Agent Skills: Technical Decision Analysis

Analyze technical options with structured comparison matrices, scoring, and ADR draft generation. Use when the user needs help choosing between technologies, architectures, or implementation approaches.

UncategorizedID: mpazaryna/claude-toolkit/feynman-decision

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skills/practice/feynman-decision/SKILL.md

Skill Metadata

Name
feynman-decision
Description
"Analyze technical options with structured comparison matrices, scoring, and ADR draft generation. Use when the user needs help choosing between technologies, architectures, or implementation approaches."

Technical Decision Analysis

Analyze the technical decision: $ARGUMENTS

Workflow

Step 1: Understand Decision Context

  • What's being decided
  • What options exist
  • Why it matters
  • What's at stake

Step 2: Define Evaluation Criteria

Infer criteria based on decision type:

Technology Selection: Maturity, performance, developer experience, documentation, future-proofing, migration path

Architecture Decision: Complexity, maintainability, scalability, user experience, cost

Implementation Approach: Time to implement, code quality, testability, flexibility

Step 3: Research Each Option

For each option gather facts from official documentation, benchmarks, real-world usage, known issues. Analyze pros, cons, tradeoffs, and risks.

Step 4: Score Options

Create comparison matrix:

| Criterion | Weight | Option A | Option B | |-----------|--------|----------|----------| | {Criterion} | H/M/L | score/5 | score/5 |

Step 5: Generate Recommendation

  • Selected Option: Which one to choose
  • Rationale: Why (based on criteria + context)
  • Tradeoffs: What we're accepting
  • Risks: What to watch out for
  • Validation: How to verify it was right

Step 6: Create ADR Draft

# ADR-{N}: {Decision Title}

**Status**: Proposed
**Date**: {DATE}
**Context**: {WHY_NEEDED}
**Decision**: We will {SELECTED_OPTION}
**Rationale**: {WHY_THIS_CHOICE}
**Consequences**: Positive, Negative, Neutral
**Alternatives Considered**: {REJECTED_OPTIONS}

Decision Quality Principles

  1. Evidence-based: Backed by research and data
  2. Context-aware: Consider project constraints
  3. Explicit about tradeoffs: No option is perfect
  4. Reversible-aware: Note if/how decision can change
  5. Validated: Include how to verify correctness