python_lib
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Tips and guidelines specific to the development of the Streamlit Python library, not applicable to scripts and e2e tests.
18 tagged data analysis, measured the same way as everything else here.
Browse within: data-science 13data-visualization 13deep-learning 13machine-learning 13
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Tips and guidelines specific to the development of the Streamlit Python library, not applicable to scripts and e2e tests.
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This directory contains product and tech specs for Streamlit features.
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Changes to these high-fan-out internals can affect every message, delta, element, or rerun. Keep work in them minimal, and benchmark changes with representative stress-test apps.
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We are building an LLM powered AI data analyst for Data Engineering teams that work with dbt to manage their analytics code bases. To use this project, users should be able to connect with their dbt cloud projects or their dbt core github repos via an interface which is then used to build a knowlege base. We then use…
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Sub-agent Dispatch System for alive-analysis. Auto-triggers specialist recommendations at each ALIVE stage. Use when advancing stages, asking for help on a specific analysis task, or when specialist expertise is needed (statistics, data quality, narrative, ethics).
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ALIVE analysis workflow for structured data analysis. Use when user discusses data analysis, metrics, A/B tests, experiments, monitoring, or asks analytical questions like 'why did X drop' or 'can we predict Y'.
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Analysis Decision Records (DR) for alive-analysis. Auto-checks methodology decisions before suggesting alternatives. Use when running /analysis-dr, during analysis stage transitions, or when methodology choices are being made.
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Perlytics analytics skill routing — match user intent to the right analytical workflow.