An open-source tool that semantically profiles your data using LLMs
The problem we solve is profiling tables: this is the initial step where you need to understand the table and identify any anomalies. During the process, many small decisions require semantic understanding. For example, missing values are normal for 'deathdate' (still alive) but abnormal for 'name.' For outliers, 100 for ages is fine, but some are -1, which is impossible! We use LLMs to semantically understand your tables and detect anomalies. You can try it by uploading a CSV, and we will email back the profile: https://cocoon-data-transformation.github.io/page/ Let me…
An open-source tool that semantically profiles your data using LLMs launched on May 3, 2024 with 10 votes, #1,249 of 4,539 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
An open-source tool that analyzes CSV files to profile and understand data structure and quality. It uses large language models to apply semantic understanding when detecting anomalies, recognizing that missing values are contextual (normal for a deathdate field, abnormal for a name field) and distinguishing between realistic and impossible outliers. Users upload a CSV file and receive a detailed data profile by email. Built for data engineers and analysts who need to understand and assess data quality before processing.
written from the facts on this page · September 2026
From the sources
Data management with LLMs. Contribute to Cocoon-Data-Transformation/cocoon development by creating an account on GitHub.
In the maker’s words, at launch
The problem we solve is profiling tables: this is the initial step where you need to understand the table and identify any anomalies. During the process, many small decisions require semantic understanding. For example, missing values are normal for 'deathdate' (still alive) but abnormal for 'name.' For outliers, 100…
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Source: Show HN post ↗ · launch text quoted in part
