Results for “diagnostic-interviews”

Plan customer discovery interviews with the right goal, segment, constraints, and method for problem validation, churn research, or new product ideas.

Plan customer discovery conversations with hypotheses, prompts, probes, and capture fields.

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

Save the current diagnosis state to disk for cross-session recall, including conclusions, rejected directions, and next steps.

Synthesize interviews into themes, contradictions, opportunities, and product implications.

Applies dialogue MA, portrait reframing, and loudness/sync QA to an interview or talking-head rough cut using canonical timeline metadata and a shared renderer.

Pattern-Based Diagnosis

Conduct structured, multi-session brand identity interviews to produce a master brandbook covering purpose, positioning, audience, personality, voice, narrative, and founder-brand tension.

Diagnoses whether a draft will resonate with its audience using a five-dimension communication psychology framework, identifies structural problems, and gives specific fixes.

Extracts the user's true intent through one-question-at-a-time interviews, surfacing hidden assumptions before any plan or code is written.

Investigates hard, flaky, or performance failures through a six-phase feedback-loop-first debugging process; use when quick targeted debugging is insufficient.

陈鲁豫式电视访谈结构：人生节点切片、短句接话与复述、轻质疑式推进、倾听后抛下一问； 访谈提纲与对话节奏参考，非恶搞梗滥用、非羞辱嘉宾。 触发：鲁豫有约、访谈节奏、真的吗（建设性质疑）、人生故事线等。

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

Restores the most recent diagnosis snapshot saved by dbs-save, allowing users to continue a previous diagnostic session.

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

Socratic deep interview with mathematical ambiguity gating before execution

针对 Modular RAG MCP Server 项目的模拟技术面试 Agent。读取用户简历（可选），围绕三个方向进行最多 3 轮深度追问，结束后生成并持久化面试报告（含参考答案、包装识别点评、评分）。Use when user says '模拟面试', '面试练习', '帮我面试', 'mock interview', 'interview practice', '面试', '考我', '开始面试', or wants to practice interviewing about this project.

Diagnoses business models using dontbesilent's ontological framework, offering consultation mode to dissolve problems and checkup mode to analyze business structures.

Runs a disciplined diagnosis loop for hard bugs and performance regressions, from reproduction through hypothesis testing, instrumentation, fixing, and regression testing.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Socratic deep interview with mathematical ambiguity gating before explicit execution approval

Agent D2 - Data Collection Specialist - Interviews, Focus Groups & Observation. Covers protocol development, question design, probing strategies, transcription conventions, and systematic observation. Absorbed D3 (Observation Protocol Designer) capabilities.

Merges multiple diagnosis snapshots from dbs-save into a single, timestamped Markdown report organized by topic.

Coaches sales teams on elite discovery methodology — question design, current-state mapping, gap quantification, and call structure that surfaces real buying motivation.

Interviews the user in depth about a plan, probing technical implementation, UI/UX, risks, tradeoffs, and missing decisions before writing or refining a spec.

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

Performs a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.

Conduct a structured interview about a feature or topic to gather requirements and produce a specification. This skill should be used when users want to think through a feature, clarify requirements, or create an implementation plan before coding.

Draft an internal legal research memo for the supervising attorney with verified authority and flagged open questions.

Runs a relentless interview that sharpens a plan or design and creates docs (ADRs and glossary) along the way. Use when the user wants to stress-test a plan or design, or produce decision records.

2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection

Runs a disciplined debugging loop for hard bugs and performance regressions, covering reproduction, hypothesis testing, instrumentation, fixing, and regression testing.

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

Perform a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.

User research specialist. Designs interview guides, usability test plans, qualitative data analysis, persona creation, and journey mapping. Complements Echo's UI validation. Use when user research design or analysis is needed.