Agent Skills: Engage Indicators

Engage Indicators

Manage indicators and metrics in the Engage Analytics dbt project at /Volumes/Biliba/github/engage-analytics/dbt.

How can I help?

Ask the user which task they need help with:

1. Get Started (New User Setup) - Install prerequisites, configure dbt, set up database connection
2. Add a New Questionnaire - Generate models for a new form added to the app
3. Update Anonymization - Mark fields as PII/non-PII in questionnaire metadata
4. Create a New Metric - Add a new indicator to the metrics catalog
5. Modify an Existing Metric - Change how an existing metric is calculated
6. Export Data to S3 - Export anonymized or PII data to S3 buckets
7. Understand Existing Indicators - Query and explore current metrics in the system

Getting Started (New User Setup)

Prerequisites

1. Install uv (Python package manager):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install direnv (optional, for auto-loading env vars):

# macOS
brew install direnv

# Add to shell (bash)
echo 'eval "$(direnv hook bash)"' >> ~/.bashrc

# Add to shell (zsh)
echo 'eval "$(direnv hook zsh)"' >> ~/.zshrc

dbt Setup

cd /Volumes/Biliba/github/engage-analytics/dbt

# Install dbt-postgres via uv
uv tool install dbt-postgres

# Install dbt package dependencies
uv run dbt deps --profiles-dir .

Database Configuration

Create environment variables for database connection. Choose one method:

Option A: Using direnv (recommended)

cd /Volumes/Biliba/github/engage-analytics/dbt
cp .envrc.example .envrc  # or edit existing .envrc
direnv allow

Option B: Manual export

export DBT_HOST=localhost
export DBT_PORT=5432
export DBT_USER=postgres
export DBT_PASSWORD=your_password
export DBT_DATABASE=airbyte
export DBT_SCHEMA=engage_analytics

Edit values to match your PostgreSQL instance.

Verify Setup

cd /Volumes/Biliba/github/engage-analytics/dbt

# Test database connection
uv run dbt debug --profiles-dir .

# Run all models
uv run dbt run --profiles-dir .

Data Export Setup (Optional)

If you need to export data to S3:

cd /Volumes/Biliba/github/engage-analytics/dataexport

# Install Python dependencies
uv sync

# Configure environment
cp .env.example .env
# Edit .env with your database and AWS credentials

Quick Reference

• Indicator specs: indicators/engage-indicators.csv
• Metrics catalog: macros/metrics.sql
• Metrics fact table: engage_analytics.fct_metrics_long
• Project docs: docs/metrics.md

Scripts (in repo root):

• model_generator.py - Generate questionnaire models (named + anon)
• metadata_manager.py - Manage questionnaire metadata
• run_dbt.sh - Run dbt commands with env vars

Data export (in dataexport/):

• run_export.sh - Refresh dbt + export to S3
• export_to_s3.py - Direct S3 export

For full project structure, see references/project-structure.md. For indicator-to-metric mapping, see references/indicator-mapping.md.

Core Capabilities

1. Understand Existing Indicators

Query current metrics:

SELECT metric_id, description, max(value) as latest_value
FROM engage_analytics.fct_metrics_long
GROUP BY 1, 2 ORDER BY 1;

Check indicator coverage in docs/metrics.md - maps all 32 CSV indicators to 64 dbt metrics.

2. Create New Indicator

Guided workflow - ask user these questions:

1. What is the indicator name and description?
2. What domain? (System Use, Programmatic, Treatment, Follow-up, Adoption)
3. What module? (mwTool, IPC, SBIRT, SPI, FWS, Planning Next Steps, All)
4. What is the data source?
   • Questionnaire ID (e.g., Questionnaire/1613532)
   • Task code (e.g., 040 for follow-up)
   • Existing model (e.g., patient, practitioners)
5. What linkId or field contains the data? (for questionnaire-based)
6. What is the data type? (Count, Percent)
7. For percent metrics: what is the numerator and denominator?
8. What disaggregation? (by organization, by practitioner, by month)

Implementation steps:

A. If new source model needed, create in models/metrics/:

-- models/metrics/new_metric_source.sql
-- ABOUTME: [What this model does]
-- ABOUTME: [What indicator it supports]

{{ config(materialized='view') }}

select
    subject_patient_id,
    organization_id,
    -- metric-specific fields
from {{ ref('source_model') }}
where conditions

B. Add to metrics catalog in macros/metrics.sql:

- id: new_metric_name
  unit: count  # or percent
  grain: day
  entity_keys: [organization_id]
  source_model: new_metric_source
  expression: "count(distinct subject_patient_id)"  # for count
  # OR for percent:
  # numerator: "count(distinct case when condition then subject_patient_id end)"
  # denominator: "nullif(count(distinct subject_patient_id), 0)"
  description: "Human-readable description"
  version: v1

C. Rebuild metrics:

cd /Volumes/Biliba/github/engage-analytics/dbt
uv run dbt run --profiles-dir . --select new_metric_source fct_metrics_long

D. Verify:

SELECT * FROM engage_analytics.fct_metrics_long
WHERE metric_id = 'new_metric_name';

3. Add New Questionnaire

When a new form is added to the app:

A. Find questionnaire ID in raw data:

SELECT DISTINCT questionnaire_id
FROM engage_analytics_engage_analytics_stg.stg_questionnaire_response
ORDER BY 1;

B. Extract metadata using metadata_manager.py:

cd /Volumes/Biliba/github/engage-analytics/dbt
python3 metadata_manager.py extract --questionnaire-id NEW_ID

This extracts linkIds and adds them to data/questionnaire_metadata.csv.

