Translation Diff Export
Devolutions/UniGetUI
Compares UniGetUI JSON locale files against English, identifies untranslated or source-changed keys, and generates patch, reference, and handoff files for a target language.
Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM.
Install Virginia M Y Lee
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-lee --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .claude/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .claude/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-leeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-lee --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .agents/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .agents/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .agents/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-lee --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .cursor/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .cursor/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/K-Dense-AI/mimeographs.git --path mimeographs/virginia-m-y-lee--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-lee --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .gemini/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .gemini/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-leeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .github/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .github/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .github/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install K-Dense-AI/mimeographs virginia-m-y-lee --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/mimeographs/virginia-m-y-lee .opencode/skills/virginia-m-y-lee && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "virginia-m-y-lee" agent skill from https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/virginia-m-y-lee into .opencode/skills/virginia-m-y-lee/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "virginia-m-y-lee", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
virginia-m-y-lee- GitHub stars
- 129
- Token cost
- ~1.6k tokens
- SKILL.md length
- 775 words
- Files
- 72 (incl. references)
- Skills in repo
- 60
- Repo updated
- First seen
- Licence
- MIT
At a glance
Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM.
- This skill when discussing Alzheimers
- SKILL.md covers Core principles, How Virginia M.-Y. Lee reasons, Applying the frameworks and Anti-patterns she pushes against, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Protein aggregation
What it does
Virginia M Y Lee is an agent skill from K-Dense-AI/mimeographs. Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Use this to channel the thinking of Virginia M.-Y. Lee, neuroscientist at the University of Pennsylvania known for her pioneering work on neurodegeneration. Trigger this skill when discussing Alzheimer's, Parkinson's, ALS, protein aggregation, cell-to-cell transmission of pathology, brain banking, or multidisciplinary scientific collaboration. It is highly…
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 74 other files, including reference files (for example `AGENTS.md`, `_workspace/agents_output.e584bd6c.json` and `_workspace/clustered_corpus.e584bd6c.json`).
It sits in Writing & Content, covering Translation. The repository describes itself as: Ready-to-use agent skills that clone the thinking of founders, philosophers, and scientists into your agent. Generated with K-Dense-AI/mimeo. The licence is MIT.
When your agent uses it
- This skill when discussing Alzheimers
- Protein aggregation
- Cell-to-cell transmission of pathology
- Multidisciplinary scientific collaboration
Example prompts
- “s, Parkinson”
- “/virginia-m-y-lee”
What it can do on your machine
Read from SKILL.md and the folder at commit a38f5fc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Virginia M Y Lee loads about 1.6k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 175 tokens; SKILL.md has 775 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from K-Dense-AI/mimeographs at commit a38f5fc, republished under its MIT licence (© K-Dense-AI). 775 words, ~1,610 tokens.
Download SKILL.mdSave it as
.claude/skills/virginia-m-y-lee/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.- name
- virginia-m-y-lee
- description
- Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Use this to channel the thinking of Virginia M.-Y. Lee, neuroscientist at the University of Pennsylvania known for her pioneering work on neurodegeneration. Trigger this skill when discussing Alzheimer's, Parkinson's, ALS, protein aggregation, cell-to-cell transmission of pathology, brain banking, or multidisciplinary scientific collaboration. It is highly relevant when users need critiques on biological models, advice on sustaining a long scientific career, or frameworks for translating clinical pathology into basic science.
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.
She relies heavily on the Genetic Proof of Causality to settle debates, looking for human genetic mutations that induce pathology in models as definitive proof. She views disease progression through the lens of Cell-to-Cell Transmission, treating misfolded proteins almost like infectious agents spreading through anatomical pathways. Underpinning all her experimental design is a strict adherence to Garbage In, Garbage Out—if the initial tissue preservation or biochemical conditions are flawed, the conclusions are useless. For more on these, see references/mental-models.md.
Applying the frameworks
The Cellular Pathway of Propagation
Use when modeling how neurodegenerative pathology spreads through a biological system. Trace the misfolded protein through five stages: Binding to the membrane, Internalization into lysosomes, Escape into the cytosol, Seeding of endogenous monomers, and Maturation into mature inclusions.
Show full SKILL.md (324 more words)Show less
In Vitro Amplification of Patient Aggregates
Use when developing cellular models that need to accurately reflect human disease. Instead of using purely recombinant proteins, purify pathological aggregates from postmortem human brains, use a small percentage to seed recombinant monomers, and amplify them in vitro to preserve the unique somatic phenotype.
For the full catalog of her experimental and therapeutic frameworks, see references/frameworks.md.
Anti-patterns she pushes against
- Relying Solely on Recombinant Proteins: Assuming test-tube generated aggregates have the same structural conformation and biological activity as those isolated from a patient's brain.
- Massive Overexpression Animal Models: Relying on transgenic mice that massively overexpress disease proteins, which fails to accurately represent the human condition.
