Test task instructions by delegating execution to a contextless agent.
npx skills add https://github.com/nicsuzor/aops --skill dogfood-nicsuzor
Please help me install this Agent Skill. Skill: dogfood Source: https://github.com/nicsuzor/aops/tree/main/aops-claude/skills/dogfood Command: npx skills add https://github.com/nicsuzor/aops --skill dogfood-nicsuzor
Delegated instruction testing enables teams to assess whether a set of instructions can be executed by a contextless agent, capturing friction, iterating on guidance, and codifying learnings to improve future tasks.
Provide a contextless instruction that starts the dogfooding loop and records friction observed during execution.
High-intent search queries and answers about installing and using this skill.
Test task instructions by delegating execution to a contextless agent, observing the results, and identifying friction. This process requires explicit data sources, structured evaluation criteria, and defined output formats to ensure safe, reproducible outcomes.
Delegated instruction testing is a workflow QA method that assesses whether instructions can be executed by a contextless agent. It captures friction, iterates on guidance, and codifies learnings to improve future task execution.
Discover friction in agent instructions by commissioning contextless execution and observing where tasks fail. Gather evidence from these failures and iteratively refine the requirements to tighten guidance for future runs.
Yes, you need explicit data sources, structured evaluation criteria, and defined output formats to run instruction testing loops. These inputs ensure safe, reproducible results when delegating execution to a contextless agent.
Codify learnings from instruction evaluation by documenting best practices, metrics, and feedback loops across multi-phase testing cycles. This governance ensures repeatable quality and continuous improvement for delegated tasks.
A contextless agent fails to follow instructions when guidance lacks explicit data sources or defined output formats. Identify the exact friction points through observation, then refine the requirements to tighten the guidance.
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