C. Review and edit metadata in data/questionnaire_metadata.csv:

• Set anon=TRUE for PII fields (names, DOB, phone, address)
• Set anon=FALSE for non-PII fields
• Add readable label for each field

D. Generate models using model_generator.py:

# Generate both named and anonymized models
python3 model_generator.py all --table qr_new_form --questionnaire-id NEW_ID

# Or generate separately:
python3 model_generator.py named --table qr_new_form --questionnaire-id NEW_ID
python3 model_generator.py anon --table qr_new_form

E. Build:

uv run dbt seed --profiles-dir .  # Reload metadata
uv run dbt run --profiles-dir . --select qr_new_form qr_new_form_anon

Manual model creation (if scripts unavailable):

Named model (models/marts/qr_named/qr_new_form.sql):

{{ config(materialized='view') }}
{% set identifiers = ["Questionnaire/NEW_ID"] %}
{% if identifiers|length == 0 %}
  select null::text as placeholder where false
{% else %}
  {{ build_qr_wide_readable(identifiers, this.name) }}
{% endif %}

Anonymized model (models/marts/qr_anon/qr_new_form_anon.sql):

{{ config(materialized='view') }}
{{ create_anonymized_qr_view('qr_new_form', []) }}

4. Update Anonymization

Edit data/questionnaire_metadata.csv:

• Set anon=TRUE for PII fields (names, DOB, phone, address, SSN, Medicaid)
• Set anon=FALSE for non-PII fields

Rebuild anonymized view:

uv run dbt seed --profiles-dir .
uv run dbt run --profiles-dir . --select qr_*_anon

5. Test Metrics

Verify metric logic matches source data:

-- Get metric value
SELECT metric_id, organization_id, value
FROM engage_analytics.fct_metrics_long
WHERE metric_id = 'metric_name';

-- Verify against source
SELECT organization_id, count(distinct subject_patient_id)
FROM engage_analytics.source_model
GROUP BY 1;

6. Export Data to S3

The dataexport/ directory contains tools for exporting data to S3 buckets.

Full refresh + export (recommended):

cd /Volumes/Biliba/github/engage-analytics/dataexport
./run_export.sh anon     # Anonymized data only
./run_export.sh pii      # PII data only
./run_export.sh          # Both (default)

This runs:

1. dbt run to refresh all models
2. export_to_s3.py to export and upload

Direct export (skip dbt refresh):

cd /Volumes/Biliba/github/engage-analytics/dataexport
uv run python export_to_s3.py --type anon
uv run python export_to_s3.py --type pii
uv run python export_to_s3.py --type both
uv run python export_to_s3.py --type both --delete-local  # Remove local files after upload

Environment setup - create dataexport/.env with:

# Database
DBT_HOST=localhost
DBT_PORT=5432
DBT_USER=postgres
DBT_PASSWORD=your_password
DBT_DATABASE=airbyte

# AWS
AWS_ACCESS_KEY_ID=AKIA...
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1

# S3 Buckets
S3_BUCKET_ANON=engage-analytics-exports-anon
S3_BUCKET_PII=engage-analytics-exports-pii

S3 file structure:

s3://engage-analytics-exports-anon/
  └── 2024/01/12/
      └── engage_analytics_export_anon_20240112_143022.zip

What gets exported:

• Anon: qr_*_anon views, patient_anon, resource tables
• PII: qr_* views, patient, resource tables

Common Patterns

Eligibility from mwTool

-- Extract boolean from mwTool (Questionnaire/1613532)
(jsonb_path_query_first(items::jsonb,
  '$.**.item[*] ? (@.linkId == "flag-name").answer[0].valueBoolean'))::boolean

Acceptance from Planning Next Steps

-- Check acceptance field
WHERE planning_next_steps_did_the_client_accept_X = 'true'

Session Tracking

-- Count sessions by intervention type
SELECT intervention_type, count(distinct qr_id)
FROM engage_analytics.intervention_sessions
GROUP BY 1;

Assessment Score Severity

• PHQ-9: Severe 20-27, Moderate 10-19, Mild 5-9, Minimal 0-4
• GAD-7: Severe 15-21, Moderate 10-14, Mild 5-9, Minimal 0-4