An LSP for agent skills–rename, references, completion, and diagnostics

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I was quite surprised to find no one had built an LSP for agent skills — one where you can go-to-definition, get skill-completions, refactor (and it renames the folder and the name:), so I went ahead and built one! (Reminds me of the old Ruby on Rails days where we had to find-and-replace-all in our codebase to refactor things lol.) Since submitting this on Monday, I've expanded it to provide: - 1-click install for VS Code, Cursor, Windsurf, VSCodium, Zed - References & description-on-hover - Did you mean / safe quick fixes for broken links - Precise diagnostics for malformed YAML, duplicate keys, invalid types and names, and unbalanced XML I’d especially love feedback from people maintaining public skill collections: what breaks as those repositories grow?

Visit product View on Hacker NewsJul 31, 2026

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

78%78% 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.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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

34%34% 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

32%32% 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

20%20% 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.

Correct prediction on native model

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