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Agent skill

Eric S Lander

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

A skill your agent uses whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation.

MITAuto-check passedResearch & Science

Install Eric S Lander

skills CLI
$ npx skills add K-Dense-AI/mimeographs --skill eric-s-lander -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/mimeographs eric-s-lander --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/eric-s-lander .claude/skills/eric-s-lander && 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
eric-s-lander
GitHub stars
129
Token cost
~1.6k tokens
SKILL.md length
743 words
Files
72 (incl. references)
Skills in repo
60
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation.

  • You are reasoning about large-scale scientific projects
  • SKILL.md covers Core principles, How Eric S. Lander reasons, Applying the frameworks and Anti-patterns they push against, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Data infrastructure

What it does

Eric S Lander is an agent skill from K-Dense-AI/mimeographs. Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S. Lander (geneticist, Broad Institute, Human Genome Project) provides a framework for hypothesis-free discovery, structuring "big science," and treating biology as an information science. Reach for this when the user is discussing open science, collaborative ecosystems, CRISPR/gene editing ethics, managing massive datasets, or setting realistic…

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 Research & Science, covering Bioinformatics, Reproducible research and 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

  • You are reasoning about large-scale scientific projects
  • Data infrastructure
  • Long-term medical translation
  • Is discussing open science

Example prompts

  • “big science,”
  • “lone genius”
  • “/eric-s-lander”

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

Eric S Lander loads about 1.6k tokens when it runs, and up to ~7.3k if it reads all its reference files. Until then it costs about 179 tokens; SKILL.md has 743 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~179
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.3k

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). 743 words, ~1,572 tokens.

Download SKILL.mdSave it as .claude/skills/eric-s-lander/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.
name
eric-s-lander
description
Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S. Lander (geneticist, Broad Institute, Human Genome Project) provides a framework for hypothesis-free discovery, structuring "big science," and treating biology as an information science. Reach for this when the user is discussing open science, collaborative ecosystems, CRISPR/gene editing ethics, managing massive datasets, or setting realistic timelines for technological breakthroughs. Apply his principles to avoid the "lone genius" myth, prevent overpromising, and build foundational infrastructure that serves the broader community.

Thinking like Eric S. Lander

Eric S. Lander is a geneticist, founding director of the Broad Institute, and a principal leader of the Human Genome Project. His thinking is defined by a commitment to "big science" as public infrastructure, the power of hypothesis-free discovery, and a profound respect for evolutionary history. He views biology fundamentally as an information science, where the genome is a foundational text that requires massive, open collaboration to decode.

Reach for this skill whenever you're helping a user design large-scale collaborative projects, evaluate the ethics and timelines of new biotechnologies (like CRISPR), or build foundational data infrastructure.

Core principles

  • The Power of Hypothesis-Free Discovery: Systematic, unbiased discovery is a necessary complement to hypothesis-driven science; when you don't know the answer, "ask the organism."
  • Open Science and Public Infrastructure: Foundational scientific data must be built as freely available public infrastructure to maximize its utility and accelerate global research.
  • The Decades-Long Arc of Medical Translation: Transforming medicine takes decades; practice realistic optimism and avoid overpromising short-term results.
  • Evolutionary Wisdom: There is rarely a "free lunch" in genetics; if a sequence is highly conserved or a variant is rare, trust evolution's vote on its biological cost or importance.
  • Technologists as Equal Partners: True innovation requires treating technologists as intellectual peers, not transactional service providers.

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

How Eric S. Lander reasons

Lander approaches complex biological and organizational problems by zooming out. He favors the "Aerial View" over looking at a single "Rock Outcropping," preferring to map entire landscapes before drilling down into specific pathways. He dismisses the "Lone Genius Myth," insisting that monumental problems require deconstruction across diverse disciplines and massive collaboration.

When evaluating data, he listens for the Whispering Signal—looking at the distribution of data rather than just strict statistical significance. When evaluating genetic interventions, he relies on the Evolutionary Sanity Check, asking why evolution didn't already make a "beneficial" change.

For a full catalog of his mental models, see references/mental-models.md.

Applying the frameworks

Staged Deliverables for Big Science

When to use: Structuring massive, expensive, and long-term projects to ensure continuous momentum and funding. Break the monolithic goal into a series of intermediate stages. Ensure each stage pays immediate, practical returns to the community. Use the success and utility of the current stage to justify funding and momentum for the next step.

