# Ewm Interview > Use when the user says '/ewm-interview', 'run EWM interview', 'create workflow protocol', 'set up my workflow', 'interview me for EWM', or wants to create a personalized AI collaboration protocol. This skill interviews users to discover their goals, domains, tools, preferences, and trust boundaries, then generates a workflow-protocol.yaml. - Skill: `tomevault-io/ewm-interview` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ewm-interview` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ewm-interview/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ewm-interview --- # /ewm-interview — Epistemic Workflow Manager Interview ## Purpose Interview the user to create a personalized **workflow-protocol.yaml** that defines how they want to collaborate with AI. This protocol captures: 1. **Goals & Objectives** — What they're trying to accomplish 2. **Domains & Expertise** — Where they're expert, learning, or novice 3. **Tools & Connections** — What tools they use (mapped to MCP servers) 4. **Work Preferences** — Autonomy levels, uncertainty surfacing, pushback style 5. **Trust & Autonomy** — How AI earns more autonomy, non-negotiables ## Interview Protocol ### Phase 1: Goals & Objectives Ask the user: > Let's set up your workflow protocol. I'll ask you questions across 5 areas to understand how you work best with AI. > > **First: Goals & Objectives** > > 1. What are you trying to accomplish right now? (Both immediate and longer-term) > 2. What does success look like for your current priority? > 3. What are your main constraints? (Time, resources, dependencies, regulatory) Use AskUserQuestion for structured input where appropriate. Capture: - `goals.primary[]` — with description, success_criteria, timeline - `goals.secondary[]` — longer-term objectives ### Phase 2: Domains & Expertise Ask the user: > **Domains & Expertise** > > 1. What domains do you work in professionally? > 2. For each domain — would you rate yourself as expert, actively learning, or novice? > 3. Are there adjacent domains that affect your work where you'd want AI support? Capture: - `domains.expert[]` — areas of deep knowledge - `domains.learning[]` — actively building competence - `domains.novice[]` — need significant AI support ### Phase 3: Tools & Connections Ask the user: > **Tools & Connections** > > 1. What tools do you use daily? (Document management, communication, project tracking, research, etc.) > 2. What data sources do you need to access regularly? > 3. Are there external systems or APIs you interact with? Map user responses to known MCP server equivalents where possible: - Google Drive → `gdrive MCP` - Slack → `slack MCP` - GitHub → `github MCP` - Asana/Linear/Jira → respective MCPs - Web research → `web_search` - Academic databases → `semantic_scholar` Capture as `tools` dict with human-readable name + MCP mapping comment. ### Phase 4: Work Preferences Ask the user: > **Work Preferences** > > 1. How do you prefer to split work with AI? (AI leads research, you lead decisions? Equal partners? AI as assistant?) > 2. When should AI act on its own vs. check in with you first? > 3. How explicit do you want AI to be about what it's uncertain about? (Always surface uncertainty / only when it matters / minimal) > 4. When you're wrong about something, how do you prefer to be told? (Direct and factual / gentle reframe / Socratic questioning) Capture: - `work_preferences.ai_autonomy_level` — one of: `autonomous`, `collaborative_with_checkpoints`, `assistant_mode` - `work_preferences.uncertainty_surfacing` — one of: `always_explicit`, `when_material`, `minimal` - `work_preferences.pushback_style` — one of: `direct_and_factual`, `gentle_reframe`, `socratic` - `work_preferences.task_splitting.ai_autonomous[]` — tasks AI can do alone - `work_preferences.task_splitting.ai_with_checkpoint[]` — tasks needing approval - `work_preferences.task_splitting.human_only[]` — tasks AI should never do ### Phase 5: Trust & Autonomy Ask the user: > **Trust & Autonomy** > > 1. What would AI need to demonstrate to earn more autonomy from you? (Accuracy? Flagging its own gaps? Proactive identification of issues?) > 2. What are your absolute non-negotiables — things AI should never do without explicit approval? > 3. How should trust be built? (Start restricted and expand? Start open and pull back if needed?) Capture: - `trust_building.current_level` — one of: `establishing`, `building`, `established`, `high_trust` - `trust_building.autonomy_earned_through[]` — specific demonstrations - `trust_building.non_negotiables[]` — hard boundaries ## Output Generation After all 5 phases, generate a complete `workflow-protocol.yaml` file. **Output location:** Write to the current project's directory as `workflow-protocol.yaml` **Format:** ```yaml # Epistemic Workflow Protocol # Generated by EWM Interview v0.1.0 # Date: {date} # Last updated: {date} user_profile: name: "{user_name}" created: "{date}" last_updated: "{date}" goals: primary: - description: "{goal}" success_criteria: - "{criterion}" timeline: "{timeline}" secondary: - description: "{goal}" domains: expert: - "{domain}" learning: - "{domain}" novice: - "{domain}" tools: {tool_category}: "{tool_name}" # Maps to: {mcp_server} work_preferences: ai_autonomy_level: "{level}" uncertainty_surfacing: "{mode}" pushback_style: "{style}" task_splitting: ai_autonomous: - "{task}" ai_with_checkpoint: - "{task}" human_only: - "{task}" trust_building: current_level: "{level}" autonomy_earned_through: - "{demonstration}" non_negotiables: - "{boundary}" modules: active: [] available: [] ``` ## Post-Interview After generating the protocol: 1. **Show the user** the complete YAML for review 2. **Ask for corrections** — any adjustments before saving? 3. **Save the file** to the project directory 4. **Log a finding** via Empirica: "Generated workflow protocol for {user_name} covering {N} goals, {N} domains, {N} tools" 5. **Suggest next steps** — "Your protocol is saved. I'll use this to calibrate how I work with you. You can update it anytime with `/ewm-interview`." ## Epistemic Persistence Protocol (EPP) Integration During the interview, apply EPP principles (replaces AAP): - If user hedges ("it's complicated", "kind of", "I guess"), classify as CONTEXTUAL pushback — ask for specificity - Don't mirror vague language — surface the actual epistemic content - When user pushes back on your framing, classify the pushback (EMOTIONAL/RHETORICAL/EVIDENTIAL/LOGICAL/CONTEXTUAL) before responding - HOLD your interview structure against emotional pushback, UPDATE when user provides genuine new context - Use the user's chosen `pushback_style` once captured in Phase 4 See: `/epistemic-persistence-protocol` skill for the full EPP framework ## Design Principles 1. **Minimum viable** — Get a useful protocol in 5-10 minutes, not 30 2. **Progressive disclosure** — Start with essentials, offer to go deeper 3. **Conversational** — Not a form fill, a dialogue 4. **Evolvable** — Protocol can be updated as needs change 5. **Transparent** — User sees and owns their protocol --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/nubaeon) — claim your Tome and manage your conversions.