- Permanently Leaving Science for Caregiving: Women giving up their careers entirely for family; the intensive caregiving period is short compared to a long lifespan, and returning to work provides decades of mental stimulation.
- Conflating Mislocalization with Nuclear Clearance: Assuming that because a protein forms a cytoplasmic inclusion, it is automatically depleted from the nucleus.
- Focusing Solely on the End Discovery: Relying on major breakthroughs for motivation, which guarantees frequent disappointment and burnout.
How to use this skill in conversation
When a user is designing a study or evaluating a disease model, prompt them to verify how closely their inputs match human pathology. Surface the "Garbage In, Garbage Out" heuristic and ask if their baseline controls are adequate. If they are relying on purely synthetic or overexpressed models, warn them about the anti-pattern of ignoring conformational strains and suggest the "In Vitro Amplification" framework.
When advising a scientist facing burnout, imposter syndrome, or work-life balance struggles, channel Lee's pragmatic resilience. Remind them to "Enjoy the Daily Process" and use the "I Can" mantra to build a self-efficacy loop. Emphasize that careers are long enough to accommodate gaps for family caregiving. Always frame this advice as coming from Lee's philosophy (e.g., "Virginia M.-Y. Lee emphasizes that..."). Do not speak in the first person as Lee.
© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 71 other files (references) in mimeographs/virginia-m-y-lee of K-Dense-AI/mimeographs.
- SKILL.md
- AGENTS.md
- _workspace/agents_output.e584bd6c.json
- _workspace/clustered_corpus.e584bd6c.json
- _workspace/discovery/books.json
- _workspace/discovery/essays.json
- _workspace/discovery/frameworks.json
- _workspace/discovery/interviews.json
- _workspace/discovery/letters.json
- _workspace/discovery/papers.json
- _workspace/discovery/podcasts.json
- _workspace/discovery/ranked_sources.e584bd6c.json
- _workspace/discovery/talks.json
- _workspace/distilled/src_002.e584bd6c.json
- _workspace/distilled/src_003.e584bd6c.json
- _workspace/distilled/src_005.e584bd6c.json
- _workspace/distilled/src_006.e584bd6c.json
- _workspace/distilled/src_007.e584bd6c.json
- … and 54 more
Open the folder on GitHubat commit a38f5fc
Compare with similar skills
Virginia M Y Lee next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Virginia M Y Lee this skillK-Dense-AI/mimeographs | 129 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Translation Diff ExportDevolutions/UniGetUI | 26k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Sync Translationssymfony/symfony | 31k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Translation Diff ImportDevolutions/UniGetUI | 26k | — | ~750 | Automated safety check: Pass | MIT | |
| Translation Diff TranslateDevolutions/UniGetUI | 26k | — | ~934 | Automated safety check: Pass | MIT | |
| Generate Translationspayloadcms/payload | 45k | — | ~1.1k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Virginia M Y Lee
What does Virginia M Y Lee do?
Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Virginia M Y Lee is an agent skill from K-Dense-AI/mimeographs. Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM.
When should I use Virginia M Y Lee?
Virginia M Y Lee fits situations like: this skill when discussing Alzheimers; protein aggregation; cell-to-cell transmission of pathology; multidisciplinary scientific collaboration.
How do I install Virginia M Y Lee in Claude Code?
Run `npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a claude-code`. Or copy the skill folder (mimeographs/virginia-m-y-lee in K-Dense-AI/mimeographs) into .claude/skills/virginia-m-y-lee in your project. Claude Code loads it when a task matches its description.
How do I install Virginia M Y Lee in Codex?
Run `npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a codex`. Or copy the skill folder (mimeographs/virginia-m-y-lee in K-Dense-AI/mimeographs) into .agents/skills/virginia-m-y-lee in your project. Codex loads it when a task matches its description.
Can I use Virginia M Y Lee in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/mimeographs --skill virginia-m-y-lee -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/virginia-m-y-lee, .gemini/skills/virginia-m-y-lee, .github/skills/virginia-m-y-lee and .opencode/skills/virginia-m-y-lee in your project.
What does Virginia M Y Lee need to run?
SKILL.md names no scripts, command-line tools or credentials: Virginia M Y Lee is instructions for the agent only.
Does Virginia M Y Lee access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Virginia M Y Lee safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Virginia M Y Lee use?
Virginia M Y Lee is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Virginia M Y Lee use?
About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.
What are the alternatives to Virginia M Y Lee?
Skills that share tags, products or a category with Virginia M Y Lee: Translation Diff Export (Devolutions/UniGetUI, 26k stars), Sync Translations (symfony/symfony, 31k stars), Translation Diff Import (Devolutions/UniGetUI, 26k stars) and Translation Diff Translate (Devolutions/UniGetUI, 26k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Virginia M Y Lee?
K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/mimeographs, which has 129 GitHub stars. The repository holds 60 skills in this directory. The repository was last updated on August 18, 2026.
Source: K-Dense-AI/mimeographs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.