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From technical-decision-making
Use weighted criteria matrices to systematically compare options and make defensible technical decisions. Use when evaluating competing approaches or vendors.
npx claudepluginhub sethdford/claude-skills --plugin tech-lead-decision-makingHow this skill is triggered — by the user, by Claude, or both
Slash command
/technical-decision-making:decision-matrixThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build scoring matrices that make tradeoffs visible and defensible, replacing gut-feel decisions with systematic analysis.
Compares alternatives against weighted criteria for transparent decisions on vendors, tools, or strategies. Covers weighting methods, scoring, sensitivity analysis, and group facilitation.
Runs weighted multi-criteria analysis to clarify trade-offs and score options against explicit criteria. Useful for comparing several alternatives systematically.
Structures decisions using decision tables, weighted scoring matrices, decision trees, and Pugh matrices. Evaluates alternatives with criteria, scores, and outcomes for defensible choices.
Share bugs, ideas, or general feedback.
Build scoring matrices that make tradeoffs visible and defensible, replacing gut-feel decisions with systematic analysis.
You are a senior tech lead making a decision for $ARGUMENTS using multiple competing options. Decision matrices force clarity on criteria, prevent bike-shedding, and create accountability.
List options: Clearly name each option competing. Example: "Use Postgres" vs "Use DynamoDB" vs "Use MongoDB". Make sure these are truly alternative choices, not variations of same choice.
Define criteria: List 6-10 weighted factors. Example: performance (40%), cost (25%), team capability (20%), vendor lock-in risk (10%), time-to-delivery (5%). Weights should sum to 100. Make criteria mutually exclusive (not "performance and speed" which overlap).
Score each option: On each criterion, score option A, B, C on 0-10 scale. Document your reasoning. "Performance (Postgres 8/10 due to proven scalability for our scale, DynamoDB 7/10 due to operational unknowns, MongoDB 6/10 due to memory overhead)."
Calculate weighted score: Score × weight for each criterion, sum across all criteria per option. Option with highest score wins. Example: Postgres 8.2/10 weighted score, DynamoDB 7.1, MongoDB 6.5.
Document tradeoffs: "Postgres wins decisively. Closest alternative is DynamoDB with 1.1-point gap. Gap is meaningful; DynamoDB doesn't lose on any major criterion, just accumulates small losses. If vendor lock-in risk becomes critical, revisit."