What problem does it solve? Writers and analysts need to quantify how an author uses language functionally — asserting, advising, explaining, questioning, challenging, or agreeing — rather than just what they say. This Skill measures the distribution of speech acts across a writing corpus to produce a replicable pragmatic signature for voice profiling and style matching. ## Core Features & Use Cases - Speech Act Classification: Categorizes each text unit into six primary acts (asserting, explaining, advising, questioning, challenging, agreeing/supporting) plus secondary acts using signal-phrase dictionaries with contextual override for indirect speech acts. - Pragmatic Signature Generation: Computes proportions, dominance rankings, pragmatic diversity (normalized Shannon entropy), and ratios like challenge-to-agree to produce numeric replication constraints. - Cross-Context Comparison: Compares speech act distributions across topics, time periods, or audiences to detect communicative shifts. - Use Case: Given a Reddit comment export, classify every comment, discover the author explains 35% of the time and challenges 20%, then encode those proportions as constraints for a voice-replication agent. ## Quick Start Analyze the writing samples in this project with the speech-act-pragmatic skill and write the pragmatic signature report to docs/analysis/22-speech-act-pragmatic.md.
