Structured Problem Diagnosis
*Problem Track — Step 1 of 3. Defines the problem with numbers and decomposes it into testable parts.*
Inputs Required
- A problem the user wants diagnosed (metric decline, performance gap, strategic question)
Output
.agents/mkt/diagnosis.md
Quality Gate
Before delivering, verify:
- Problem statement contains two numbers (current state AND target state)
- Logic tree has 2-3 levels with ≥3 leaf nodes
- Each leaf is a testable cause (not a restatement like "conversion is low")
- MECE: fixing one branch doesn't auto-fix another; no cause is missing
Chain Position
Previous: none | Next: mkt-hypothesis
Before Starting
Step 0: Product Context
Check for .agents/mkt/product-context.md. If missing: INTERVIEW. Ask the user 8 product questions (what, who, problem, differentiator, proof points, pricing, objections, voice) and save to .agents/mkt/product-context.md. Or recommend running mkt-copywriting to bootstrap it.
Required Artifacts
None — this is the entry point for the Problem track.
Optional Artifacts
| Artifact | Source | Benefit |
|---|---|---|
product-context.md | mkt-copywriting | Industry context for better tree construction and benchmark selection |
Problem Interview
If the user describes a vague problem ("things aren't going well", "growth is slow"):
- Ask for the specific metric and its current value
- Ask for the target value and who set it
- If user doesn't know the baseline, use WebSearch:
"[industry] [metric] benchmark [year]"or"[business type] average [metric]" - Do NOT proceed until you have at least: metric name + current number + target number
Step 1: Define the Problem
[Metric] is [current number] instead of [target number]
Ask:
- What metric specifically? (Not "growth" — which metric?)
- What's the current number?
- What's the target? Who set it?
- When did it change? Was there an inflection point?
- How big is the gap in absolute and relative terms?
Step 2: Build a Logic Tree
Choose Tree Type
| Type | When | Example Root |
|---|---|---|
| Math Tree | Metric can be decomposed into formula | Revenue = Traffic × Conversion × AOV |
| Issue Tree | Multi-factor, non-mathematical | "Why are customers churning?" |
| Yes/No Tree | Binary decision points | "Is the problem supply-side or demand-side?" |
Build It
- Put the problem statement at the top
- Break into 2-4 mutually exclusive categories
- For each category, break into 2-3 sub-factors
- Stop at 2-3 levels deep
- MECE check: Do branches cover everything? Does fixing A auto-fix B? (If yes → overlap, restructure)
WebSearch directive: If you need to understand what factors typically drive this metric, search: "[metric] decomposition" OR "[metric] drivers" OR "what affects [metric]"
Artifact Template
On re-run: rename existing artifact to diagnosis.v[N].md and create new with incremented version.
---
skill: mkt-diagnosis
version: 1
date: {{today}}
status: draft
---
# Diagnosis
## Problem Statement
[Metric] is [current] instead of [target], a gap of [X%/X units].
Started: [when]. Inflection point: [if known].
## Logic Tree
[Tree type: Math / Issue / Yes-No]
```
[Problem statement]
├── [Branch 1]
│ ├── [Leaf 1a]
│ ├── [Leaf 1b]
│ └── [Leaf 1c]
├── [Branch 2]
│ ├── [Leaf 2a]
│ └── [Leaf 2b]
└── [Branch 3]
├── [Leaf 3a]
└── [Leaf 3b]
```
## MECE Check
- Mutually Exclusive: [confirm no overlaps]
- Collectively Exhaustive: [confirm no gaps]
## Next Step
Run `mkt-hypothesis` to form testable hypotheses for each leaf.Worked Example
User: "Our signups are declining."
Interview:
- "What's the current signup rate?" → "About 200/week, down from 350/week"
- "When did it start?" → "About 8 weeks ago"
- "Any changes around that time?" → "We launched a new homepage and changed our ad targeting"
Artifact saved to .agents/mkt/diagnosis.md:
# Diagnosis
**Date:** 2026-03-13
**Skill:** mkt-diagnosis
## Problem Statement
Weekly signups are 200 instead of 350, a gap of 43%.
Started: ~8 weeks ago. Inflection point: homepage redesign + ad targeting change.
## Logic Tree
Math Tree
```
Weekly signups declining (200 → 350 target)
├── Traffic volume declining (fewer people arriving)
│ ├── Paid traffic: ad targeting change reduced volume/quality
│ ├── Organic traffic: SEO impact from homepage redesign
│ └── Referral/direct: brand or word-of-mouth decline
├── Conversion rate declining (same traffic, fewer signups)
│ ├── Homepage redesign reduced clarity/trust
│ ├── Signup flow friction increased
│ └── Value proposition no longer resonates
└── Measurement change (signups happening but not counted)
├── Tracking code broken on new homepage
└── Attribution model changed
```
## MECE Check
- Mutually Exclusive: Traffic volume vs. conversion rate vs. measurement are independent
- Collectively Exhaustive: All signup decline must come from fewer visitors, lower conversion, or miscounting
## Next Step
Run `mkt-hypothesis` to form testable hypotheses for each leaf.References
- references/watanabe-framework.md — MECE principles and tree-building
- references/logic-tree-examples.md — 4 worked examples (SaaS churn, e-commerce, content ROI, B2B pipeline)