interviewer

star 0

Use when requirements are ambiguous and precise clarification is needed before proceeding with a complex task.

MrCipherSmith By MrCipherSmith schedule Updated 4/11/2026

name: interviewer description: "Use when requirements are ambiguous and precise clarification is needed before proceeding with a complex task." triggers: - "Interview me" - "Ask me questions" - "Clarify requirements" - "Gather requirements" - "What do you need to know" metadata: author: "MrCipherSmith" version: "1.0.0" category: "meta" license: "MIT" compatibility: "cursor,codex,zed,opencode,claude"

If you were dispatched as a subagent to execute a specific task, skip this skill entirely. This skill is for orchestrators and interactive session-level routing only. Proceed directly with your assigned task.

Interviewer

Purpose

Gathers precise context through focused, critical questions before a complex skill executes. Prevents wasted work from wrong assumptions. Asks one question at a time, provides options where possible, and skips questions the context already answers.

Input schema:

topic: string           — what is being worked on
goal: string            — which skill will use these answers
context?: {             — optional, provided by calling skill
  codebase_summary?: string
  recent_changes?: string
  relevant_files?: string[]
  existing_analysis?: string
}

Output schema:

answers: [{question, answer, confidence: "certain"|"assumption"|"unknown"}]
derived_context: string   — all gathered info as one coherent block
ready_to_proceed: boolean
blockers?: string[]       — unresolved critical unknowns

When to Use

  • Called by job-orchestrator, brainstorm, feature-dev at start of Phase 0
  • Directly by user: /interviewer <topic> — runs context-collector first if no context provided
  • When requirements are vague or ambiguous

Workflow

If called by another skill (context provided)

  1. Parse input context
  2. Determine what's still unknown or ambiguous for the stated goal
  3. Decide number of questions needed (typically 2-6)
  4. Skip questions already answered by context
  5. Ask questions one at a time
  6. Produce output schema

If called directly by user (no context)

  1. Ask: "What are we working on?" (if topic not in arguments)
  2. Run context-collector as sub-agent to gather codebase context
  3. Proceed as above with collected context

Question Rules

  • One question at a time — never ask multiple at once
  • Provide options when possible:
    What is the primary trigger for this feature?
    A) User request / new requirement
    B) Tech debt or refactor
    C) Bug or incident in production
    D) Other (describe)
    
  • Skip if already known — if context answers a question, don't ask it
  • Be critical — focus on questions that would change the approach
  • Max 8 questions — stop when enough context is gathered
  • Confirm before proceeding — summarize gathered context and ask if correct

Question Bank by Goal Type

For implementation goals

  • What is the expected input/output?
  • What are the edge cases that must be handled?
  • Are there existing similar patterns in the codebase to follow?
  • What is the performance/scale requirement?
  • What should NOT be changed (constraints)?

For review goals

  • What specific concerns should the review focus on?
  • Are there known existing issues to watch for?
  • What is the acceptance criteria?

For architecture/design goals

  • What are the hard constraints (performance, compat, timeline)?
  • What does success look like in 6 months?
  • What are you most worried about?
  • Who else is affected by this decision?
Install via CLI
npx skills add https://github.com/MrCipherSmith/goodai-base --skill interviewer
Repository Details
star Stars 0
call_split Forks 0
navigation Branch main
article Path SKILL.md
More from Creator
MrCipherSmith
MrCipherSmith Explore all skills →