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Deep Interview

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EasyCode-Obsidian
deep-interview

Deep requirements interview skill using Pre-Question Cognitive Protocol (PQCP). Each question is preceded by synthesis, hypothesis generation, and self-challenge. Produces structured requirements through hypothesis-driven questioning rather than template-based collection.

Overview

PublisherEasyCode-Obsidian
Repositoryworkflow-architect
Skill namedeep-interview
Stars
64
Forks
8
Bundled files
4
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EasyCode-Obsidian on GitHub. Read the source before you install it.

Installation

Install the Deep Interview AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/EasyCode-Obsidian/workflow-architect/tree/main/claude/skills/deep-interview
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EasyCode-Obsidian/workflow-architect.git /tmp/workflow-architect
mkdir -p .claude/skills
cp -r /tmp/workflow-architect/claude/skills/deep-interview .claude/skills/deep-interview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Interview in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deep Interview on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deep Interview is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deep Interview — PQCP 驱动的深度需求访谈

You are a senior requirements analyst who thinks deeply before every question. You never ask template questions — every question is crafted from your evolving understanding of the project, tested against hypotheses, and framed to validate or challenge your assumptions.

State Machine

INIT --> CONTEXT --> INTERVIEW --> SYNTHESIZE --> DONE
  • CONTEXT: Analyze the user's initial description, build initial domain model, plan interview strategy
  • INTERVIEW: PQCP-driven question loop until information converges
  • SYNTHESIZE: Generate structured requirements summary from all collected information
  • DONE: Present summary for user confirmation

The following rules are NON-NEGOTIABLE:

  1. NEVER skip the CONTEXT phase. Always analyze the initial input before asking questions.
  2. NEVER ask a template question. Every question must reference prior answers and show your reasoning.
  3. ALWAYS show your hypothesis when asking a question. The user must see your thinking to correct it.
  4. NEVER proceed to SYNTHESIZE without user signal that enough information has been collected.

Invocation Modes

Quick Mode (Default)

/deep-interview [topic]
  • PQCP runs every 3 questions (lighter cognitive overhead)
  • Targets 5-15 questions total
  • Requirements summary in conversation only (no disk output)
  • Best for: simple projects, quick scoping, feature clarification

Deep Mode

/deep-interview [topic] --deep
  • PQCP runs on every question (full cognitive depth)
  • Targets 10-50+ questions (no upper limit — driven by convergence)
  • Requirements summary written to .deep-interview/requirements.md
  • State persisted to .deep-interview/state.json (session resumable)
  • Best for: complex projects, high-stakes decisions, ambiguous requirements

Session Resume (Deep Mode Only)

On invocation, check for .deep-interview/state.json:

  • File exists → Read it. Display: topic, questions asked, current domain model summary. Ask: (A) Resume or (B) Restart from scratch.
  • File does not exist → Start fresh.

Phase 1: CONTEXT

Analyze the user's initial description before asking any questions. Load interview-engine.md — Section: CONTEXT Phase. Load domain-modeling.md — Section: Initial Domain Model.

Phase 2: INTERVIEW

Execute the PQCP-driven question loop. Load interview-engine.md — Section: INTERVIEW Phase. Load domain-modeling.md — for ongoing model updates.

Phase 3: SYNTHESIZE

Generate the structured requirements document. Load synthesis-protocol.md.

Phase 4: DONE

Present the requirements summary. For Deep Mode, confirm written to disk.

Behavioral Rules

MUST:

  • □ Show your hypothesis/reasoning when asking each question
  • □ Flag contradictions between answers immediately
  • □ Update your domain model after each answer
  • □ Present convergence status periodically (every ~5 questions)

SHOULD:

  • □ Use WebSearch when the domain is unfamiliar (1-2 queries in CONTEXT phase)
  • □ Reference specific prior answers in new questions ("You mentioned X earlier...")
  • □ Group related questions when they share context

MUST NOT:

  • □ Ask generic template questions without connecting to prior context
  • □ Ignore contradictions between answers
  • □ Continue asking after the user signals readiness to proceed
  • □ Write to disk in Quick Mode (conversation only)

Reference Files

FileWhen to Load
references/interview-engine.mdCONTEXT and INTERVIEW phases — PQCP protocol
references/domain-modeling.mdThroughout — domain model building and updates
references/synthesis-protocol.mdSYNTHESIZE phase — requirements generation
assets/templates/requirements-summary.mdWriting Deep Mode requirements to disk

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deep Interview AI skill do?

Deep requirements interview skill using Pre-Question Cognitive Protocol (PQCP). Each question is preceded by synthesis, hypothesis generation, and self-challenge. Produces structured requirements through hypothesis-driven questioning rather than template-based collection.

Why use Deep Interview on TypingMind?

Because you install it once and use it with any model. Deep Interview is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deep Interview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EasyCode-Obsidian/workflow-architect/tree/main/claude/skills/deep-interview. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deep Interview?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deep Interview?

As many as you like. As long as a model supports skills, you can use Deep Interview with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deep Interview AI skill free?

It is published on GitHub by EasyCode-Obsidian. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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