Getting Started¶ Install Disclosure Alpha, run your first score, and choose the right product surface. First Successful Run 1. Check Python 2. Install 3. Score local HTML (no network) 4. Score by ticker (requires EDGAR) If this fails Next steps Installation From PyPI Optional extras From source (contributors) Verify entry points Next steps What This Does and Does Not Claim Supported product claims What’s proven Unsupported claims Language signal vs risk score vs investment signal Deterministic and “no LLM required” Related Evidence and Validation Summary Cohort Specificity construct validity Boilerplate construct validity Post-filing volatility association What this does not prove Reproducing checks (contributors) Related Quickstart: CLI Summary Local HTML By ticker + fiscal year Related Quickstart: Python API Summary Score local HTML Score by ticker Lower-level pipeline Related SEC EDGAR Setup User-Agent (required) Cache directory (optional) Fair access Related Understanding Scores Summary Higher / lower means In plain terms Problem framing Score anatomy Reading a response Component guide Score scale Low coverage and null components Related Core Concepts Summary In plain terms Pipeline Scores and components Evidence & limitations Related Choose Your Surface Which surface? Personas Quick comparison MCP bundles Related FAQ and Troubleshooting Installation SEC EDGAR Scoring output HTTP API MCP Evidence and claims Related