Claude-skill-registry-data measure

Quantify values with uncertainty bounds. Use when estimating metrics, calculating risk scores, assessing magnitude, or measuring any quantifiable property.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry-data

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/measure" ~/.claude/skills/majiayu000-claude-skill-registry-data-measure && rm -rf "$T"

manifest: data/measure/SKILL.md

source content

Intent

Quantify a specific metric for a target, providing a numerical value with explicit uncertainty bounds. This capability consolidates all estimation tasks (risk, impact, effort, etc.) into a single parameterized operation.

Success criteria:

Numerical value provided for requested metric
Uncertainty bounds explicitly stated
Measurement method documented
Units clearly specified

Compatible schemas:

```
schemas/output_schema.yaml
```

Inputs

Parameter	Required	Type	Description
`target`	Yes	any	What to measure (system, code, entity, process)
`metric`	Yes	string	The metric to quantify (risk, complexity, effort, size, etc.)
`unit`	No	string	Unit of measurement (optional, inferred if not provided)
`method`	No	string	Measurement approach (heuristic, statistical, model-based)

Procedure

Define the metric: Clarify exactly what is being measured
- Establish clear definition of the metric
- Identify appropriate unit of measurement
- Determine measurement methodology
Gather measurement inputs: Collect data needed for measurement
- Read relevant files, logs, or data sources
- Identify quantifiable indicators
- Note data quality and completeness
Calculate measurement: Apply measurement method to inputs
- Use appropriate calculation for the metric type
- For risk: probability * impact assessment
- For complexity: cyclomatic, cognitive, or structural metrics
- For effort: decomposition and estimation techniques
Establish uncertainty bounds: Quantify measurement confidence
- Calculate or estimate lower and upper bounds
- Consider data quality, method limitations
- Express as confidence interval or range
Ground the measurement: Document evidence and method
- Reference specific data points used
- Note any assumptions in calculation
- Document measurement methodology

Output Contract

Return a structured object:

value: number  # The measured value
uncertainty:
  lower: number  # Lower bound (confidence interval)
  upper: number  # Upper bound (confidence interval)
  type: string  # Type of uncertainty (confidence_interval, range, std_dev)
unit: string  # Unit of measurement
metric: string  # What was measured
method: string  # How it was measured
breakdown:  # Optional component breakdown
  - component: string
    value: number
confidence: 0..1  # Confidence in measurement accuracy
evidence_anchors: ["file:line", "data:source"]
assumptions: []

Field Definitions

Field	Type	Description
`value`	number	Primary measurement value
`uncertainty`	object	Bounds and type of uncertainty
`unit`	string	Unit of measurement
`metric`	string	Name of measured property
`method`	string	Measurement methodology used
`breakdown`	array	Optional decomposition of measurement

Examples

Example 1: Measure Code Complexity

Input:

target: "src/services/order_processor.rb"
metric: "complexity"
unit: "cyclomatic"

Output:

value: 24
uncertainty:
  lower: 22
  upper: 26
  type: "range"
unit: "cyclomatic complexity"
metric: "complexity"
method: "counted decision points (if, case, loops, &&, ||)"
breakdown:
  - component: "process_order method"
    value: 12
  - component: "validate_items method"
    value: 8
  - component: "calculate_totals method"
    value: 4
confidence: 0.9
evidence_anchors:
  - "src/services/order_processor.rb:15-120"
assumptions:
  - "Ruby control flow statements counted as decision points"
  - "Exception handling counted as branches"

Example 2: Measure Risk

Input:

target: "database migration: add user_preferences column"
metric: "risk"
method: "probability_impact"

Output:

value: 0.35
uncertainty:
  lower: 0.25
  upper: 0.50
  type: "confidence_interval_90"
unit: "risk score (0-1)"
metric: "risk"
method: "probability (0.5) * impact (0.7) normalized"
breakdown:
  - component: "probability_of_failure"
    value: 0.5
  - component: "impact_if_failed"
    value: 0.7
  - component: "data_loss_risk"
    value: 0.2
  - component: "downtime_risk"
    value: 0.4
confidence: 0.7
evidence_anchors:
  - "migrations/20240115_add_preferences.rb:1-25"
  - "tool:grep:similar_migrations"
assumptions:
  - "Table has ~1M rows based on user count"
  - "Migration will lock table during ALTER"
  - "No concurrent deployments during migration"

Verification

Numerical value provided
Uncertainty bounds are reasonable (lower < value < upper)
Unit of measurement specified
Method documented
Evidence anchors reference measurement inputs

Verification tools: Read (to verify measurement inputs)

Safety Constraints

```
mutation
```
: false
```
requires_checkpoint
```
: false
```
requires_approval
```
: false
```
risk
```
: low

Capability-specific rules:

Always provide uncertainty bounds, never claim false precision
Document measurement methodology for reproducibility
Flag when data is insufficient for reliable measurement
Do not extrapolate beyond available data without noting assumptions

Composition Patterns

Commonly follows:

```
observe
```
- Measure properties of observed state
```
detect
```
- Measure characteristics of detected items
```
retrieve
```
- Measure retrieved data

Commonly precedes:

```
predict
```
- Measurements feed into predictions
```
compare
```
- Measurements enable quantitative comparison
```
plan
```
- Measurements inform risk-aware planning

Anti-patterns:

Never use measure for binary detection (use
```
detect
```
)
Avoid measure for categorical assessment (use
```
classify
```
)

Workflow references:

See

reference/workflow_catalog.yaml#digital_twin_sync_loop

for risk measurement