Cm

CmdBox – A CLI tool for saving and running parameterized commands

Hacker News

CmdBox – A CLI tool for saving and running parameterized commands

For years I've worked with a lot of complex, hard to remember, or tedious to enter, shell commands. I managed them with shell aliases, notes, and google searches, but aliases don't support parameters well, notes are difficult to search and organize, and having to constantly google commands is tiresome. I wanted a tool that made it easy to recall infrequently used commands and allowed me to enter tedious commands quickly. CmdBox lets you store commands with named variables using angle bracket syntax, like <host> or <port>, and either prompts you for values at runtime or lets you store values so they are reusable in several commands. It also allows for nested commands inside other commands using <cmd:command-name>, so you can build up reusable building blocks rather than duplicating logic. A few other features it supports: stored execution context per command (working directory, shell, environment variables, and timeout) with runtime overrides, multi-line command templates executed as scripts, tag based organization and filtering, field based search across commands, import and export for command sharing, and configurable global settings for shell, output capture, color and more. It is written in Python, installable via pip as cmdbox-cli, and works on Linux, macOS, and Windows. I would love feedback on the concept, design, features, or anything else. Happy to answer any questions as well.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, google · Missing: agents, agent, cursor
88%88% 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: supports, para · Missing: reddit linkedin, podcasting, created
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, 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
41%41% 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: google, para · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, 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.

Correct prediction on native model

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