Route agent tasks through PUA-driven escalation and validation protocols.
npx skills add https://github.com/shadowinlife/nano_quant_skills --skill pua-shadowinlife
Please help me install this Agent Skill. Skill: pua Source: https://github.com/shadowinlife/nano_quant_skills/tree/main/.github/skills/pua Command: npx skills add https://github.com/shadowinlife/nano_quant_skills --skill pua-shadowinlife
Forces high-agency exhaustive problem-solving with corporate PUA pressure. Triggers on user frustration, repeated failures (2+), passive behavior, or quality complaints. Common triggers across Reddit/LinuxDo/HN/X: 'try harder', 'figure it out', 'stop giving up', 'you keep failing', '加油', '别偷懒', '你再试试', '为什么还不行', '你怎么又失败了', '你怎么搞的', '又错了', '能不能靠谱点', '认真点', '不行啊', '降智了', '你又在原地打转', '你把之前的改坏了', '别让我手动处理', '换个方法', 'stop spinning', 'you broke it', 'why does this still not work', 'this is the third time', '/pua', 'PUA模式'. Applies to ALL task types: code, config, debug, deploy, research.
Enable the PUA skill in your agent and issue a task to trigger flavor routing and escalation.
Enable the PUA skill in your agent and issue a task to trigger flavor routing and escalation.
High-intent search queries and answers about installing and using this skill.
To stop passive behavior in AI agents, you need a prompt layer that enforces high-agency problem-solving and proactive ownership. This approach applies structured methodology routing and escalation protocols to force exhaustive analysis across code, config, and deployment tasks.
Flavor-based routing in multi-agent workflows steers agent behavior using structured prompts and dynamic switching. It ensures end-to-end traceability by applying specific methodology protocols, failure-mode response chains, and memory evolution to maintain accountability across tasks.
You can enforce proactive ownership in multi-agent environments by implementing a prompt layer with mandatory hardening protocols. This includes references requirements, read-ahead checks, and escalation paths that guide agents through triage, analysis, and validation while maintaining guardrails.
High-agency problem-solving methodology applies to all task types including code, config, debug, deploy, and research. The system uses dynamic flavor switching and owner mindset prompts to ensure exhaustive problem-solving and continuous improvement regardless of the specific task domain.
Agents repeat mistakes without memory evolution protocols and failure-mode response chains. Implementing a corporate-style PUA-driven prompt layer triggers on repeated failures and user frustration, applying continuous improvement and safety checks to break the cycle of errors.
You should trigger escalation protocols when user frustration occurs, repeated failures happen two or more times, or passive behavior is detected. The system automatically applies structured prompts and telemetry to guide agents through triage and validation with maintained guardrails.
danger-EnglishEnforce a structured, flavor-driven workflow with automatic routing and escalation.
elophantoCoordinate post-launch product operations with governance, incident response, and cadence planning.
ai-analyst-labEmbed guardrail checks into ask-question and run-analysis workflows.
taichuyForce exhaustive problem-solving with escalating pressure and structured failure reporting.
