Interview a user one question at a time to reach shared plan understanding.
npx skills add https://github.com/paialex/ai-skills-bank --skill grill-me-paialex
Please help me install this Agent Skill. Skill: grill-me Source: https://github.com/paialex/ai-skills-bank/tree/main/mattpocock_skills/productivity/grill-me Command: npx skills add https://github.com/paialex/ai-skills-bank --skill grill-me-paialex
Relentlessly interviews a user about a plan or design until a shared understanding is reached, surfacing ambiguities and dependencies.
Ask one targeted question about the plan and provide your recommended answer for that question.
High-intent search queries and answers about installing and using this skill.
Plan validation is achieved through a relentless interview process that asks one targeted question at a time. This incremental approach surfaces ambiguities and reconciles dependencies branch-by-branch until a shared understanding is reached.
Validating design decisions is best achieved by walking through each decision branch individually. By providing recommended answers for every critical inquiry, you ensure all design choices are fully vetted for clarity.
Yes, you can resolve dependencies across branches by conducting a focused interview. The process walks through each decision sequentially, ensuring that cross-branch dependencies are identified and reconciled in real-time.
To start a decision-tree interview, ask one targeted question about your plan and provide your recommended answer for that specific question. This keeps the discussion focused and incremental from the very beginning.
One-question-at-a-time prompting works effectively for complex plan validation by keeping the discussion focused. It walks through each decision branch sequentially to capture recommended answers and exhaust critical questions.
dachentAnalyze repository context to stress-test project plans through adversarial interviews.
t-i-0414Summarize project goals, constraints, and dependencies into a context document.
compozyInterview users through a branching decision tree to resolve design ambiguities.
LcpMarvelGenerate Feishu whiteboards from design briefs using Python and the Feishu API.
