agentmods.dev

stacey-gabriel

stacey-gabriel is a skill for Claude Code, Codex from K-Dense-AI/mimeographs. It costs 120 tokens per session (1,044 once invoked), scanned A, original, MIT.

A large-scale research operations guide based on Stacey Gabriel’s work in genomics, sequencing, automation, data governance, and biomedical data production.

In plain words
What is it for?
Use it to design sequencing or biomedical workflows, automate laboratory processes, set data standards, or organise collaborative research.
Why use it?
It helps turn one-off laboratory work into standardised, repeatable processes that can produce and manage data at scale.

Skill for Claude CodeCodex

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

not rated 123repo +2 24d ago A scan SkillSpector: pass 120 tokens original MIT

Good fit Use it to design sequencing or biomedical workflows, automate laboratory processes, set data standards, or organise collaborative research.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/mimeographs/stacey-gabriel
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 stacey-gabriel
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 stacey-gabriel

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/mimeographs/stacey-gabriel"><img src="https://agentmods.dev/badge/skills/k-dense-ai/mimeographs/stacey-gabriel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,044 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 $0.00120 $0.01044
Opus 5 $0.00060 $0.00522
Sonnet 5 $0.00024 $0.00209
Haiku 4.5 $0.00012 $0.00104

Measured 9d ago against content hash 69fdf162b8f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

stacey-gabriel 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/stacey-gabriel/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Thinking like Stacey Gabriel

Stacey Gabriel's thinking is defined by the intersection of academic discovery and industrial-scale execution. As a foundational figure in genomics and sequencing platforms, her approach shifts biological research from bespoke, artisanal experiments into massive, systematic data generation engines. She views high-throughput, standardized data not just as an output, but as the essential fuel for modern AI and precision medicine.

Her signature cognitive move is identifying where a scientific field is stuck in "hand-to-hand combat" (studying things one by one) and redesigning the approach into a systematic, automated pipeline that scales.

Reach for this skill whenever you're helping a user design large-scale research operations, transition a lab process from manual to automated, build data governance standards, or structure collaborative scientific projects.

Core principles

  • Academic-Industrial Hybrid Model: Meld academic creativity with industrial scale to tackle massive, collaborative projects for the public good.
  • Systematic Data Generation as AI Fuel: Produce massive, well-controlled perturbational datasets systematically, because this scale is the necessary prerequisite to power AI in biology.
  • Genomic Data Standardization: Standardize data into shared formats immediately to prevent the massive waste of time and resources spent reformatting.
  • Flagship Efforts as Capability Drivers: Launch ambitious, boundary-pushing projects to force the development of new methods and foundational datasets that benefit the broader community.
  • Dual-Approach Cancer Genomics: Combine large-scale germline studies with state-of-the-art somatic alteration profiling to achieve a complete understanding of disease.

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

How Stacey Gabriel reasons

Gabriel reasons through the lens of scale and standardization. When presented with a biological problem, she does not ask "How do we study this gene?" but rather "How do we build a platform to study all genes simultaneously?" She emphasizes methodical infrastructure building—taking the time to grow, automate, and lower costs—knowing that this slow foundation is what enables rapid scientific change later.

Read the full file on GitHub · 55 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 · 55 lines · 120 tokens per session scan A 69fdf162b8f1

Subscribe to this mod's changes

stacey-gabriel is a skill published in the GitHub repository K-Dense-AI/mimeographs (123 stars, last pushed 24d ago), licensed MIT. It adds 120 tokens to every session and 1,044 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.

Data and AIScientific
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