| name | make-decision |
| description | AI-powered decision-making framework: 10+ methodologies, cognitive bias detection,
strategic planning, career & business decisions. Use when user says "decide",
"choose", "compare", "which one", "should I", "weigh options", "trade-off",
"pros and cons", "help me pick", "evaluate", "select", "prioritize",
"nên chọn cái nào", "chọn cái nào", "so sánh", "cân nhắc", "phân vân",
"không biết chọn gì", or describes any choice between 2+ alternatives.
|
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:
- Classifies the decision type automatically
- Searches across all 6 knowledge domains
- Recommends the best framework for this type of decision
- Identifies relevant evaluation criteria with weights
- Warns about cognitive biases likely to affect this decision
- Suggests analysis techniques and facilitation methods
- 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:
| Need | Domain | Example |
|---|
| Choose a decision framework | frameworks | --domain frameworks "hypothesis uncertainty" |
| Classify the decision type | types | --domain types "binary strategic" |
| Identify cognitive biases | biases | --domain biases "confirmation sunk cost" |
| Select analysis techniques | analysis | --domain analysis "sensitivity break-even" |
| Get evaluation criteria | criteria | --domain criteria "technology vendor" |
| Plan group facilitation | facilitation | --domain facilitation "pre-mortem red team" |
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:
python3 scripts/search.py --journal "Choosing cloud provider for Q3 migration"
python3 scripts/search.py --journal --review
python3 scripts/search.py --journal --update "choosing-cloud" --outcome "Chose AWS, migration completed on time, 15% under budget"
Search Reference
Available Domains
| Domain | Records | Use For | Example Keywords |
|---|
frameworks | 10 | Choosing a decision methodology | hypothesis, logic tree, weighted matrix, sensitivity, expected value, scenario, pros-cons, pre-mortem, reversibility, iterative |
types | 8 | Classifying the type of decision | binary, multi-option, resource allocation, strategic, operational, uncertainty, group, time-pressured |
biases | 12 | Identifying thinking errors to avoid | confirmation, anchoring, sunk cost, status quo, overconfidence, framing, availability, groupthink, planning fallacy, loss aversion |
analysis | 10 | Selecting analytical methods | sensitivity, break-even, decision tree, scenario, scoring, opportunity cost, risk-reward, bayesian, pre-mortem, reference class |
criteria | 8 | Getting evaluation criteria templates | technology, hiring, vendor, investment, market entry, product feature, organizational change, location |
facilitation | 8 | Planning group decision sessions | pre-mortem, red team, nominal group, debate, dot voting, anonymous input, devil's advocate, workplan |
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
python3 scripts/search.py "weighted criteria evaluation" --domain frameworks
python3 scripts/search.py "status quo sunk cost technology" --domain biases
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:
python3 scripts/search.py "market entry strategy" --plan
python3 scripts/search.py "market entry strategy" --plan -f markdown
Key Decision-Making Principles
- Define before deciding — Invest time in framing the decision correctly before evaluating options
- Map options exhaustively — Use MECE decomposition to ensure no alternatives are missed
- Criteria before options — Define evaluation criteria BEFORE seeing options to prevent anchoring
- Hypothesis-driven — State your Day One answer early, then test it with evidence
- Prioritize ruthlessly — 80/20: focus analysis on the few factors that actually drive the decision
- Test sensitivity — Identify which assumptions would flip the decision if changed
- Watch for biases — Confirmation bias, anchoring, and sunk cost fallacy are the most dangerous
- Stress-test with pre-mortem — Assume the decision failed and work backward to find blind spots
- Classify reversibility — Two-way door decisions deserve quick action; one-way doors deserve deep analysis
- 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:
- Check Python: Run
python3 --version or python --version — if neither is found, guide the user to install it
- 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
- Script errors: If
search.py returns no results, try broader keywords or search a different domain
- 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
