How do I install the Equiflow skill?

Run npx skillmds@latest add tomevault-io/equiflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Equiflow safe to use?

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Which AI agents work with Equiflow?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Equiflow free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Equiflow?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Equiflow by tomevault-io — Agent Skill

Name: Equiflow
Author: tomevault-io

Equiflow

Generate equity-focused cohort selection flow diagrams. Tracks demographic, socioeconomic, and outcome variables at each exclusion step and calculates SMD to detect selection bias. Use for cohort construction, CONSORT-style diagrams, or bias detection in clinical ML/research. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

EquiFlow - Equity-Focused Cohort Flow Diagrams

Visualize and quantify selection bias in clinical ML/research cohorts. Based on Ellen et al. (2024) J Biomed Inform.

When to Use This Skill

Building patient cohorts from MIMIC-IV or eICU
Documenting inclusion/exclusion criteria with CONSORT-style diagrams
Detecting disproportionate exclusion of vulnerable groups
Quantifying selection bias via Standardized Mean Difference (SMD)

Default Equity Variables

When no variables are specified, CohortFlow automatically tracks:

Category	Variables	Column Aliases
Demographics	gender	gender, sex
	race	race, ethnicity
	age	anchor_age, age, admission_age
Socioeconomic	insurance	insurance, insurance_type, payer
	language	language, primary_language
	marital_status	marital_status, marital
Clinical	los	los, length_of_stay, icu_los
Outcome	mortality	hospital_expire_flag, mortality, death

SMD Interpretation

| |SMD| | Interpretation | Action | |-------|----------------|--------| | < 0.1 | Negligible | OK | | 0.1-0.2 | Small | Monitor | | > 0.2 | Meaningful | Investigate | | > 0.5 | Large | Serious concern |

See references/smd_interpretation.md for detailed guidance.

Critical Implementation Notes

Auto-detection: CohortFlow scans DataFrame columns for known aliases. Override with use_defaults=False if you want full control.
SMD > 0.2 is the default threshold for flagging potential bias. This follows established covariate balance literature.
Missing data: Exclusion steps that remove patients with missing values can introduce systematic bias. Always check SMD after such steps.

Example Queries

Full workflow with MIMIC-IV

from cohort_flow import CohortFlow

# Step 1: Query MIMIC-IV
query = """
SELECT
    p.subject_id, p.gender, p.anchor_age,
    a.race, a.insurance, a.language, a.marital_status,
    a.hospital_expire_flag,
    i.los
FROM mimiciv_hosp.patients p
JOIN mimiciv_hosp.admissions a USING (subject_id)
JOIN mimiciv_icu.icustays i ON a.hadm_id = i.hadm_id
"""
df = execute_query(query)

# Step 2: Build cohort with equity tracking
cf = CohortFlow(df)  # Auto-detects equity variables

cf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")
cf.exclude(df['los'] >= 24, "ICU < 24h", "ICU >= 24h")
cf.exclude(df['anchor_age'] <= 90, "Age > 90", "Age 18-90")

# Step 3: Check for bias
print(cf.check_bias())  # Flags variables with SMD > 0.2

# Step 4: Generate diagram
cf.plot("sepsis_cohort_flow")

Minimal example

cf = CohortFlow(df)
cf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")
cf.view_drifts()  # SMD values after exclusion

Dependencies

pip install equiflow

References

Ellen JG, et al. "Participant flow diagrams for health equity in AI." J Biomed Inform. 2024;152:104631.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: equiflowdescription: Generate equity-focused cohort selection flow diagrams. Tracks demographic, socioeconomic, and outcome variables at each exclusion step and calculates SMD to detect selection bias. Use for cohort construction, CONSORT-style diagrams, or bias detection in clinical ML/research. Use when this capability is needed.---# EquiFlow - Equity-Focused Cohort Flow DiagramsVisualize and quantify selection bias in clinical ML/research cohorts. Based on Ellen et al. (2024) J Biomed Inform.## When to Use This Skill- Building patient cohorts from MIMIC-IV or eICU- Documenting inclusion/exclusion criteria with CONSORT-style diagrams- Detecting disproportionate exclusion of vulnerable groups- Quantifying selection bias via Standardized Mean Difference (SMD)## Default Equity VariablesWhen no variables are specified, CohortFlow automatically tracks:| Category | Variables | Column Aliases ||----------|-----------|----------------|| Demographics | gender | gender, sex || | race | race, ethnicity || | age | anchor_age, age, admission_age || Socioeconomic | insurance | insurance, insurance_type, payer || | language | language, primary_language || | marital_status | marital_status, marital || Clinical | los | los, length_of_stay, icu_los || Outcome | mortality | hospital_expire_flag, mortality, death |## SMD Interpretation| |SMD| | Interpretation | Action ||-------|----------------|--------|| < 0.1 | Negligible | OK || 0.1-0.2 | Small | Monitor || > 0.2 | Meaningful | Investigate || > 0.5 | Large | Serious concern |See `references/smd_interpretation.md` for detailed guidance.## Critical Implementation Notes1. **Auto-detection**: CohortFlow scans DataFrame columns for known aliases. Override with `use_defaults=False` if you want full control.2. **SMD > 0.2 is the default threshold** for flagging potential bias. This follows established covariate balance literature.3. **Missing data**: Exclusion steps that remove patients with missing values can introduce systematic bias. Always check SMD after such steps.## Example Queries### Full workflow with MIMIC-IV```pythonfrom cohort_flow import CohortFlow# Step 1: Query MIMIC-IVquery = """SELECT p.subject_id, p.gender, p.anchor_age, a.race, a.insurance, a.language, a.marital_status, a.hospital_expire_flag, i.losFROM mimiciv_hosp.patients pJOIN mimiciv_hosp.admissions a USING (subject_id)JOIN mimiciv_icu.icustays i ON a.hadm_id = i.hadm_id"""df = execute_query(query)# Step 2: Build cohort with equity trackingcf = CohortFlow(df) # Auto-detects equity variablescf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")cf.exclude(df['los'] >= 24, "ICU < 24h", "ICU >= 24h")cf.exclude(df['anchor_age'] <= 90, "Age > 90", "Age 18-90")# Step 3: Check for biasprint(cf.check_bias()) # Flags variables with SMD > 0.2# Step 4: Generate diagramcf.plot("sepsis_cohort_flow")```### Minimal example```pythoncf = CohortFlow(df)cf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")cf.view_drifts() # SMD values after exclusion```## Dependencies```bashpip install equiflow```## References- Ellen JG, et al. "Participant flow diagrams for health equity in AI." J Biomed Inform. 2024;152:104631.---> Converted and distributed by [TomeVault](https://tomevault.io/claim/hannesill) — claim your Tome and manage your conversions.

Frequently asked questions