Genomic Information Project Playbook

When to use: Building foundational datasets that require community-wide effort. Lay out clear goals and timelines. Establish international collaboration and build necessary technological infrastructure. Make the resulting information completely, freely, and immediately available. Release the vast majority of the data (e.g., 95-98%) rather than waiting for absolute perfection.

Show full SKILL.md (302 more words)Show less
Evaluating the Necessity of Germline Editing

When to use: Determining if CRISPR germline editing is medically justified for preventing genetic disease. Identify if the disease is dominant or recessive, and if parents are heterozygous or homozygous. Prioritize Preimplantation Genetic Diagnosis (PGD) for heterozygous parents. Only consider germline editing in the exceedingly rare cases where parents are homozygous and 100% of embryos would inherit the disease.

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

Anti-patterns they push against

  • Hypothesis-Limited Science: Rejecting exploratory mapping research simply because it lacks a specific prior hypothesis.
  • Overpromising Timelines: Creating false expectations that cures are "around the corner," which leads to public disillusionment.
  • Privatizing Foundational Data: Hoarding datasets or patenting genes, which restricts the broader scientific community.
  • Obsessing Over 100% Completeness: Waiting for absolute perfection before releasing data, delaying scientific progress.
  • Demanding Trust Through Authority: Telling the public "just trust me, I'm a scientist" instead of earning trust through transparency and humility.
  • The Dry Cleaner Model: Treating core technology facilities as transactional drop-off services rather than collaborative partnerships.

How to use this skill in conversation

When the user is facing a situation involving large-scale scientific organization, data sharing, or evaluating biological technologies, surface the relevant principle or framework by name. For example, if a user is waiting for a dataset to be perfect before publishing, invoke "Staged Deliverables for Big Science" and advise them that "absolute completion shouldn't be the enemy of getting the vast majority of the information out."

If a user is trying to guess a biological mechanism, suggest they use "Hypothesis-Free Discovery" and "ask the organism." Always apply the thinking directly to the user's context and cite where the idea comes from (e.g., "Eric S. Lander calls this the Evolutionary Sanity Check"). Do not pretend to be Lander; channel his structural, collaborative, and evolutionary perspective.

© 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/eric-s-lander 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_004.e584bd6c.json
  • _workspace/distilled/src_005.e584bd6c.json
  • _workspace/distilled/src_006.e584bd6c.json
  • … and 54 more

Open the folder on GitHubat commit a38f5fc

Compare with similar skills

Eric S Lander 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.

Eric S Lander compared with similar skills
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Eric S Lander this skillK-Dense-AI/mimeographs129—~1.6kAutomated safety check: PassMIT
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AI Scientist EvaluatorBioTender-max/awesome-bio-agent-skills200—~2.4kAutomated safety check: PassCustom licence
Latchbio Integrationdavila7/claude-code-templates33k11 repos~2.4kAutomated safety check: PassMIT
Ensembl Databasegoogle-deepmind/science-skills3.2k1 repos~2.2kAutomated safety check: PassApache-2.0
Remote Compute Sshaipoch/open-science5.5k—~5.7kAutomated safety check: PassApache-2.0

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Categories

Questions about Eric S Lander

What does Eric S Lander do?

A skill your agent uses whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S Lander is an agent skill from K-Dense-AI/mimeographs. Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation.

When should I use Eric S Lander?

Eric S Lander fits situations like: you are reasoning about large-scale scientific projects; data infrastructure; long-term medical translation; is discussing open science.

How do I install Eric S Lander in Claude Code?

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

How do I install Eric S Lander in Codex?

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

Can I use Eric S Lander 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 eric-s-lander -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eric-s-lander, .gemini/skills/eric-s-lander, .github/skills/eric-s-lander and .opencode/skills/eric-s-lander in your project.

What does Eric S Lander need to run?

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

Does Eric S Lander 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 Eric S Lander 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 Eric S Lander use?

Eric S Lander 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 Eric S Lander use?

About 1.6k tokens (SKILL.md is roughly 6.3k 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 5.7k tokens, read only when the agent opens those files.

What are the alternatives to Eric S Lander?

Skills that share tags, products or a category with Eric S Lander: LaminDB Biological Data Management (davila7/claude-code-templates, 33k stars), AI Scientist Evaluator (BioTender-max/awesome-bio-agent-skills, 200 stars), Latchbio Integration (davila7/claude-code-templates, 33k stars) and Ensembl Database (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Eric S Lander?

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.