AgentSkillsCN

research-interview

通过认知追踪,快速开启结构化知识挖掘的入口。主动启用以下场景:(1) 进行访谈与需求收集;(2) 定义问题;(3) 捕获领域知识;(4) 启发利益相关者;(5) 揭示假设。触发指令包括:“研究访谈”“挖掘知识”“收集需求”“定义问题”“访谈利益相关者”“捕获领域知识”。

SKILL.md
--- frontmatter
name: research-interview
description: >
  Quick entry point for structured knowledge elicitation with epistemic tracking.
  PROACTIVELY activate for: (1) interviews and requirements gathering, (2) problem definition,
  (3) domain knowledge capture, (4) stakeholder elicitation, (5) assumptions surfacing.
  Triggers: "research interview", "elicit knowledge", "gather requirements", "define problem",
  "interview stakeholder", "capture domain knowledge"
argument-hint: [topic]

Research Interview

Structured knowledge elicitation with epistemic tracking for problem definition and requirements gathering.

When to Use

Use this skill when you need to:

  • Elicit knowledge from stakeholders or domain experts
  • Define a problem space systematically
  • Gather requirements with confidence tracking
  • Surface hidden assumptions and constraints
  • Capture domain knowledge for later research

Workflow

Invoke the research-interviewer skill for: "$ARGUMENTS"

Default Parameters

ParameterValueRationale
output_formatPROBLEM-STATEMENTDefault; produces actionable problem definition
confidence_threshold0.85High confidence required for conclusions
validation_modebalancedMix of challenging and supportive questioning
epistemic_trackingenabledTrack certainty levels throughout

Output Formats

Select based on your goal:

  • PROBLEM-STATEMENT - Clear problem definition with constraints and success criteria
  • REQUIREMENTS - Structured requirements with priority and confidence levels
  • KNOWLEDGE-CORPUS - Domain knowledge capture for research continuation

Interview Techniques Applied

  1. Open-ended exploration - Surface the problem space
  2. Assumption challenging - Identify hidden constraints
  3. Edge case probing - Find boundary conditions
  4. Confidence calibration - Assess certainty levels
  5. Gap identification - Find missing information

Epistemic Labels

All findings are tagged with confidence:

  • VERIFIED - Confirmed by multiple sources or evidence
  • STATED - Reported but not independently verified
  • INFERRED - Derived from other information
  • SPECULATIVE - Hypothesis requiring validation
  • UNKNOWN - Explicit knowledge gap

Output Format

The research interview produces structured output:

xml
<interview-output>
  <header>
    <id>[unique identifier]</id>
    <topic>$ARGUMENTS</topic>
    <format>[PROBLEM-STATEMENT|REQUIREMENTS|KNOWLEDGE-CORPUS]</format>
  </header>

  <findings>
    <finding confidence="[0.0-1.0]" epistemic="[VERIFIED|STATED|INFERRED|SPECULATIVE]">
      [Key finding or requirement]
    </finding>
    <!-- ... more findings ... -->
  </findings>

  <assumptions>
    <assumption status="[confirmed|challenged|unknown]">
      [Assumption that was surfaced]
    </assumption>
  </assumptions>

  <gaps>
    <gap priority="[high|medium|low]">
      [Information still needed]
    </gap>
  </gaps>

  <next-steps>
    1. [Recommended follow-up action]
    2. [Additional research needed]
  </next-steps>
</interview-output>

Quality Gates

  • Topic is clearly defined and scoped
  • Key assumptions have been surfaced and examined
  • Confidence levels assigned to all findings
  • Information gaps explicitly identified
  • Next steps actionable and specific
  • Output ready for research-brief input

Workflow Integration

This skill is the first step in the research pipeline:

code
/research-interview → /research-brief → /consolidate-research
     (elicit)            (design)           (synthesize)

After completing a research interview, consider:

  • Run /research-brief with the problem statement to design multi-LLM research
  • Run /evaluate-schema if the topic involves data modeling
  • Run /compare-options if multiple solutions were identified