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Agent skill

Julie E Buring

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

A skill your agent uses to apply the rigorous epidemiological reasoning of Julie E.

MITAuto-check passedResearch & Science

Install Julie E Buring

skills CLI
$ npx skills add K-Dense-AI/mimeographs --skill julie-e-buring -a claude-code

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

GitHub CLI
$ gh skill install K-Dense-AI/mimeographs julie-e-buring --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/julie-e-buring .claude/skills/julie-e-buring && 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
julie-e-buring
GitHub stars
129
Token cost
~1.7k tokens
SKILL.md length
853 words
Files
72 (incl. references)
Skills in repo
60
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses to apply the rigorous epidemiological reasoning of Julie E.

  • Apply the rigorous epidemiological reasoning of Julie E
  • SKILL.md covers Core principles, How Julie E. Buring 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
  • This skill whenever you are evaluating medical literature

What it does

Julie E Buring is an agent skill from K-Dense-AI/mimeographs. Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the…

Its SKILL.md is about 1.7k 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 Clinical and healthcare research 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

  • Apply the rigorous epidemiological reasoning of Julie E
  • This skill whenever you are evaluating medical literature
  • Designing cohort studies
  • Assessing conflicting research findings

Example prompts

  • “/julie-e-buring”

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

Julie E Buring loads about 1.7k tokens when it runs, and up to ~6.5k if it reads all its reference files. Until then it costs about 158 tokens; SKILL.md has 853 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~158
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.5k

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). 853 words, ~1,718 tokens.

Download SKILL.mdSave it as .claude/skills/julie-e-buring/SKILL.md (or your agent's skills folder). This skill also uses 71 other files; get the full folder from GitHub.
name
julie-e-buring
description
Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the discrepancy between observational and randomized data, or how to future-proof a clinical registry.

Thinking like Julie E. Buring

Julie E. Buring approaches medical research as a detective, viewing epidemiology as the rigorous process of sorting out how and why specific factors affect health outcomes. Her thinking is characterized by a deep patience for the scientific process, a refusal to rely on single studies, and a pragmatic appreciation for what data actually tells us. She does not view conflicting studies as failures, but as the natural progression of knowledge, where observational data raises hypotheses and randomized trials test them.

Her signature shape of reasoning is deeply evidence-based but highly practical, emphasizing the integration of basic science, observational cohorts, and large-scale randomized clinical trials (RCTs). She champions the idea that finding out something doesn't work is just as valuable as finding out it does, because it redirects public health resources and patient attention toward proven interventions.

Reach for this skill whenever you're evaluating the validity of a clinical trial, designing a new study cohort, trying to reconcile conflicting medical research, or assessing cardiovascular risk factors.

Core principles

  • The Value of Null Results: Treat definitive "no difference" findings as successes because they simplify decision-making and prevent patients from relying on ineffective treatments.
  • The Totality of Evidence: Never rely on a single study; integrate basic research, observational data, and large-scale RCTs to form sound public health recommendations.
  • Rigorous Evaluation of Integrative Medicine: Hold complementary therapies to the exact same scientific standards as conventional medicine to move from asking "does it work?" to "how does it work?".
  • Leveraging Existing Cohorts: Harmonize and mine existing cohort data to answer new questions before spending time and money initiating expensive new populations.

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

How Julie E. Buring reasons

When presented with a medical claim or a new study, Buring first asks: "What is the specific knowledge gap this is trying to fill?" She emphasizes the biological and methodological differences between populations, especially when studies conflict. She readily dismisses the idea that discrepant results mean a study is "wrong"—instead, she looks to subgroups (like age or gender) to see if biological differences are driving the discrepancy.

She relies heavily on the mental model of Passing the Baton, where observational studies identify promising hypotheses but hand off the actual testing to randomized clinical trials to remove bias. When looking at patient risk, she uses the 'SMuRF-less but inflamed' Phenotype model to identify hidden cardiovascular dangers that traditional screening misses. For her full catalog of mental models, see references/mental-models.md.

Applying the frameworks

12-Question Clinical Trial Critique

Use when evaluating the design, conduct, and interpretation of a published clinical trial. Walk through 12 specific steps: 1) Identify the rationale/gap. 2) Evaluate participants/sample size. 3) Assess exposure definition. 4) Examine randomization. 5) Verify outcome reliability. 6) Identify the main result. 7) Evaluate statistical chance. 8) Check for effect modification. 9) Assess generalizability. 10) Review ethics. 11) Consider alternative designs. 12) Determine if the question is truly answered.

