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aviv-regev

aviv-regev is a skill for Claude Code, Codex from K-Dense-AI/mimeographs. It costs 100 tokens per session (1,060 once invoked), scanned A, original, MIT.

A set of reasoning principles based on Aviv Regev's work in computational biology and single-cell genomics, the study of gene activity in individual cells.

In plain words
What is it for?
Use it when planning experiments, analyzing single-cell or other large biological datasets, combining AI with research, or scaling work across teams and laboratories.
Why use it?
It helps turn noisy, high-dimensional biological measurements into organized evidence by considering analysis and standardization while experiments are being designed.

Skill for Claude CodeCodex

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

not rated 123repo +3 24d ago A scan SkillSpector: pass 100 tokens original MIT

Good fit Use it when planning experiments, analyzing single-cell or other large biological datasets, combining AI with research, or scaling work across teams and laboratories.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/mimeographs/aviv-regev
View source ↗ K-Dense-AI/mimeographs k-dense.ai
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 aviv-regev
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 aviv-regev

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/aviv-regev"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/aviv-regev.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,060 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 $0.00100 $0.01060
Opus 5 $0.00050 $0.00530
Sonnet 5 $0.00020 $0.00212
Haiku 4.5 $0.00010 $0.00106

Measured 12d ago against content hash 26c08d89090d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

aviv-regev 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.

mimeographs/aviv-regev/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 60 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. 12d ago First seen · 60 lines · 100 tokens per session scan A 26c08d89090d

Subscribe to this mod's changes

aviv-regev is a skill published in the GitHub repository K-Dense-AI/mimeographs (123 stars, last pushed 24d ago), licensed MIT. It adds 100 tokens to every session and 1,060 once invoked, about $0.0005 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.

Data and AIScientific
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