● Covered by 1 source · 1 reportLow impact1 neutral
Terse Plugin for Claude Code Reduces Reply Length and Cost by Removing Filler
Key points
- Terse plugin cuts Claude Code reply length by approximately 50%.
- Removes metaphors, slogans, 'X, not Y' reframes, and bloat from responses.
- Reduces token costs: 51% for Fable 5.1, 32% for Opus 5.5.
- Applies writing rules to replies, documents, commits, and subagents.
Addressing Verbosity in Claude Code
A new plugin, Terse, has been developed for Claude Code to mitigate the AI's tendency to produce overly long and conversational responses. The creator noted consistent issues with Claude generating essays filled with slogans, metaphors, and unnecessary recaps, even when using Claude's built-in 'concise' mode or system prompts.
Plugin Functionality and Impact
The Terse plugin specifically targets and removes various stylistic elements identified as 'filler'. These include figurative language (metaphors), comparative reframes ('X, not Y'), vague principles (slogans), rhythmic phrasing (cadence), sentences adding no new facts (bloat), and first-person narration. The plugin applies these writing rules across all Claude outputs, including replies, documents, commits, and subagents.
Measured Reductions in Length and Cost
Testing against 20 prompts on a real codebase showed significant reductions. For Claude Fable 5.1, the plugin resulted in 46% fewer words and a 51% lower cost. With Claude Opus 5.5, word count decreased by 54% and cost by 32%. These metrics indicate the plugin's effectiveness in streamlining AI communication and potentially reducing operational expenses associated with token usage.
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Primary sources
GitHub cmc_internal/api GitHub github/collect GitHub lowenbjer/claude-terse GitHub _private/browser GitHub get-started/accessibility GitHub open-source/sponsorsReporting from
A new Claude Code plugin named Terse has been released, designed to reduce the length of Claude's responses by removing conversational filler, metaphors, and slogans. This plugin reportedly halves reply length and lowers token costs, addressing user frustration with verbose AI output.