py

pyproject – A linter for your Python project configuration

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pyproject – A linter for your Python project configuration

Hey all, I've been working on a static analysis tool (and language server) for `pyproject.toml` files after encountering inconsistencies in build tool error reporting (e.g. some tools will let you ship empty licenses directories). It would be nice to have a single source of truth for PEP 621 related checks and beyond that can run prior to running more expensive workflows. There are already a few basic rules for PEP 621-related errors and warnings, but it’s easily extendable to fit any specific tool’s requirements. It's written in Rust and uses `ariadne` for command-line diagnostic reporting and `taplo` for the linting backend. There are also a surprising (maybe not so much if you know about Astral) number of useful crates that implement PEP-specific things (i.e. PEP 440, PEP 508) that I found useful for a bunch of the supported rules. It's still heavy alpha software, but I thought I'd share in case there's interest for something like this :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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