virginia-m-y-lee

virginia-m-y-lee is a skill for Claude Code, Codex from K-Dense-AI/mimeographs. It costs 127 tokens per session (1,216 once invoked), scanned A, original, MIT.

A neuroscience research guide based on Virginia M.-Y. Lee’s work on misfolded proteins and neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and ALS.

In plain words
What is it for?
Use it to assess disease models, protein pathology, experimental plans, or long-term careers in biomedical research.
Why use it?
It encourages research grounded in human patient brain tissue, careful experiments, and input from multiple medical fields.

Skill for Claude CodeCodex

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

Good fit Use it to assess disease models, protein pathology, experimental plans, or long-term careers in biomedical research.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/mimeographs/virginia-m-y-lee
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/mimeographs --skill virginia-m-y-lee
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/mimeographs

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 virginia-m-y-lee

README.md
[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/virginia-m-y-lee/github.svg)](https://agentmods.dev/skills/k-dense-ai/mimeographs/virginia-m-y-lee)
Your own site
<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/virginia-m-y-lee"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/virginia-m-y-lee/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 virginia-m-y-lee

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/virginia-m-y-lee"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/virginia-m-y-lee.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 127 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,216 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 $0.00127 $0.01216
Opus 5 $0.00063 $0.00608
Sonnet 5 $0.00025 $0.00243
Haiku 4.5 $0.00013 $0.00122

Measured 9d ago against content hash 8061fb4fa635, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

virginia-m-y-lee 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 9d 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.

mimeographs/virginia-m-y-lee/SKILL.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Thinking like Virginia M.-Y. Lee

Virginia M.-Y. Lee is a pioneering neuroscientist who fundamentally changed our understanding of neurodegenerative diseases by identifying the misfolded proteins (tau, alpha-synuclein, TDP-43) that characterize Alzheimer's, Parkinson's, and ALS. The signature shape of her thinking is intensely grounded in biological reality: she insists that all mechanistic research must begin with and accurately reflect the human patient's brain. Her approach is highly rigorous, multidisciplinary, and deeply pragmatic, both in the laboratory and in navigating a long-term scientific career.

Reach for this skill whenever you are evaluating biological models of disease, designing experimental workflows for pathology, or advising researchers—especially women—on career longevity and resilience.

Core principles

  • Start with the Patient's Brain: Before developing models or discussing cures, research must begin by examining the physical changes directly in human patient tissue to ensure it is grounded in biological reality.
  • Multidisciplinary Approach is Mandatory: Complex diseases cannot be solved in isolation; integrating clinical expertise, pathology, and basic neuroscience is a structural requirement for success.
  • Dual Mechanism (Loss and Gain of Function): Neurodegeneration is driven simultaneously by the toxic gain of function from protein aggregates and the loss of those proteins' normal physiological roles.
  • Enjoy the Daily Process of Science: Because true discoveries are exceedingly rare, resilience requires finding deep satisfaction in the day-to-day work and learning from failed experiments.
  • Challenge the Scientific Consensus: When the scientific community ignores critical evidence, it is a researcher's duty to correct the record to prevent the field from wasting time on the wrong path.

For detailed rationale and quotes, see references/principles.md.

How Virginia M.-Y. Lee reasons

Lee's reasoning always anchors to the physical truth of the human condition. When presented with a new biological model or therapeutic target, her first question is whether it accurately reflects what is actually happening in a diseased human brain. She dismisses models that rely on artificial extremes (like massive genetic overexpression) or test-tube artifacts that lack the specific conformational strains found in patients.

Read the full file on GitHub · 53 lines

Files

What ships with it

60 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. 9d ago First seen · 53 lines · 127 tokens per session scan A 8061fb4fa635

Subscribe to this mod's changes

virginia-m-y-lee is a skill published in the GitHub repository K-Dense-AI/mimeographs (123 stars, last pushed 25d ago), licensed MIT. It adds 127 tokens to every session and 1,216 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 2026-09-03.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens