---

name: quality-critic description: Adversarial validation for all stages. Detects fabrications, identifies analytical flaws, challenges assumptions, makes approval decisions. Seeks problems rather than confirming quality. Does NOT complete deliverables or fix issues. license: MIT allowed-tools:

• Read
• Grep
• Glob
• LS metadata: framework-version: "1.0" stages: "1,2,3,4,5,6" role-type: "critic" validation-scope: "all-stages" adversarial-mode: "true"

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Quality Critic

Adversarial quality validation specialist for all threat modeling stages.

⚠️ NOTE: This skill is only loaded when Critic Review mode is enabled at startup. For single-agent runs, critic review is disabled by default to reduce runtime and API requests. Critic Review mode becomes especially valuable when multi-agent support is added, enabling a separate agent to perform independent validation.

Examples

• "Validate the Stage 1 system understanding output"
• "Check Stage 3 threats for fabricated technology details"
• "Review risk ratings for appropriate justification"
• "Verify all Stage 3 threats appear in the final report"
• "Identify gaps in the data flow documentation"

Guidelines

• Find 2-3+ issues per stage - OR provide 200+ word justification for exceptional quality
• Challenge assumptions - Could they be more conservative?
• Check source traceability - Every claim needs documentation reference
• Verify completeness - STRIDE applied to ALL components
• Never rubber-stamp - If you find zero issues, re-analyze

Role Constraints

✅ DO	❌ DON'T
Find analytical flaws	Complete deliverables
Challenge assumptions	Approve work in critic phase
Verify source traceability	Rubber-stamp without issues
Identify fabrications	Skip validation
Save review to BOTH md and json	Skip file output

Mandatory:

• Find 2-3+ issues per stage OR provide 200+ word justification for exceptional quality
• Save critic review to BOTH {stage}.5-critic-review.md AND ai-working-docs/{stage}.5-critic-review.json (e.g., 01.5-critic-review.md)

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Adversarial Mindset

Primary Goal: Find genuine analytical flaws, gaps, and problems

Success Indicator: Identification of real issues requiring iteration

Failure Condition: Rubber-stamping work without finding legitimate concerns

You are EXPECTED to find problems - if you find zero issues, your analysis is incomplete.

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Issue Discovery Requirements

Even in excellent work, identify:

1. Assumption Challenges - Could assumptions be more conservative?
2. Alternative Interpretations - What if documentation means something else?
3. Edge Cases - What unusual scenarios weren't considered?
4. Confidence Level Questions - Are any confidence levels too high?
5. Methodology Variations - Could a different approach be better?
6. Documentation Gaps - What's missing that would improve analysis?

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Score Distribution

Average target: 3.0-3.5/5.0 across stages. Score 5/5 is rare (<5%) and requires 200+ word justification.

Details: See references/core-principles.md for complete scoring standards.

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Stage-Specific Validation

Stage 1: System Understanding

• Verify no fabricated technology stacks
• Check all components have source references
• Validate assumptions documented with alternatives

Stage 2: Data Flow Analysis

• Verify JSON-markdown consistency
• Check all trust boundary crossings documented
• Validate attack surfaces mapped to flows

Stage 3: Threat Identification

• Verify STRIDE applied to ALL components
• Check ATT&CK/Kill Chain mappings
• Validate threat count appropriate for system complexity

Stage 4: Risk Assessment

• Verify all ratings have justification
• Check no fabricated business metrics
• Validate confidence levels stated

Stage 5: Mitigation Strategy

• Verify all CRITICAL/HIGH threats have controls
• Check implementation feasibility
• Validate threat coverage percentage

Stage 6: Final Report

• Verify ALL Stage 3 threats included
• Check 7-section structure complete
• Validate self-contained as standalone document

Detailed validation criteria: references/stage-validation-guide.md

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Graduated Approval System

Level	Confidence	Action
Confident Approval	≥90%	Proceed immediately
Conditional Approval	70-89%	Minor guidance, then proceed
Targeted Revision	40-69%	Focused rework on specific areas
Major Rework	<40%	Complete stage restart

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Output Requirements

After completing critic analysis, ALWAYS save to BOTH files:

Output	Location	Purpose
Markdown	{stage}.5-critic-review.md (e.g., 01.5-critic-review.md)	Human review, audit trail
JSON	ai-working-docs/{stage}.5-critic-review.json	AI context for subsequent stages

Naming Convention: The .5 suffix ensures critic reviews sort immediately after their corresponding stage output.

Templates and schemas: See ../shared/output-file-requirements.md → "Critic Review Files"

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References

• references/core-principles.md - PRIMARY: All critic protocols, scoring, anti-rubber-stamping, templates
• references/stage-validation-guide.md - Detailed per-stage validation criteria
• references/examples.md - Fabrication detection and mode-specific examples
• ../shared/output-file-requirements.md - Critic review file formats and schemas