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Agent skill

Stacey Gabriel

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

Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute).

MITAuto-check passedResearch & Science

Install Stacey Gabriel

skills CLI
$ npx skills add K-Dense-AI/mimeographs --skill stacey-gabriel -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/mimeographs stacey-gabriel --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/stacey-gabriel .claude/skills/stacey-gabriel && 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
stacey-gabriel
GitHub stars
129
Token cost
~1.4k tokens
SKILL.md length
629 words
Files
72 (incl. references)
Skills in repo
60
Repo updated
First seen
Licence
MIT

At a glance

Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute).

  • Works in 4 steps: Identify the bottleneck: If they are… → Advocate for standardization: If they… → Apply the hybrid model: If they are… → …
  • The user is designing large-scale biomedical research
  • SKILL.md covers Core principles, How Stacey Gabriel 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

Stacey Gabriel is an agent skill from K-Dense-AI/mimeographs. Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute). Use this skill whenever the user is designing large-scale biomedical research, operationalizing lab workflows, standardizing data governance, or transitioning from bespoke academic experiments to industrial-scale science. It is highly relevant for queries involving genomics, precision medicine, multiomics, cellular perturbation pipelines, or building massive datasets to fuel AI…

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 Data governance. 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 designing large-scale biomedical research
  • Operationalizing lab workflows
  • Standardizing data governance
  • Transitioning from bespoke academic experiments to industrial-scale science

Example prompts

  • “s focus from”
  • “Use the stacey-gabriel skill to apply the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the…”
  • “/stacey-gabriel”

Workflow steps

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

  1. Identify the bottleneck: If they are doing things one-by-one, surface the "Hand-to-hand combat vs. Systematic scale" mental model.
  2. Advocate for standardization: If they are struggling with data silos, invoke Gabriel's principle on Genomic Data Standardization and…
  3. Apply the hybrid model: If they are structuring a team, suggest the Academic-Industrial Hybrid Model to balance discovery with execution.
  4. Channel the thinking: Use phrases like "Stacey Gabriel frames this as..." or "Applying Stacey Gabriel's approach to scale..." Do not…

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

Stacey Gabriel loads about 1.4k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 170 tokens; SKILL.md has 629 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~170
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
~4.4k

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). 629 words, ~1,385 tokens.

Download SKILL.mdSave it as .claude/skills/stacey-gabriel/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.
name
stacey-gabriel
description
Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute). Use this skill whenever the user is designing large-scale biomedical research, operationalizing lab workflows, standardizing data governance, or transitioning from bespoke academic experiments to industrial-scale science. It is highly relevant for queries involving genomics, precision medicine, multiomics, cellular perturbation pipelines, or building massive datasets to fuel AI. Trigger this skill to shift the user's focus from 'hand-to-hand combat' one-off studies to systematic, standardized, and scalable data generation.

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.

She actively dismisses one-by-one scientific approaches, utilizing the Hand-to-hand combat vs. Systematic scale mental model to evaluate research efficiency. She also applies the heuristic that Methodical Scaling Precedes Rapid Change. For a deeper dive into these models, see references/mental-models.md.

Applying the frameworks

Achieving Scientific Revolutions

When to use: Designing a massive, collaborative scientific initiative that cannot be done in a traditional siloed lab. Steps: Define the vision (what and why), launch flagship efforts to force capability creation, and apply a hybrid execution model (academic creativity + industrial project management).

Show full SKILL.md (237 more words)Show less
High-Scale Cellular Perturbation Platform

When to use: Transitioning from knowing a list of genetic variants to understanding their functional impact at scale. Steps: Standardize cellular inputs, apply systematic perturbations (e.g., CRISPR), perform high-throughput readouts, and feed the harmonized downstream data into AI models.

For the full catalog and steps, see references/frameworks.md.

Anti-patterns she pushes against

  • Siloed Research Models: Relying solely on exploratory academic models or purely financial industrial models for foundational public-good research.
  • One-by-One Variant Studies: Engaging in "hand-to-hand combat" to study single genetic variants instead of building systematic, genome-wide perturbation platforms.
  • Bespoke Data Formatting: Allowing individual investigators to use custom data formats, which requires constant reprocessing and destroys collaborative efficiency.

How to use this skill in conversation

When the user is facing a challenge related to scaling research, managing massive datasets, or structuring a scientific collaboration:

  1. Identify the bottleneck: If they are doing things one-by-one, surface the "Hand-to-hand combat vs. Systematic scale" mental model.
  2. Advocate for standardization: If they are struggling with data silos, invoke Gabriel's principle on Genomic Data Standardization and explain how bespoke formats waste resources.
  3. Apply the hybrid model: If they are structuring a team, suggest the Academic-Industrial Hybrid Model to balance discovery with execution.
  4. Channel the thinking: Use phrases like "Stacey Gabriel frames this as..." or "Applying Stacey Gabriel's approach to scale..." Do not pretend to be her; instead, apply her operational blueprints to the user's specific context.

© 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/stacey-gabriel 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_003.e584bd6c.json
  • _workspace/distilled/src_004.e584bd6c.json
  • … and 54 more

Open the folder on GitHubat commit a38f5fc

Compare with similar skills

Stacey Gabriel 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.

Stacey Gabriel compared with similar skills
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Stacey Gabriel this skillK-Dense-AI/mimeographs129—~1.4kAutomated safety check: PassMIT
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Lamindbaipoch/medical-research-skills1.9k—~4.8kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0

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Categories

Questions about Stacey Gabriel

What does Stacey Gabriel do?

Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute). Stacey Gabriel is an agent skill from K-Dense-AI/mimeographs. Applies the operational and scientific frameworks of Stacey Gabriel (genomicist and sequencing platform leader at the Broad Institute).

When should I use Stacey Gabriel?

Stacey Gabriel fits situations like: the user is designing large-scale biomedical research; operationalizing lab workflows; standardizing data governance; transitioning from bespoke academic experiments to industrial-scale science.

How do I install Stacey Gabriel in Claude Code?

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

How do I install Stacey Gabriel in Codex?

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

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

What does Stacey Gabriel need to run?

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

Does Stacey Gabriel 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 Stacey Gabriel 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 Stacey Gabriel use?

Stacey Gabriel 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 Stacey Gabriel 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 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Stacey Gabriel?

Skills that share tags, products or a category with Stacey Gabriel: LaminDB Biological Data Management (davila7/claude-code-templates, 33k stars), Lamindb (aipoch/medical-research-skills, 1.9k stars), Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars) and 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stacey Gabriel?

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.