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Agent skill

Aviv Regev

by K-Dense-AI in K-Dense-AI/mimeographs

Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics).

MITAuto-check passedResearch & Science

Install Aviv Regev

skills CLI
$ npx skills add K-Dense-AI/mimeographs --skill aviv-regev -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/mimeographs aviv-regev --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeographs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mimeographs/aviv-regev .claude/skills/aviv-regev && rm -rf skills-src

Use ~/.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/

Facts

Skill name
aviv-regev
GitHub stars
129
Token cost
~1.4k tokens
SKILL.md length
678 words
Files
72 (incl. references)
Skills in repo
60
Repo updated
First seen
Licence
MIT

At a glance

Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics).

  • Works in 3 steps: Train computational models using… → Use the models to predict the next, most… → Run the experiments and feed the data…
  • The user is dealing with experimental design
  • SKILL.md covers Core principles, How Aviv Regev 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

What it does

Aviv Regev is an agent skill from K-Dense-AI/mimeographs. Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data…

Its SKILL.md is about 1.4k 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 Research & Science, covering Bioinformatics and Experimental design. 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

  • The user is dealing with experimental design
  • High-dimensional data analysis
  • Integrating AI into scientific workflows
  • Scaling biological research

Example prompts

  • “Use the aviv-regev skill to apply the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell…”
  • “/aviv-regev”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Train computational models using experimental or clinical data.
  2. Use the models to predict the next, most informative set of experiments to run.
  3. Run the experiments and feed the data back to iterate at scale, yielding specific predictions and globally improving the model.

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

Aviv Regev loads about 1.4k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 135 tokens; SKILL.md has 678 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~135
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.6k

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). 678 words, ~1,377 tokens.

Download SKILL.mdSave it as .claude/skills/aviv-regev/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.
name
aviv-regev
description
Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data collection.

Thinking like Aviv Regev

Aviv Regev is a pioneer in computational biology and single-cell genomics who views biology fundamentally as a data and computation problem. Her signature thinking shape involves breaking complex, noisy biological systems down to their fundamental base units (cells), and then using massive-scale, standardized data collection combined with AI to map and model those systems.

Reach for this skill whenever you're helping a user design experiments, integrate AI into a scientific workflow, scale a research project, or make sense of high-dimensional, noisy data.

Core principles

  • Computation Before Collection: Integrate statistical frameworks and power analyses into experimental design before data collection, rather than treating computation as a post-experiment afterthought.
  • Standardized Consortium Approach: Build foundational catalogs using unified, shared approaches across labs, because uncoordinated techniques produce disconnected findings riddled with technical noise.
  • Maximize Cell Numbers Over Depth: In complex systems, prioritize analyzing tens of thousands of units shallowly over a few units deeply to accurately capture rare types and diversity.
  • Cells as the Genotype-Phenotype Bridge: Focus on the specific cells where genetic variants manifest, as they are the critical intermediate for understanding disease and functional characterization.
  • Algorithm Dictates Insight: Recognize that applying different mathematical and AI approaches to the exact same dataset will reveal fundamentally different phenomena.

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

How Aviv Regev reasons

Regev reasons by mapping the unknown. She starts by identifying the fundamental unit of the system (e.g., the cell as the "periodic table" of biology) and asks how to sample that space efficiently. She dismisses exhaustive, brute-force measurement as impossible due to combinatorial explosion; instead, she relies on "Pointillist Sampling & Low-Dimensional Inference" to extract comprehensive understanding from under-sampled data.

When adopting new tools, she strictly avoids "retrofitting" them into old workflows. Instead, she asks how to "liberate" the technology by reimagining the process from the ground up. She views massive datasets not just as reference catalogs, but as the essential training ground for foundation models.

For her complete catalog of mental models, see references/mental-models.md.

Applying the frameworks

Lab in a Loop

Use when designing AI-driven discovery processes or automated experimental workflows.

  1. Train computational models using experimental or clinical data.
  2. Use the models to predict the next, most informative set of experiments to run.
  3. Run the experiments and feed the data back to iterate at scale, yielding specific predictions and globally improving the model.
Show full SKILL.md (283 more words)Show less
Computational Modeling of Cellular Circuits

Use when deciphering how complex networks respond to stimuli or perturbations.

  1. Gather dynamic baseline data (e.g., single-cell RNA sequencing).
  2. Subject the system to stimuli.
  3. Create algorithms to decipher the most likely sequence of events.
  4. Test predictions by silencing specific nodes and observing the response.

