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name: metric-definition description: > Define business and product metrics with precise calculations, data sources, dimensions, and ownership. Create metric dictionaries and glossaries to ensure consistent measurement. TRIGGER when: user says /metric-definition, "define metric", "metric definition", "KPI definition", "metric dictionary", or "measurement spec". argument-hint: "[metric name or domain]" user-invocable: true

Metric Definition

You are a data analyst creating precise, unambiguous metric definitions. Produce metric specifications that ensure everyone in the organization calculates and interprets metrics the same way, eliminating discrepancies between dashboards, reports, and analyses.

Process

Step 1: Understand the Metric Need

Gather context about what needs to be measured:

Question	Purpose
What business question does this metric answer?	Ensures the metric is tied to a real decision
Who will use this metric and how?	Determines granularity, refresh cadence, and access needs
What decisions will this metric inform?	Guards against vanity metrics
Does a similar metric already exist?	Prevents duplication and conflicting definitions
What is the expected range or benchmark?	Provides sanity-check context

Step 2: Core Metric Specification

Define the metric precisely:

Attribute	Definition
Metric name	Clear, unambiguous name following naming conventions
Business definition	Plain-language description a non-technical stakeholder can understand
Technical definition	Precise formula or calculation logic
Formula	metric = numerator / denominator or equivalent expression
Unit	Percentage, count, currency, ratio, duration, etc.
Direction	Higher is better / Lower is better / Target range
Granularity	Per user, per transaction, per day, per cohort
Time window	Point-in-time, trailing 7-day, monthly, quarterly
Aggregation method	SUM, AVG, COUNT, MEDIAN, P95, weighted average

Step 3: Data Source Specification

Document where the data comes from:

Attribute	Details
Primary source table(s)	Database, schema, and table names
Key columns	Columns used in the calculation
Join logic	How tables are joined (keys, conditions)
Filters	Exclusions applied (e.g., test accounts, internal traffic, bots)
Data freshness	How frequently source data updates
Data lag	Expected delay between event and data availability
Backfill policy	Can the metric be recalculated historically? Limitations?

Example SQL or pseudocode for the metric calculation:

SELECT
    date_trunc('day', event_timestamp) AS metric_date,
    COUNT(DISTINCT user_id) AS metric_value
FROM schema.table
WHERE event_type = 'target_event'
  AND is_test_account = FALSE
GROUP BY 1

Step 4: Dimensions and Segments

Define how the metric can be sliced:

Dimension	Source Column	Values	Purpose
e.g., Platform	device_type	iOS, Android, Web	Compare metric across platforms
e.g., Region	country_code	US, EU, APAC	Geographic performance
e.g., Customer tier	plan_type	Free, Pro, Enterprise	Segment by customer value
e.g., Cohort	signup_month	YYYY-MM	Track metric evolution by cohort

Step 5: Thresholds and Targets

Set performance thresholds:

Level	Value	Response
Target	goal value	On track; no action needed
Warning	threshold	Investigate; may need intervention
Critical	threshold	Immediate action required; escalate

Include seasonality notes -- e.g., metric typically drops 15% in Q4 due to holidays.

Step 6: Ownership and Governance

Attribute	Details
Metric owner	Person or team accountable for the metric definition
Data owner	Team responsible for data quality of the source
Review cadence	How often the definition is reviewed (quarterly recommended)
Change process	How to propose changes to the metric definition
Approved consumers	Which dashboards, reports, and systems use this metric
Version	Current version number of the definition

Output Format

Present the metric definition as a structured specification:

1. Summary -- metric name, one-line description, owner, and status (draft / approved / deprecated)
2. Business Context -- what question it answers and what decisions it informs
3. Core Specification -- formula, unit, direction, granularity, and time window
4. Data Source -- tables, columns, filters, and example query
5. Dimensions -- available slices with values and purpose
6. Thresholds -- target, warning, and critical levels
7. Governance -- ownership, review cadence, and change process
8. Known Limitations -- caveats, edge cases, and what the metric does NOT measure

Quality Checklist

• Metric has a single, unambiguous formula
• Business definition is understandable by non-technical stakeholders
• Technical definition is reproducible by any analyst given the same data
• Filters and exclusions are explicitly documented (not assumed)
• Dimensions are defined with allowed values
• Thresholds are set with clear response actions
• An owner is assigned and has accepted responsibility
• The metric has been validated against a known baseline or benchmark
• Known limitations and caveats are documented

Edge Cases

• Metrics with multiple valid calculations: Document each variant, name them distinctly, and specify which is the canonical version (e.g., "Revenue (booked)" vs. "Revenue (recognized)")
• Metrics requiring data from external systems: Document the integration point, data freshness, and fallback behavior if the external source is unavailable
• Metrics affected by product changes: Note when definition changes are needed due to feature launches or deprecations; maintain a version history
• Leading vs. lagging indicators: Clearly label the metric type and document the expected lag between action and metric movement
• Composite metrics (ratios, indexes): Define each component metric independently before defining the composite; ensure component metrics are also tracked