Builds an end-to-end pipeline to cluster text data with HCI and visualize it.
npx skills add https://github.com/josh-cooper/.claude --skill clio-clustering
Please help me install this Agent Skill. Skill: clio-clustering Source: https://github.com/josh-cooper/.claude/tree/main/skills/clio-clustering Command: npx skills add https://github.com/josh-cooper/.claude --skill clio-clustering
Builds an end-to-end clustering and visualization pipeline for text data, enabling pattern discovery and exploratory analysis across sources like GitHub issues, Slack messages, tickets, code reviews, and forum posts.
Follow the phases to identify data sources, verify access with tests, fetch data, generate embeddings, perform hierarchical clustering, label clusters with an LLM, and launch the interactive visualization.
High-intent search queries and answers about installing and using this skill.
To cluster text data from sources like GitHub issues and Slack messages, this pipeline scrapes your sources, generates OpenAI embeddings, and applies HDBSCAN hierarchical clustering. It then produces an interactive visualization to explore the resulting clusters and drill down into individual items.
Using an LLM to label clusters of unstructured text automatically generates descriptive summaries for quick interpretation. This approach pairs hierarchical clustering results with LLM-based labeling to assign meaningful titles and descriptions to each discovered group.
Yes, you need an OpenAI API key set as an environment variable to generate text embeddings. The semantic clustering pipeline relies on the OPENAI_API_KEY environment variable to process text data, alongside provider-specific tokens for data sourcing.
Yes, HDBSCAN hierarchical clustering is well-suited for exploratory data analysis of text embeddings. It groups similar items into meaningful clusters without requiring a predefined number of clusters, making it ideal for discovering unknown patterns in text data.
Yes, the pipeline supports data sourcing and scraping from forums, tickets, code reviews, and Slack messages. It fetches text data directly from these sources before generating embeddings and performing hierarchical clustering for exploratory analysis.
A SQLite backend manages the workflow state and fetched text data during the clustering pipeline. It supports the end-to-end process by storing sourced data, embeddings, and hierarchical clustering results before rendering them in the interactive visualization frontend.
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