process-mapper（流程映射器）

Process Mapper Skill

Systematic workflow for discovering, documenting, and analyzing processes. Implements user's SOP-first doctrine: "You can't automate what you can't see."

Core Philosophy

From user's work:

"When I sit with a person or team to start working through how they can work out where and how to apply AI into their job, I often like to start with a common task or value stream, and talk through—or more often than not, document—the SOP for that value stream."

Three truths:

1. Shadow processes are real processes (what actually happens ≠ org chart)
2. Tacit knowledge is documentable (capture decision *points*, not decision *logic*)
3. Structure enables automation (visibility → AI opportunities)

Core Workflow

1. Diagnostic: Assess Current State

Determine SOP state:

Load references/discovery-methodology.md for framework

State 1: Fiction

• SOPs exist but nobody follows them
• Beautiful docs, zero usage
• Aspirational not actual

State 2: Nonexistent

• No documentation
• Tribal knowledge
• "Just ask Sarah"

State 3: Accurate

• Docs match reality
• Referenced regularly
• Updated when process changes

Action by state:

• Fiction → Archive and start fresh
• Nonexistent → Begin discovery (most common)
• Accurate → Use for automation analysis

2. Process Discovery Interview

If starting from State 2 (Nonexistent), conduct discovery:

Setup:

• Identify process owner (person who actually does this)
• Secure 45-90 minutes uninterrupted
• Frame: "Show me what actually happens, not what should happen"
• Get screen access to tools they use

Five-round interview sequence:

Load references/discovery-methodology.md for detailed questions. Brief framework:

Round 1: High-Level Flow - Get end-to-end sequence (5-10 major steps, trigger, endpoint, duration)

Round 2: Step Decomposition - Break into substeps (inputs, tools, transformations). Look for copy-paste, manual entry, system switching.

Round 3: Decision Points - Identify where judgment is required. Distinguish explicit rules from tacit judgment (labeled black box pattern).

Round 4: Edge Cases & Exceptions - Understand failure modes, workarounds, frequency. High exceptions = process might be wrong.

Round 5: Context Dependencies - Identify tacit knowledge (domain knowledge, institutional knowledge, relationships). Reveals automation tractability.

3. Process Documentation

Choose format based on process characteristics:

Format 1: Linear SOP (≤10 steps, minimal branching)

• Sequential steps with actions/tools/inputs
• Quality checks
• Common issues

Format 2: Decision Tree (multiple paths, branching logic)

• Entry conditions
• Path A/B/C with criteria
• Decision matrix

Format 3: Swimlane (multi-role, handoffs important)

• Who does what when
• Handoff points
• Role responsibilities

Format 4: Visual Diagram (complex flows)

• Mermaid flowchart
• System integrations
• Exception paths

Load assets/visual-templates.md for specific templates

Documentation principles:

• Capture actual current state (not aspirational)
• Mark tacit knowledge points with ⚡
• Note context dependencies with 🧠
• Flag frequent failures with ⚠️
• Include frequency/volume data
• Validate with process owner

4. Complexity Classification

Map each process step to Tractability Grid (9 zones):

Load references/automation-framework.md for full framework and detailed assessment criteria.

Two dimensions:

1. Context Dependence: Low (algorithmic, no expertise) → High (tacit judgment, relationships)
2. Task Complexity: Simple (≤5 steps, single system) → Complex (15+ steps, multiple systems)

Assessment questions: Could intern do this with instructions? How many steps/systems/decisions?

5. Automation Opportunity Analysis

Plot each step on 9-zone grid (see references/automation-framework.md for visual):

• Zones 1-2 (Green): High automation (85-75% success) - RPA, quick wins
• Zones 3-5 (Yellow): Medium (60-40%) - AI copilots, human-in-loop
• Zones 6-9 (Red): Low (<25%) - Avoid core automation, support tasks only

6. Prioritization & ROI

Priority quadrants (Pain × Feasibility):

• P1 - Quick Wins: High pain, easy (Zones 1-2) - Do immediately
• P2 - Strategic: High pain, hard (Zones 3-5) - Worth investment
• P3 - Efficiency: Low pain, easy - Do when capacity available
• P4 - Avoid: Low pain, hard (Zones 6-9) - Not worth effort

ROI: Payback Period = Cost / (Hours Saved × Rate + Error Reduction)

7. Output Delivery

Standard deliverables: Process Map (visual), SOP Document (written), Automation Analysis (zone classifications, priorities, ROI), Implementation Roadmap (phased plan)

Optional: Interview transcript, validation notes, comparative analysis, metrics dashboard

The Labeled Black Box Pattern

Critical technique: Document THAT a decision exists, not HOW it's made (when tacit).