For the full catalog of frameworks, see references/frameworks.md.

Anti-patterns she pushes against

  • Retrofitting new technologies: Forcing new tools (like AI) into old paradigms limits their potential; reimagine the workflow instead.
  • Computation as an afterthought: Failing to use statistical frameworks for power analysis before an experiment guarantees suboptimal data collection.
  • Over-sequencing single units: Insisting on deep sequencing for every cell wastes resources on duplicate reads; breadth is more valuable than depth in complex tissues.
  • Fragmented mapping: Building foundational datasets through unstandardized, isolated efforts introduces massive technical noise and batch effects.
  • Always asking for advice: Relying too heavily on "common wisdom" can steer you away from unconventional leaps and temper your natural curiosity.

How to use this skill in conversation

When the user is designing an experiment, building a data pipeline, or applying AI to a complex domain, channel Regev's computational lens.

If they are struggling with noise or scale, suggest "Pointillist Sampling" or remind them to "Maximize Cell Numbers Over Depth." If they are trying to plug AI into an existing process, challenge them to "Liberate, don't retrofit" (citing Regev's philosophy). Frame their data collection not just as gathering facts, but as building a "Foundation Model" or a "Google Maps" for their specific domain. Do not pretend to be Aviv Regev; instead, say things like, "Aviv Regev approaches this by..." or "Using Aviv Regev's 'Lab in a Loop' framework, we should..."

© 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/aviv-regev 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_000.e584bd6c.json
  • _workspace/distilled/src_001.e584bd6c.json
  • _workspace/distilled/src_002.e584bd6c.json
  • _workspace/distilled/src_004.e584bd6c.json
  • _workspace/distilled/src_005.e584bd6c.json
  • … and 54 more

Open the folder on GitHubat commit a38f5fc

Compare with similar skills

Aviv Regev 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.

Aviv Regev compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Aviv Regev this skillK-Dense-AI/mimeographs129—~1.4kAutomated safety check: PassMIT
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Bio Splicing QcGPTomics/bioSkills1.2k2 repos~6.2kAutomated safety check: PassMIT
Bio Experimental Design Batch DesignGPTomics/bioSkills1.2k1 repos~4kAutomated safety check: PassMIT
Bio Experimental Design Multiple TestingGPTomics/bioSkills1.2k1 repos~3.5kAutomated safety check: PassMIT
Bio Experimental Design Power AnalysisGPTomics/bioSkills1.2k1 repos~3.7kAutomated safety check: PassMIT

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Categories

Questions about Aviv Regev

What does Aviv Regev do?

Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Aviv Regev is an agent skill from K-Dense-AI/mimeographs. Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics).

When should I use Aviv Regev?

Aviv Regev fits situations like: the user is dealing with experimental design; high-dimensional data analysis; integrating AI into scientific workflows; scaling biological research.

How do I install Aviv Regev in Claude Code?

Run `npx skills add K-Dense-AI/mimeographs --skill aviv-regev -a claude-code`. Or copy the skill folder (mimeographs/aviv-regev in K-Dense-AI/mimeographs) into .claude/skills/aviv-regev in your project. Claude Code loads it when a task matches its description.

How do I install Aviv Regev in Codex?

Run `npx skills add K-Dense-AI/mimeographs --skill aviv-regev -a codex`. Or copy the skill folder (mimeographs/aviv-regev in K-Dense-AI/mimeographs) into .agents/skills/aviv-regev in your project. Codex loads it when a task matches its description.

Can I use Aviv Regev 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 aviv-regev -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aviv-regev, .gemini/skills/aviv-regev, .github/skills/aviv-regev and .opencode/skills/aviv-regev in your project.

What does Aviv Regev need to run?

SKILL.md names no scripts, command-line tools or credentials: Aviv Regev is instructions for the agent only.

Does Aviv Regev 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 Aviv Regev 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 Aviv Regev use?

Aviv Regev 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 Aviv Regev use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 5.2k tokens, read only when the agent opens those files.

What are the alternatives to Aviv Regev?

Skills that share tags, products or a category with Aviv Regev: Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars), Bio Splicing Qc (GPTomics/bioSkills, 1.2k stars), Bio Experimental Design Batch Design (GPTomics/bioSkills, 1.2k stars) and Bio Experimental Design Multiple Testing (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aviv Regev?

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.