Browse and discover pipeline utilities
Wrap string fields in localization objects while keeping the result as structured JSON
Compute rollups and summary metrics over arrays such as count, sum, average, unique values, and flatten
Clean noisy JSON by removing nulls, empties, duplicates, and other low-value clutter
Create derived values or fields from formulas, expressions, and simple conditionals
Enforce expected source-shape requirements before a pipeline transforms data
Copy, move, or swap values between known JSON paths without broad restructuring
Parse CSV text into a JSON array of row objects for further cleanup or reshaping
Combine two object trees into one with configurable deep-merge conflict resolution
Compare two JSON inputs and return a structured diff of what changed
Add or default fields on objects using expressions, generated values, and fallbacks
Convert objects to key-value entry arrays or rebuild objects from entries
Filter rows, items, keys, or values by explicit conditions and keep only the matches you want
Find and replace matching keys or values across JSON using broad search patterns
Convert nested objects to flat key paths or rebuild them using delimiter and casing rules
Reformat individual values with case changes, trimming, coercion, and slugification
Generate mock JSON data from a template for testing, demos, or seeding pipelines
Convert JSON array row data into final CSV text output
Remap existing values through a lookup table such as enums, codes, or category names
Enforce a consistent schema across array items by filling missing keys and removing extras
Parse browser DevTools console text into a structured JSON array of console entries
Parse Named Debug Structured log lines into structured JSON records
Keep only a known allowlist of fields and remove everything else
Mask secrets and PII while keeping the surrounding JSON structure intact
Remove known fields, paths, or matching keys from JSON without masking the values
Rename known keys or convert key casing without changing the underlying values
Flexibly restructure collections by grouping, unwinding, transposing, or rearranging nested data
Enforce a known target shape by extracting paths, ensuring arrays, unwrapping values, or plucking indices
Summarize a payload with compact statistics, structural overviews, and anomaly signals
Shape tabular JSON rows with spreadsheet-like operations before later transforms or export
Reduce oversized payloads by truncating strings, capping arrays, and dropping large blobs