problem-solving-pro

name

problem-solving-pro

description

Systematic problem-solving toolkit: root cause analysis, hypothesis testing, debugging strategies, critical thinking frameworks. Use when user says "solve", "analyze", "diagnose", "debug", "figure out", "what's wrong", "root cause", "why is this happening", "I'm stuck", "break down", "decompose", "structure", "tại sao bị vậy", "tìm nguyên nhân", "phân tích", "giải quyết", "bị kẹt", "không biết làm sao", or describes any problem that needs structured decomposition.

Goal

Help users solve complex problems systematically using proven frameworks — turning vague issues into structured analyses with actionable recommendations.

problem-solving-pro

Comprehensive structured problem-solving framework for tackling any complex challenge. Contains a 7-step methodology, 15 decomposition frameworks, 8 prioritization techniques, 12 analysis tools, 12 cognitive biases with debiasing strategies, 10 communication patterns, 12 mental models, and 10 team dynamics patterns. Searchable database with reasoning-based recommendations that adapts to your specific problem type.

Prerequisites

IMPORTANT: Detect the correct Python command first. Some systems use python3, others use python. Run:

python3 --version 2>/dev/null || python --version

Use whichever command succeeds (python3 or python) for ALL script calls below. If the system only has python (common on Windows), substitute python everywhere you see python3 in this document.

If Python is not installed at all, install it based on user's OS:

macOS:

brew install python3

Ubuntu/Debian:

sudo apt update && sudo apt install python3

Windows:

winget install Python.Python.3.12

Note: On Windows, Python 3 is typically available as python (not python3).

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How to Use This Workflow

When user requests problem-solving help (analyze, solve, diagnose, decide, structure, decompose, plan, strategy, recommendation), follow this workflow:

Step 1: Understand the Problem

Extract key information from user's problem description:

• Problem type: Business performance, market entry, organizational change, product, cost reduction, innovation, crisis, data/analytics, partnership/M&A, policy
• Complexity: Well-structured, ill-structured, wicked
• Keywords: revenue, growth, decline, entry, change, innovation, cost, crisis, etc.
• Context: Industry, scale, time pressure, stakeholder dynamics

Step 2: Generate Problem-Solving Plan (REQUIRED)

Always start with --plan to get comprehensive recommendations with reasoning:

python3 scripts/search.py "<problem_description>" --plan [-p "Project Name"]

This command:

1. Classifies the problem type automatically
2. Searches across all 9 knowledge domains in parallel
3. Applies reasoning rules to select best frameworks and tools
4. Returns a complete solving plan: methodology, decomposition, analysis, communication, mental models, bias warnings
5. Includes anti-patterns to avoid and a problem-solving checklist

Example:

python3 scripts/search.py "revenue declining 20% despite market growth" --plan -p "Revenue Recovery"

Step 2b: Persist Problem-Solving Plan

To save the plan for reference:

python3 scripts/search.py "<problem>" --plan --persist -p "Project Name"

This creates:

• solving-plans/project-name/PLAN.md — Complete problem-solving plan

Step 3: Deep-Dive Domain Searches

Use when the plan's recommendation needs more detail, OR when user asks about a specific topic (e.g., "how do I do a root cause analysis?"):

python3 scripts/search.py "<keyword>" --domain <domain> [-n <max_results>]

When to use domain searches:

Need	Domain	Example
Understand the methodology steps	steps	--domain steps "define problem"
Classify the problem	problem-types	--domain problem-types "wicked systemic"
Choose decomposition framework	decomposition	--domain decomposition "MECE logic tree"
Pick prioritization technique	prioritization	--domain prioritization "impact feasibility"
Select analysis tools	analysis	--domain analysis "root cause benchmark"
Identify cognitive biases	biases	--domain biases "confirmation anchoring"
Structure communication	communication	--domain communication "pyramid executive"
Apply mental models	heuristics	--domain heuristics "first principles inversion"
Improve team dynamics	team	--domain team "red team brainstorm"

Step 4: Apply the Framework

Guide the user through the recommended process:

1. Define: Help craft a precise problem statement
2. Decompose: Build the recommended logic tree (MECE)
3. Prioritize: Apply 80/20 to focus on what matters
4. Plan: Design specific analyses for priority issues
5. Analyze: Guide data gathering and hypothesis testing
6. Synthesize: Extract 'so what' insights and build the argument
7. Communicate: Structure the recommendation for the audience

