronic secondary diseases involving mechanical pump impairment, tissue fibrosis, and inflammation require combined therapeutics (e.g., pro-lymphangiogenic plus anti-inflammatory agents) rather than single-target magic bullets.
  • Design In Vitro Systems for Collaborative Simplicity: Bioengineering platforms and microfluidic microenvironments must be operationally simple enough for non-engineers to independently adopt and replicate.

Read the full file on GitHub · 85 lines

Files

What ships with it

7 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.

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. 8d ago Changed · +2 lines deb824c7d0c4
  2. 12d ago First seen · 83 lines · 123 tokens per session scan A 9926793b994c

Subscribe to this mod's changes

j-brandon-dixon is a skill published in the GitHub repository K-Dense-AI/mimeo (269 stars, last pushed 9d ago), licensed MIT. It adds 123 tokens to every session and 1,806 once invoked, about $0.0006 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 2026-08-30.

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