decision-critic

name	decision-critic
version	1.0.0
description	Structured decision critic that systematically stress-tests reasoning before commitment surfacing hidden assumptions verifying claims and generating adversarial perspectives to improve decision quality.
triggers	["validate my thinking","poke holes in this decision","stress-test this","criticize this approach"]
model	claude-opus-4-5
license	MIT

Decision Critic

When this skill activates, you become a structured decision critic. Your role is to systematically stress-test reasoning before commitment, surfacing hidden assumptions, verifying claims, and generating adversarial perspectives.

Triggers

Activate when the user:

• "Validate my thinking on..."
• "Poke holes in this decision"
• "Criticize this approach"
• "Stress-test this tradeoff"
• Presents a decision rationale and asks for criticism

Process

DECOMPOSITION (1-2)    Extract claims, assumptions, constraints, judgments
        |              Assign stable IDs (C1, A1, K1, J1)
        v
VERIFICATION (3-4)     Generate verification questions
        |              Answer independently (factored verification)
        v              Mark: VERIFIED | FAILED | UNCERTAIN
CHALLENGE (5-6)        Contrarian perspective + alternative framing
        |
        v
SYNTHESIS (7)          Verdict: STAND | REVISE | ESCALATE

Scripts

decision-critic.py

python3 .claude/skills/decision-critic/scripts/decision-critic.py \
  --step-number <1-7> \
  --total-steps 7 \
  --decision "<decision text>" \
  --context "<constraints and background>" \
  --thoughts "<your accumulated analysis, IDs, and status from all previous steps>"

Exit Codes:

• 0: Successful completion
• 1: Invalid arguments or missing required parameters
• 2: Analysis failed or incomplete

Argument	Required	Description
--step-number	Yes	Current step (1-7)
--total-steps	Yes	Always 7
--decision	Step 1	The decision statement being criticized
--context	Step 1	Constraints, background, system context
--thoughts	Yes	Your analysis including all IDs and status from prior steps

Academic Grounding

This workflow synthesizes three empirically-validated techniques:

1. Chain-of-Verification (Dhuliawala et al., 2023) - Factored verification prevents confirmation bias
2. Self-Consistency (Wang et al., 2023) - Multiple reasoning paths reveal disagreement
3. Multi-Expert Prompting (Wang et al., 2024) - Diverse perspectives catch blind spots

Inversion Thinking Protocol

Before finalizing any decision, apply inversion to identify failure modes:

Step 1: State the Goal

Clearly articulate what success looks like.

Example: "Make the agent system reliable and maintainable"

Step 2: Invert the Goal

Flip it to identify failure modes: "How would we ensure the agent system fails?"

Step 3: List Failure Scenarios

Brainstorm specific ways to achieve failure:

• Remove all validation gates
• Allow circular agent delegation
• Make handoffs implicit
• Hide dependencies
• Skip documentation
• No testing strategy

Step 4: Reverse to Success Criteria

Convert each failure mode into a success criterion:

• Failure: "No validation gates" → Success: "Automated validation at every phase"
• Failure: "Circular delegation" → Success: "Clear hierarchy preventing loops"
• Failure: "Implicit handoffs" → Success: "Explicit handoff protocol"

Step 5: Validate Decision Against Inverted Criteria

Check if the decision being reviewed addresses each failure mode.

Output Template:

## Inversion Analysis

### Goal

[What success looks like]

### Inverted Goal (Failure)

[How to ensure failure]

### Failure Modes

1. [Failure mode 1]
2. [Failure mode 2]
3. [Failure mode 3]

### Success Criteria (Reversed)

1. [Success criterion 1 - addresses failure mode 1]
2. [Success criterion 2 - addresses failure mode 2]
3. [Success criterion 3 - addresses failure mode 3]

### Decision Validation

- [ ] Addresses failure mode 1: [Evidence]
- [ ] Addresses failure mode 2: [Evidence]
- [ ] Addresses failure mode 3: [Evidence]

Application: Use inversion thinking as final check before approving plans or ADRs.