omni

Compress verbose AI responses while preserving technical accuracy and code fidelity.

150|48|Updated Jan 24, 2026

Search Tags:#agent communication #token efficiency #concise responses #tool call formatting #prompt modifiers #safety pauses

Ready to run

npx skills add https://github.com/irahardianto/awesome-agv --skill omni-irahardianto

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: omni
Source: https://github.com/irahardianto/awesome-agv/tree/main/.agents/skills/omni
Command: npx skills add https://github.com/irahardianto/awesome-agv --skill omni-irahardianto

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Omni solves wasted tokens and verbose responses when you need communication that stays fully accurate but is optimized for tight budgets.

Core Features & Use Cases

Token-efficient, opt-in formatting: Compresses prose while leaving code blocks, tool calls, file paths, and data uncompressed.
Operational guardrails: Activates only when explicitly requested, used as a sub-agent in token-budget pipelines, or used via the omni headless modifier.
Reliability-focused structure: Enforces zero fluff/echoing, deterministic notation conventions, and clear safety pauses for destructive or ambiguous actions.
Agent-to-agent support: Provides /omni headless mode for raw, unstyled inter-agent communication.

Quick Start

Ask your agent for concise output by saying: “Use omni to answer briefly and precisely.”

Frequently Asked Questions about omni

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce token usage in AI responses without losing technical accuracy?▼

You can reduce token usage by applying opt-in formatting that compresses prose while strictly preserving code blocks, tool calls, file paths, and structured data. This ensures communication remains fully accurate but optimized for tight token budgets.

What is headless mode for agent-to-agent communication?▼

Headless mode for agent-to-agent communication provides a raw, unstyled output channel activated via a specific modifier. It facilitates deterministic, token-efficient data exchange between sub-agents in workflow pipelines.

How do I format AI outputs for token-budget pipelines?▼

Formatting AI outputs for token-budget pipelines involves using the Skill as a sub-agent to eliminate fluff and echoing. It enforces deterministic notation conventions and maintains zero compression for structured data to ensure pipeline reliability.

Does concise response compression affect code blocks and file paths?▼

Concise response compression does not affect code blocks and file paths. The system enforces strict non-compression for code, tool calls, file paths, URLs, and structured data to preserve full technical accuracy.

When should I use safety pauses for ambiguous actions in AI workflows?▼

You should use safety pauses for ambiguous actions when a workflow involves destructive steps or unclear operations. The system provides failsafe clarity suspension to prevent errors during these critical, high-risk execution phases.