The Neovim syntax tree traversal plugin you've been waiting for

Hacker News

Treewalker.nvim For a while I'd been wanting a good syntax tree traversal and manipulation plugin for neovim. I tried stuff like treesitter-textobjects and syntax-tree-surfer but neither could meet my needs. So I built the one I'd been wanting. Introducing Treewalker.nvim ( https://github.com/aaronik/treewalker.nvim ). It offers movement and swapping. Design goals include stability and ergonomics. Movement * Up/down go to neighbor nodes up/down in the document. * Left goes to the parent node * Right goes to next found indent in the document Each has their own spin on a literal tree movement. The ultimate goel is to make the movement feel ergonomic, not to strictly adhere to the AST. Swapping * Up/down swaps take the highest node on the line _and its comments, decorators, and annotations_. This makes it easy to swap things like route handlers. These swaps operate on whole lines. * Left/right swaps operate on the node under the cursor. This is good for swapping arguments and list items. The plugin aims to be stable - it's well tested and leverages types with luacheck in the CI. There are few options. It's meant to "just work". Ok that's all, I hope this can be of some use to folks!

Visit product View on Hacker NewsJan 19, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: cursor · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% 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

44%44% 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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