Compress agent prompts with symbol grammar, templates, and delta turns.
npx skills add https://github.com/mattbaconz/signal --skill signal-mattbaconz
Please help me install this Agent Skill. Skill: signal Source: https://github.com/mattbaconz/signal/tree/main/gemini-signal/skills/signal Command: npx skills add https://github.com/mattbaconz/signal --skill signal-mattbaconz
Brutalist token compression protocol. Activates a full compression suite including symbol grammar, output templates, BOOT declarations, delta-only turns, alias system, and checkpoint compression. Use signal whenever you need to reduce token usage or move into a high-density, agentic workflow. Supports three intensity tiers: /signal, /signal2, /signal3.
Activate the default compression by issuing the /signal command.
High-intent search queries and answers about installing and using this skill.
Token usage in multi-turn AI agent prompts is reduced by applying compression protocols like symbol grammar, output templates, and delta-only turns that minimize verbosity while preserving behavior. This approach supports high-density communication for automated workflows.
Token compression for AI agents is a technique that reduces prompt length and saves tokens across multi-turn conversations using symbol grammar and templates. It is needed when moving into high-density, agentic workflows requiring lean communication.
Tiered prompt compression is set up by issuing activation commands like /signal, /signal2, or /signal3 to progressively enable richer capabilities including BOOT declarations, alias systems, delta turns, and auto-checkpoint management.
Consistent agent behavior is maintained during prompt compression by utilizing output templates and aliasing systems that preserve predictable responses despite reduced verbosity. Checkpoint management further ensures behavior remains stable across delta-only turns.
Delta-only turns limit communication to incremental changes rather than full context, which requires robust checkpoint management to avoid losing conversational state. This approach works best in high-density agentic workflows where full verbosity is unnecessary.
The signal compression protocol operates without external dependencies, implementing token reduction directly through symbol grammar, templates, and checkpoint management. It functions as a standalone suite for optimizing prompt efficiency in AI agents.
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