Coordinate multi-agent campaign lifecycles with contract-first planning and evidence-backed feedback.
npx skills add https://github.com/espensev/ai-skills --skill loop-master
Please help me install this Agent Skill. Skill: loop-master Source: https://github.com/espensev/ai-skills/tree/main/gemini-skills/skills/loop-master Command: npx skills add https://github.com/espensev/ai-skills --skill loop-master
The Loop Master orchestrates high-level campaign lifecycles and continuous verification across multiple agents, preventing coordination gaps and drift from guardrails.
Tell the Loop Master to initiate a new campaign round with your goals, constraints, and expected outcomes.
High-intent search queries and answers about installing and using this skill.
Multi-agent campaigns are orchestrated through centralized governance, enforcing contract-first execution, continuous verification, and scoped writes to prevent coordination gaps and drift from guardrails across all participating agents.
A planning contract is a 13-element framework that enforces contract-first execution for campaign orchestration. It structures multi-agent planning rounds by requiring evidence-backed decisions, explicit citations, and guardrail enforcement to maintain traceable governance.
To start a new campaign round, initiate the process by providing your campaign goals, constraints, and expected outcomes. The system then orchestrates multi-agent planning rounds, applying centralized governance, continuous verification, and guardrails to ensure traceable decisions.
Evidence-backed feedback in planning rounds requires explicit citations for all decisions. During multi-agent orchestration, this mechanism enforces guardrails and ensures continuous verification by validating that every round feedback iteration is supported by traceable evidence.
Continuous verification is needed for complex campaigns when coordinating multiple agents to prevent coordination gaps and drift from guardrails. It maintains consistency through status reporting, scoped writes, and iterative feedback cycles throughout the campaign lifecycle.
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nexus-labs-automationInstrument AI agent inputs and outputs with safety and policy guardrails.
