Loading...
Loading...
`azure-functions-doctor` is a diagnostic CLI for projects built on the Azure Functions Python v2
# PRD - azure-functions-doctor ## Overview `azure-functions-doctor` is a diagnostic CLI for projects built on the Azure Functions Python v2 programming model. It inspects a local project and reports common configuration, dependency, and environment issues before they surface during local runs or deployment. ## Problem Statement Azure Functions Python projects can fail for routine reasons that are easy to miss: - missing `host.json` - missing or incorrect `azure-functions` dependency - unsupported Python configuration - incomplete local development setup - inconsistent project layout These failures are often discovered late, through confusing runtime errors or deployment issues. ## Goals - Provide fast, readable diagnostics for Azure Functions Python v2 projects. - Surface required and optional checks with clear pass/fail output. - Support both local CLI use and CI integration. - Keep checks aligned with representative example projects. ## Non-Goals - Fixing project issues automatically - Replacing Azure Functions Core Tools - Managing deployment workflows - Supporting the legacy `function.json`-based Python v1 model ## Primary Users - Maintainers of Azure Functions Python repositories - Developers setting up local Azure Functions projects - Teams that want lightweight CI diagnostics for Functions projects ## Core Use Cases - Run diagnostics against the current project directory - Run diagnostics against a specific example or target path - Use a smaller profile for required-only checks - Consume human-readable or machine-readable output in automation ## Success Criteria - Representative examples pass diagnostic smoke tests in CI - Broken example copies fail in predictable ways - CLI output remains stable enough for user troubleshooting and automation ## Example-First Design ### Philosophy A diagnostic CLI earns trust by showing exactly what it does. `azure-functions-doctor` ships runnable example projects — both healthy and intentionally broken — so developers can see real pass/fail output before pointing the tool at their own code. ### Quick Start (Hello World) Run diagnostics against the representative example: ```bash pip install azure-functions-doctor azure-functions doctor --path examples/v2/http-trigger ``` Expected output shows a clean diagnostic pass: ```text Azure Functions Doctor [PASS] HOST_JSON_EXISTS [PASS] REQUIREMENTS_TXT_EXISTS [PASS] AZURE_FUNCTIONS_DEPENDENCY [PASS] V2_DECORATORS_USED ... Result: All checks passed ``` Run against a broken example to see clear failure output: ```bash azure-functions doctor --path examples/v2/broken-missing-host-json ``` ### Why Examples Matter 1. **Lower entry barrier.** Developers can run the CLI against bundled examples before applying it to their own project. The pass/fail contrast builds confidence. 2. **AI agent discoverability.** Tools like GitHub Copilot, Cursor, and Claude Code recommend libraries based on README, PRD, and example content. CLI output samples in documentation help AI agents understand what `azure-functions-doctor` does and when to suggest it. 3. **Cookbook role.** For niche ecosystems, `examples/` and `docs/` often serve as the primary learning material. Both healthy and broken examples teach diagnostic patterns. 4. **Proven approach.** FastAPI, LangChain, SQLAlchemy, and Pandas all achieved early adoption through extensive, copy-paste-friendly examples and clear output samples. ### Examples Inventory | Role | Path | Pattern | |---|---|---| | Representative | `examples/v2/http-trigger` | Minimal HTTP trigger (passes all checks) | | Representative | `examples/v2/timer-trigger` | Timer trigger (passes all checks) | | Complex | `examples/v2/multi-trigger` | Multiple triggers in one app | | Complex | `examples/v2/blueprint` | Blueprint-based modular routing | | Broken | `examples/v2/broken-missing-host-json` | Missing host.json | | Broken | `examples/v2/broken-missing-requirements` | Missing requirements.txt | | Broken | `examples/v2/broken-missing-azure-functions` | Missing azure-functions dep | | Broken | `examples/v2/broken-no-v2-decorators` | No v2 decorators | All examples are smoke-tested in CI. New diagnostic rules should ship with a corresponding broken example that demonstrates the failure.
SkillSprout is an AI-powered microlearning platform designed to help users learn new skills through bite-sized lessons and adaptive quizzes. The platform leverages Azure OpenAI for content generation, Gradio for user interaction, and Model Context Protocol (MCP) for agent interoperability.
This dashboard is a web-based interface built using **Next.js (or Astro)** and hosted on **Vercel**. It acts as the control center for Joey’s stock intelligence, allowing you to:
Gemini Code Flow is an advanced AI-powered development orchestration platform that adapts RuV's Claude Code Flow for Google's Gemini CLI. It enables developers to leverage multiple AI agents working in parallel to write, test, and optimize code using the SPARC methodology.
**Version: 6.0 (FINAL)**