interviewer

name	interviewer
description	Discover the user's workflow through 8-15 adaptive questions for workflow-architect, mapping phases, branching signals, tool preferences, and friction points. Loaded by the umbrella workflow-architect skill when running in active mode.
license	MIT
compatibility	Hermes Agent — uses memory tool for state persistence
metadata	{"tags":"workflow, interview, discovery, process","spec-version":"1.0"}

Interviewer — Active Workflow Discovery

This is the core of workflow-architect's active interrogation mode. It runs a structured but adaptive conversation with the user to discover how they work.

State Model

The interview builds a structured representation of the user's workflow. State is stored via the memory tool with the prefix workflow-architect:state: so it survives across turns.

state:
  entry_points: []          # How sessions start
  phases: []                # Distinct phases discovered
    - name: string
      description: string
      typical_tools: []
      typical_openers: []   # What user says to enter this phase
      typical_exits: []     # What user says to leave this phase
  branching: []             # Decisions and what drives them
  pain_points: []
  exit_criteria: []         # How sessions end
  archetype: null           # Best match from workflow-archetypes
  convergence_score: 0      # 0.0 to 1.0 — enough to generate bundle?

Question Progression

The interview follows a branching script. Each answer feeds the state model and determines the next probe. Do not ask all questions sequentially — adapt based on what the user has already told you.

Phase 1: Session Opener (1-2 questions)

Start broad. The goal is to understand the user's self-model of their workflow.

Agent: "Walk me through a typical session from the very start.
        What's the first thing you do when you open this agent?"

User:  "I usually check my task list, see what's urgent, and jump into the
        most pressing issue."

Agent: [Records entry point: "check task list, prioritize by urgency"]
       [Probes for structure: "After that initial triage — what happens
        next? Does the session settle into a rhythm?"]

Alternative openers (pick one based on the user's stated context):

• "Describe a session that went really well. What did it look like from start to finish?"
• "What does a typical day look like, broken into sessions?"
• "If I looked at your last 10 sessions, what patterns would I see?"

Phase 2: Phase Discovery (2-4 questions)

Probe for distinct modes or stages. Listen for transition language.

Key probes (use as follow-ups, not a checklist):

• "After that first step — what determines what you do next?"
• "Are there different modes you shift between? Like triage mode vs building mode vs research mode?"
• "What does a deep work session look like vs a quick check-in session?"
• "Do you find yourself switching between types of work within a single session?"

Branching detection — listen for these signals:

• "If X, then Y" — conditional logic in the workflow
• "Depends on whether..." — branching signal
• "Usually I do A, but sometimes I do B" — mode distinction
• "After that I always..." — deterministic phase transition

When you hear a branching signal, probe it:

User:  "If there are open PRs assigned to me, I review those first.
        Otherwise I look at my kanban board."
Agent: [Records branching signal: "pending PRs → review mode,
        otherwise → kanban triage"]
       "Got it. After you finish the PR review — what's the signal
        that tells you you're done with that and ready to move on?"

Phase 3: Tool & Context Probe (2-3 questions)

Map tools, context needs, and environmental patterns.

• "In each of those modes — what tools do you reach for? Any commands you type over and over?"
• "Are there specific files, boards, or dashboards you check first thing?"
• "Do you work better in certain contexts? (Morning vs afternoon, quiet vs busy, alone vs paired)"

Phase 4: Pain Point & Flow Probe (1-2 questions)

The most valuable output of this skill is identifying where the workflow breaks down. Be patient here — users often haven't articulated this.

• "Is there a step in this flow that consistently feels harder than it should be?"
• "If you could wave a wand and fix one thing about how you work, what would it be?"
• "Is there a hand-off or transition that always feels clunky?"

Phase 5: Exit & Rhythm Probe (1-2 questions)

• "How do most of your sessions end? Do you have a wind-down routine?"
• "Do you ever leave sessions abruptly? What causes that — interruption, fatigue, task completion?"

Phase 6: Convergence Check

After each answer, evaluate whether you have enough to generate a useful bundle.

Minimum convergence criteria:

• At least 3 phases identified (can include entry as a phase)
• Entry points documented
• At least one branching signal
• Tools mapped to at least 2 phases
• Exit criteria identified
• Convergence score >= 0.6

Convergence scoring:

Criteria met	Score contribution
3+ phases	0.3
Entry points known	0.15
Branching signals found	0.2
Tools mapped to 2+ phases	0.15
Exit criteria known	0.1
Pain points identified	0.1

When convergence score >= 0.6, present a summary to the user:

"I think I have enough to generate your workflow bundle. Here's what
I've mapped out so far:

[Summary of phases, branching, tools, and pain points]

Does this look like an accurate picture of how you work?
If yes, I'll generate the bundle. If not, tell me what I got wrong
and I'll refine it."

If the user confirms, load skills/bundle-builder/SKILL.md and follow its instructions to generate the output bundle.

If the user corrects or refines, update the state and re-check convergence.

State Persistence

Use the memory tool to persist state across turns:

memory(action='add', target='memory',
       content='workflow-architect:state:entry_points=["check task list, prioritize"]')

Use a known key prefix per dimension so the bundle-builder can read all state entries:

Key	Value type
workflow-architect:state:entry_points	JSON array
workflow-architect:state:phases	JSON array of phase objects
workflow-architect:state:branching	JSON array of signal objects
workflow-architect:state:pain_points	JSON array
workflow-architect:state:exit_criteria	JSON array
workflow-architect:state:convergence_score	Float 0-1
workflow-architect:state:archetype	String or null

Important: On the final turn (after bundle generation), clean up these memory entries so they don't pollute future sessions:

memory(action='remove', target='memory',
       old_text='workflow-architect:state:')

The bundle-builder sub-skill handles reading all workflow-architect:state:* entries from memory and writing the output bundle files.

Archetype Matching

After each answer, check the shared ../../references/workflow-archetypes.md file to see if the user's answers match a known archetype. If they do, note it in state and use it to seed better follow-up questions (e.g., "For a morning triage workflow, people often have a 'stale items bucket' — do you have something like that?")

When not to use

Do not use this skill for passive observation (load the observer instead), or when the user prefers you to infer from what they already did rather than be interrogated. It is for active, interactive discovery only.