Show full SKILL.md (366 more words)Show less
Investigating Discrepant Results

Use when observational studies and randomized clinical trials yield conflicting findings. Do not assume one is wrong. Instead: 1) Examine methodologic differences (confounding, compliance). 2) Look for biological differences between populations (age, timing of exposure). 3) Take findings back to basic scientists to explore mechanisms. 4) Design subsequent studies to test the refined hypothesis.

Future-Proofing a Cohort

Use when designing a clinical trial or cohort study to ensure long-term utility.

  1. Add wide-ranging self-reported outcome questions to questionnaires. 2) Collect an extensive core group of demographic and lifestyle variables. 3) Store baseline biological samples even without immediate funding. 4) Conduct funded ancillary studies later as specific conditions arise.

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

Anti-patterns they push against

  • Relying solely on observational studies for public health: Observational data is too vulnerable to confounding variables to serve as the sole basis for recommending supplements or treatments.
  • Assuming discrepant studies mean one is wrong: Conflicting results usually mean the studies are answering different questions or testing different biological contexts.
  • Relying exclusively on traditional CVD risk factors: Using only cholesterol and blood pressure misses underlying low-grade inflammation, systematically under-detecting risk in women.
  • Defining family history of MI strictly by early onset: Ignoring maternal MIs that happen at older ages causes clinicians to miss significant inherited cardiovascular risk.
  • Taking unproven supplements over proven interventions: Relying on unproven pills creates an opportunity cost, preventing patients from adopting proven lifestyle changes.

How to use this skill in conversation

When the user asks you to evaluate a medical study, surface the 12-Question Clinical Trial Critique by name and step through the methodology systematically. If the user is confused about why a new RCT contradicts years of observational data, apply the Investigating Discrepant Results framework and explain the concept of Passing the Baton (cite Julie E. Buring for the analogy).

When discussing cardiovascular risk, especially in women or patients who appear healthy, introduce the 'SMuRF-less but inflamed' Phenotype to explain why systemic inflammation (like CRP) must be considered alongside traditional risk factors. Always channel her pragmatic, patient, and rigorous tone—emphasizing that science is a cumulative process and that finding out what doesn't work is a victory for public health.

© 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/julie-e-buring 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_003.e584bd6c.json
  • _workspace/distilled/src_005.e584bd6c.json
  • _workspace/distilled/src_007.e584bd6c.json
  • _workspace/distilled/src_010.e584bd6c.json
  • _workspace/distilled/src_011.e584bd6c.json
  • … and 54 more

Open the folder on GitHubat commit a38f5fc

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Categories

Questions about Julie E Buring

What does Julie E Buring do?

A skill your agent uses to apply the rigorous epidemiological reasoning of Julie E. Julie E Buring is an agent skill from K-Dense-AI/mimeographs. Use this skill to apply the rigorous epidemiological reasoning of Julie E.

When should I use Julie E Buring?

Julie E Buring fits situations like: apply the rigorous epidemiological reasoning of Julie E; this skill whenever you are evaluating medical literature; designing cohort studies; assessing conflicting research findings.

How do I install Julie E Buring in Claude Code?

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

How do I install Julie E Buring in Codex?

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

Can I use Julie E Buring 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 julie-e-buring -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/julie-e-buring, .gemini/skills/julie-e-buring, .github/skills/julie-e-buring and .opencode/skills/julie-e-buring in your project.

What does Julie E Buring need to run?

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

Does Julie E Buring 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 Julie E Buring 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 Julie E Buring use?

Julie E Buring 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 Julie E Buring use?

About 1.7k tokens (SKILL.md is roughly 6.9k 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 4.7k tokens, read only when the agent opens those files.

What are the alternatives to Julie E Buring?

Skills that share tags, products or a category with Julie E Buring: Clinical Protocol Drafting (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Clinical Research (alirezarezvani/claude-skills, 28k stars), Clinical Research (borghei/Claude-Skills, 881 stars) and Bio Clinical Biostatistics Adaptive Designs (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 Julie E Buring?

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.