Do I need to memorize specific slash commands?

No. The intent detection system automatically identifies your goals and suggests the correct commands, allowing you to interact with Claude using plain English.

What is Contextune Intent Recognition?

It is a specialized skill for Claude Code that translates natural language requests into specific slash commands for research, planning, and parallel task execution.

How does this skill improve development speed?

It identifies opportunities for parallel development, enabling Claude to run multiple tasks simultaneously in separate git worktrees, which can be 50-70% faster for multi-feature work.

Does it require manual configuration?

No, it features a zero-transformation architecture that works automatically to suggest the best commands and workflows based on your input.

Contextune Discovery & Intent Recognition

Name: Contextune Discovery & Intent Recognition
Author: Shakes-tzd

byShakes-tzd

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Productivity & Workflow

Simplifies Claude Code interactions by mapping natural language to specialized slash commands and parallel development workflows.

About

Contextune's Intent Recognition skill acts as a discovery and navigation layer for the Contextune plugin suite, allowing users to trigger complex development workflows using plain English. It eliminates the need to memorize specific slash commands by automatically detecting intent through a 3-tier semantic cascade and suggesting appropriate tools for research, planning, and parallel execution. This skill is essential for users looking to optimize their context usage and accelerate development through automated parallel agent coordination across multiple git worktrees.

Key Features

Context-aware suggestions for parallelization opportunities
1 GitHub stars
Guided discovery for parallel research and planning agents
3-tier intent detection cascade (Keyword, Model2Vec, Semantic Router)
Automatic natural language to slash command mapping
Zero-configuration integration with existing Claude Code workflows

Use Cases

Triggering complex multi-agent workflows using natural language prompts
Reducing token overhead through modular, intent-specific context engineering
Learning how to utilize Contextune's parallel development and research features

About

Key Features

Context-aware suggestions for parallelization opportunities
1 GitHub stars
Guided discovery for parallel research and planning agents
3-tier intent detection cascade (Keyword, Model2Vec, Semantic Router)
Automatic natural language to slash command mapping
Zero-configuration integration with existing Claude Code workflows

Use Cases

Triggering complex multi-agent workflows using natural language prompts
Reducing token overhead through modular, intent-specific context engineering
Learning how to utilize Contextune's parallel development and research features