Decompose problems into pipelines of data transformations. Refactors loops into map/filter/reduce chains, converts nested/OO logic into composable function sequences, designs multi-step data transformation pipelines. Trigger on: "transformational programming", "data pipeline", "function pipeline", "pipe operator", "|>", "stream processing", "chained transformations", "Unix pipes", "dataflow", "decompose into steps", "write this as a pipeline", "compose functions", "chain of transformations", or restructuring imperative/OO code into data transforms. NOT for ETL infrastructure or stream processing frameworks (Kafka, Flink) — focuses on code-level function composition and transformation design patterns.
94
97%
Does it follow best practices?
Impact
85%
1.19xAverage score across 3 eval scenarios
Passed
No known issues
Correct output format structure
Transformation Analysis heading
0%
50%
Data Flow section
20%
60%
Arrow notation in Data Flow
0%
100%
Step breakdown section
20%
0%
Step breakdown as markdown table
0%
0%
Implementation section
50%
60%
Error handling section
0%
50%
Input-output function framing
100%
100%
Single-responsibility steps
70%
100%
Pure functions and JS method chaining
No shared mutable state
100%
100%
Pure stage functions
72%
100%
JS method chaining used
100%
100%
Single-responsibility stages
83%
100%
Top-level compose function
100%
100%
Data Flow documented
100%
100%
Step breakdown documented
100%
100%
Error/skip handling without mutation
57%
100%
Elixir pipe operator and ok/error tuples
Pipe operator used
100%
100%
ok/error tuples for errors
100%
100%
Pattern matching on error tuples
100%
100%
Errors propagate without aborting
100%
100%
Pure transformation functions
100%
100%
Data Flow documented
80%
100%
Step breakdown documented
100%
100%