Complish – Generate Fish-Completion File for Any CLI via ChatGPT

Hacker News

Background Well-known CLIs often come with auto-completion, while there are still many CLIs that lack auto-completion scripts in different kinds of shell. It's frustrating for me to execute `cmd --help` every time I forget the parameter. I hope complish (named after fish+completion and accomplish) can solve this problem in my favorite fish shell. Approach 1. parse help page of a CLI 2. generate fish-completion file ChatGPT demonstrates excellent performance in text-to-structure. So I use OpenAI's API to parse CLI's help page and then generate fish-completion scripts. Drawback 1. a little slow since it calls OpenAI's API. It takes 10s to 1min to do this work depending on the scale of the help page text. 2. some help pages are too long to parse in one call, I plan to split help page text to handle this. After all, welcome to have a try and give me suggestion.

Visit product View on Hacker NewsMar 12, 2023

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

83%83% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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 · Strong signals: para · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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