Detailed Analysis
A Reddit post detailing a small but telling piece of developer tooling has surfaced in the Claude Code community: a plugin called "onebreath" that forces Claude to respond in either a single sentence or a single paragraph, invoked via the slash commands `/onebreath:1s` and `/onebreath:1p`. The creator, user louixs, built the tool to solve a recurring annoyance—Claude's tendency to produce verbose, thoroughly-hedged responses even when a quick status check or a simple yes/no judgment call is all that's needed. Rather than retyping "please answer in one sentence" dozens of times per day, the developer packaged that instruction into a reusable command and released it as free, open-source software on GitHub under an MIT license.
The underlying problem the plugin addresses is a well-known friction point in how large language models like Claude are tuned to behave. Anthropic has trained Claude to be helpful and thorough, which often manifests as long, carefully qualified answers even for questions that call for a terse reply. This becomes especially cumbersome in developer workflows where engineers run multiple Claude Code sessions simultaneously—the post mentions having five or six sessions open at once—and just need a quick pulse check on what's happening in each one rather than a full explanation. In that context, a wall of text isn't more helpful; it's an obstacle to fast context-switching, forcing the user to scan and mentally compress information that the model could have compressed itself.
The examples given in the post illustrate the practical value of terse-mode prompting. One example asks whether a coding task warrants upgrading to a more powerful model (Opus) versus using a lighter one (Sonnet or "Fable"), and the one-sentence answer efficiently delivers a recommendation with a compact justification. Another example, asking which open-source license to use, gets a one-paragraph answer that covers the recommendation (MIT) and briefly weighs it against alternatives (Apache 2.0, GPL) without unnecessary elaboration. These aren't trivial UX conveniences—they reflect a broader pattern in how developers are learning to "instruct around" default model behavior through prompt engineering and, increasingly, through custom tooling that encodes those instructions permanently rather than repeating them ad hoc.
This development fits into a larger trend of the Claude Code ecosystem maturing around plugins, custom slash commands, and skills that let users customize model behavior for specific workflows rather than relying on one-size-fits-all defaults. Anthropic's decision to make Claude Code extensible—supporting plugins, hooks, and custom commands—has enabled a growing community of third-party tools that patch over friction points the base model doesn't solve out of the box, verbosity control being one of the most common complaints. It also reflects a broader industry conversation about "AI verbosity fatigue," where users across many LLM products are pushing back against overly hedged, padded responses in favor of terser, more decisive output, particularly in professional and technical contexts where speed and clarity matter more than exhaustive explanation. Small community-built tools like onebreath signal that users are willing to build and share their own solutions rather than wait for foundation model providers to bake in better default response-length calibration.
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