Goal

Help users make better decisions in minutes instead of hours by applying proven frameworks, detecting cognitive biases, and structuring the evaluation process.

make-decision

Comprehensive decision-making framework for structured evaluation of options. Contains 10 decision frameworks, 8 decision type classifications, 12 cognitive biases with debiasing strategies, 10 analysis techniques, 8 domain-specific criteria templates, and 8 group facilitation techniques. Searchable knowledge base with BM25 ranking that auto-recommends frameworks, criteria, and bias warnings tailored to your specific decision 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).

How to Use This Workflow

When user requests decision-making help (decide, choose, compare, evaluate, select, prioritize, trade-off, weigh options), follow this workflow:

Step 1: Understand the Decision

Extract key information from user's decision description:

• Decision type: Binary choice, multi-option, resource allocation, strategic, operational, under uncertainty, group/stakeholder, time-pressured
• Options: What alternatives are being considered?
• Context: Industry, stakes, timeline, stakeholders, reversibility
• Constraints: Budget, time, resources, dependencies

Step 2: Generate Decision Plan (REQUIRED)

Always start with --plan to get comprehensive recommendations:

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

This command:

1. Classifies the decision type automatically
2. Searches across all 6 knowledge domains
3. Recommends the best framework for this type of decision
4. Identifies relevant evaluation criteria with weights
5. Warns about cognitive biases likely to affect this decision
6. Suggests analysis techniques and facilitation methods
7. Includes anti-patterns to avoid and a decision checklist

Example:

python3 scripts/search.py "choosing between AWS and Azure for cloud migration" --plan -p "Cloud Migration"

Step 2b: Persist Decision Plan

To save the plan for reference:

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

This creates:

• decision-plans/project-name/PLAN.md — Complete decision-making 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., "what biases should I watch for?"):

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

When to use domain searches:

Step 4: Compare Options

Use when user has 2+ named options to compare (e.g., "A vs B vs C"). Generate a comparison matrix:

python3 scripts/search.py --matrix "AWS vs Azure vs GCP" [-c "cost,scalability,security"]

This generates a weighted comparison matrix with criteria auto-suggested from templates (or custom criteria via -c), scoring guide, and calculation instructions.

Step 5: Document the Decision

Use after reaching a conclusion. Creates a journal entry for future reflection and calibration:

# Create journal entry
python3 scripts/search.py --journal "Choosing cloud provider for Q3 migration"

# Review past decisions
python3 scripts/search.py --journal --review

# Update with actual outcome (weeks/months later)
python3 scripts/search.py --journal --update "choosing-cloud" --outcome "Chose AWS, migration completed on time, 15% under budget"

Search Reference

Available Domains

Example Workflow

User request: "We need to choose between building in-house, buying a SaaS solution, or hiring a development agency for our new CRM system."

Step 1: Understand the Decision

• Decision type: Multi-Option Selection / Technology Selection
• Options: Build in-house, Buy SaaS, Hire agency
• Context: Technology decision with long-term impact
• Constraints: Budget, timeline, team capacity

Step 2: Generate Decision Plan

python3 scripts/search.py "build vs buy vs outsource CRM system" --plan -p "CRM Decision"

Output: Complete plan with decision type classification (Multi-Option Selection), recommended framework (Weighted Criteria Matrix), Technology Selection criteria with weights, analysis techniques (Sensitivity Analysis, Opportunity Cost), bias warnings (Status Quo Bias, Sunk Cost Fallacy), and decision checklist.

Step 3: Deep-Dive Searches

# Get detailed framework guidance
python3 scripts/search.py "weighted criteria evaluation" --domain frameworks

# Check for relevant biases
python3 scripts/search.py "status quo sunk cost technology" --domain biases

# Get facilitation guidance for team decision
python3 scripts/search.py "structured debate team" --domain facilitation

Step 4: Compare Options

python3 scripts/search.py --matrix "Build in-house vs Buy SaaS vs Hire agency"

Step 5: Document the Decision

python3 scripts/search.py --journal "CRM platform: build vs buy vs outsource" -p "CRM Decision"

Then: Synthesize the plan, searches, and matrix into a structured decision recommendation for the user, walking them through each step of the recommended framework.

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

Key Decision-Making Principles

1. Define before deciding — Invest time in framing the decision correctly before evaluating options
2. Map options exhaustively — Use MECE decomposition to ensure no alternatives are missed
3. Criteria before options — Define evaluation criteria BEFORE seeing options to prevent anchoring
4. Hypothesis-driven — State your Day One answer early, then test it with evidence
5. Prioritize ruthlessly — 80/20: focus analysis on the few factors that actually drive the decision
6. Test sensitivity — Identify which assumptions would flip the decision if changed
7. Watch for biases — Confirmation bias, anchoring, and sunk cost fallacy are the most dangerous
8. Stress-test with pre-mortem — Assume the decision failed and work backward to find blind spots
9. Classify reversibility — Two-way door decisions deserve quick action; one-way doors deserve deep analysis
10. Document and reflect — Keep a decision journal to improve calibration over time

Constraints

• Always start with Step 2 (--plan) before doing domain searches — the plan provides context for everything else
• Do NOT skip bias detection — every decision has biases; explicitly address them
• Keep recommendations under 500 words — decision-makers skim, not read
• Never present more than 5 criteria — choice overload defeats the purpose
• When in doubt, ask — if the user's decision type is unclear, ask one clarifying question before running the plan

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 Decision-Making Principles above manually:
   • Ask user to describe the decision → classify the type yourself
   • Suggest a framework based on the type (e.g., Weighted Matrix for multi-option, Pros-Cons-Fixes for binary)
   • Warn about the 3 most common biases for that decision type
   • Walk through the framework step by step
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