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Search Reference

Available Domains

Domain	Records	Use For	Example Keywords
steps	7	Understanding each step of the methodology	define, disaggregate, prioritize, analyze, synthesize, communicate
problem-types	8	Classifying the type of problem	diagnostic, opportunity, wicked, prediction, negotiation, design
decomposition	10	Choosing how to break down the problem	issue tree, hypothesis tree, MECE, profitability, process, scenario
prioritization	8	Deciding where to focus effort	pareto, impact-feasibility, sensitivity, dot-voting, MoSCoW, weighted
analysis	12	Selecting analytical methods	benchmark, root cause, regression, scenario, fermi, A/B test, pre-mortem
biases	12	Identifying thinking errors to avoid	confirmation, anchoring, sunk cost, groupthink, overconfidence, framing
communication	10	Structuring findings and recommendations	pyramid, SCR, action titles, BLUF, one-page, storytelling, day-1 answer
heuristics	12	Applying mental models to the problem	first principles, inversion, second-order, Bayesian, Occam, leverage
team	10	Improving team problem-solving effectiveness	red team, brainstorm, psychological safety, hypothesis-driven, workplan

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Example Workflow

User request: "Our company's revenue has declined 20% this year despite the market growing. Help me figure out what's going on and what to do about it."

Step 1: Understand the Problem

• Problem type: Business Performance / Diagnostic
• Complexity: Ill-structured (multiple potential causes)
• Keywords: revenue, decline, market growth, performance gap
• Context: Company underperforming vs market

Step 2: Generate Problem-Solving Plan (REQUIRED)

python3 scripts/search.py "revenue declining 20% despite market growth" --plan -p "Revenue Diagnosis"

Output: Complete plan with profitability tree decomposition, Pareto prioritization, benchmarking + root cause analysis toolkit, pyramid principle communication, and bias warnings (confirmation bias, anchoring).

Step 3: Deep-Dive Searches

# Get decomposition framework details
python3 scripts/search.py "profitability revenue cost" --domain decomposition

# Get root cause analysis methodology
python3 scripts/search.py "root cause 5 whys diagnostic" --domain analysis

# Check for relevant biases
python3 scripts/search.py "confirmation bias anchoring" --domain biases

Step 4: Apply the Framework

Walk the user through:

1. Define: "Revenue declined 20% YoY (vs market +5%) — identify root drivers and recommend recovery actions within 90 days"
2. Decompose: Profitability tree → Revenue (Price × Volume) → Costs (Fixed + Variable)
3. Prioritize: Which branches explain >80% of the decline?
4. Analyze: Benchmark against competitors, test hypotheses on each priority branch
5. Synthesize: Group findings into themes, extract 'so what' for each
6. Communicate: Start with the recommendation (pyramid), support with evidence

Then: Synthesize the plan + domain searches into a structured problem-solving approach for the user.

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Output Formats

The --plan flag supports two output formats:

# ASCII box (default) - best for terminal display
python3 scripts/search.py "market entry strategy" --plan

# Markdown - best for documentation
python3 scripts/search.py "market entry strategy" --plan -f markdown

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Key Principles

1. Start with the problem, not the solution — Invest time in defining and framing before solving
2. Disaggregate before analyzing — Break it down MECE before diving into any branch
3. Prioritize ruthlessly — 80/20: focus on the vital few issues that drive the answer
4. Be hypothesis-driven — State your best guess early, then test it (Day 1 Answer)
5. So what? — Every finding must pass the 'so what' test to matter
6. Answer first — Lead with the recommendation, support with evidence (Pyramid Principle)
7. Watch for biases — Confirmation bias, anchoring, and groupthink are the most dangerous
8. Iterate — Update your answer as evidence comes in (Bayesian updating)
9. Simple first — Use the simplest analysis that answers the question (Occam's Razor)
10. Communication is the final product — The best analysis is worthless if you can't drive action
11. What you'd have to believe — When stuck or attached to an idea, ask "what would have to be true for this to be the right answer?" to break framing ruts and rigorously test assumptions.

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Constraints

• Always start with Step 2 (--plan) before doing domain searches — the plan provides context for everything else
• Problem statement must be specific — reject vague statements; ask for measurable outcomes
• Keep the logic tree to 3 levels max — deeper than 3 loses clarity
• Limit to top 3 priority branches — 80/20 rule; don't analyze everything
• When user is vague, ask — if the problem type is unclear, ask: "What would success look like?" before running the plan

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Error Handling

If the Python scripts fail or are unavailable:

1. Check Python: Run python3 --version or python --version — if neither is found, guide the user to install it
2. Manual fallback: If scripts cannot run, apply the Key Principles above manually:
   • Ask the user to describe the problem → classify the type yourself
   • Suggest a decomposition framework (e.g., Issue Tree for diagnostic, Hypothesis Tree for uncertain causes)
   • Walk through the 7-step methodology: Define → Decompose → Prioritize → Plan → Analyze → Synthesize → Communicate
   • Warn about the 3 most common biases for that problem type
3. Script errors: If search.py returns no results, try broader keywords or search a different domain
4. Non-English queries: The knowledge base is English-only. Translate the user's key terms to English before calling search.py — this ensures rich results for any language