Load references/documentation-patterns.md for detailed examples and template.

Core principle: Name decision points even when logic is tacit. Enables process visibility, appropriate handoffs, training focus, and future automation planning.

Movement Strategy

Key insight: Can't automate high-context zones directly. Build infrastructure to move problems to lower zones.

Load references/documentation-patterns.md for case study (Air India: Zone 8 → Zone 2, 97% accuracy).

Infrastructure path: Zone 8→5 (frameworks), Zone 5→2 (explicit logic), Zone 2→1 (eliminate manual steps)

Quality Signals

Good process map has:

• [ ] Actual current state (not aspirational)
• [ ] All decision points identified
• [ ] Tacit knowledge points marked (⚡)
• [ ] Context dependencies noted (🧠)
• [ ] Exception paths included
• [ ] Validated by process owner
• [ ] Frequency/volume data
• [ ] Pain points documented
• [ ] Clear start and end
• [ ] Realistic time estimates

Red flags:

• Too neat (probably fictional)
• No exceptions (incomplete)
• No pain points (not real discovery)
• No tacit knowledge points (missed shadow process)
• Can't estimate frequency (no data)
• Process owner says "that's not quite right"

Integration Points

With concept-forge:

• Test automation hypotheses dialectically
• Challenge zone classifications
• Refine through multiple perspectives

With strategy-to-artifact:

• Process map → Presentation deck
• Automation roadmap → Executive one-pager
• Business case → Slide deck

With research-to-essay:

• Process patterns → Substack post on SOP doctrine
• Case studies → Long-form analysis

With user's voice (from research-to-essay):

• Use dialogue structure in documentation
• Employ concrete examples (Air India vs Air Canada)
• Include practitioner stance ("In my experience...")
• Show recursive refinement ("Let me be more precise...")

Common Process Types

Approval Workflows: Zones 1-2 (rule-based) or 4-5 (judgment). High automation potential for rules.

Data Processing: Zones 1-3. Very high automation if algorithmic.

Customer Service: Zones 4-8. Medium automation (copilot model).

Reporting: Zones 1-3 (structured) or 5-7 (insights). High for gathering, medium for analysis.

Coordination: Zones 4-9 (relationship-dependent). Low for core, high for supporting tasks.

Anti-Patterns

Don't:

• Map aspirational process (document what actually happens)
• Skip validation with process owner (will be wrong)
• Try to capture tacit HOW (use labeled black box)
• Force Zone 8-9 into automation (will fail)
• Ignore shadow processes (they're the real process)
• Over-document (keep it actionable)
• Create one-time map (processes evolve, keep updated)

Do:

• Start with actual current state
• Validate iteratively
• Document decision points even when logic is tacit
• Acknowledge complexity honestly
• Focus on high-ROI opportunities
• Build movement infrastructure
• Update as process changes

Success Metrics

Process mapping succeeds when:

• Process owner says "Yes, that's exactly what we do"
• New team members can follow documented process
• Automation opportunities clearly identified
• ROI estimates are validated
• Quick wins deliver promised value
• Documentation is referenced regularly (not ignored)

Automation analysis succeeds when:

• Zone classifications match reality
• Prioritization aligns with business value
• Implementation follows plan
• Expected savings materialize
• User adoption high (not forced)

Example Triggers

• "Map our customer onboarding process"
• "Document how we handle support tickets"
• "Where can we apply AI to our workflow?"
• "Create SOP for expense approval"
• "Show me where automation makes sense"
• "Why does this process keep breaking?"
• "Help me understand what my team actually does"
• "Walk me through your typical day"