version: "1.0.1" name: negative-contrastive-framing description: Defines concepts, quality criteria, and boundaries by showing what they are NOT -- using anti-goals, near-miss examples, and failure patterns to create crisp decision criteria where positive definitions alone are ambiguous. Use when clarifying fuzzy boundaries, defining quality criteria, teaching by counterexample, preventing common mistakes, setting design guardrails, disambiguating similar concepts, refining requirements through anti-patterns, or when user mentions near-miss examples, anti-goals, what not to do, negative examples, counterexamples, or boundary clarification.

Negative Contrastive Framing

Workflow

Copy this checklist and track your progress:

Negative Contrastive Framing Progress:
- [ ] Step 1: Define positive concept
- [ ] Step 2: Identify negative examples
- [ ] Step 3: Analyze contrasts
- [ ] Step 4: Validate quality
- [ ] Step 5: Deliver framework

Step 1: Define positive concept

Start with initial positive definition, identify why it's ambiguous or fuzzy (multiple interpretations, edge cases unclear), and clarify purpose (teaching, decision-making, quality control). See Common Patterns for typical applications.

Step 2: Identify negative examples

For simple cases with clear anti-patterns → Use resources/template.md to structure anti-goals, near-misses, and failure patterns. For complex cases with subtle boundaries → Study resources/methodology.md for techniques like contrast matrices and boundary mapping.

Step 3: Analyze contrasts

Create negative-contrastive-framing.md with: positive definition, 3-5 anti-goals, 5-10 near-miss examples with explanations, common failure patterns, clear decision criteria ("passes if..." / "fails if..."), and boundary cases. Ensure contrasts reveal the why behind criteria.

Step 4: Validate quality

Self-assess using resources/evaluators/rubric_negative_contrastive_framing.json. Check: negative examples span the boundary space, near-misses are genuinely close calls, contrasts clarify criteria better than positive definition alone, failure patterns are actionable guards. Minimum standard: Average score ≥ 3.5.

Step 5: Deliver framework

Present completed framework with positive definition sharpened by negatives, most instructive near-misses highlighted, decision criteria operationalized as checklist, common mistakes identified for prevention.

Common Patterns

By Domain

Engineering (Code Quality):

• Positive: "Maintainable code"
• Negative: God objects, tight coupling, unclear names, magic numbers, exception swallowing
• Near-miss: Well-commented spaghetti code (documentation without structure)

Design (UX):

• Positive: "Intuitive interface"
• Negative: Hidden actions, inconsistent patterns, cryptic error messages
• Near-miss: Beautiful but unusable (form over function)

Communication (Clear Writing):

• Positive: "Clear documentation"
• Negative: Jargon-heavy, assuming context, no examples, passive voice
• Near-miss: Technically accurate but incomprehensible to target audience

Strategy (Market Positioning):

• Positive: "Premium brand"
• Negative: Overpriced without differentiation, luxury signaling without substance
• Near-miss: High price without service quality to match

By Application

Teaching:

• Show common mistakes students make
• Provide near-miss solutions revealing misconceptions
• Identify "looks right but is wrong" patterns

Decision Criteria:

• Define disqualifiers (automatic rejection criteria)
• Show edge cases that almost pass
• Clarify ambiguous middle ground

Quality Control:

• Identify anti-patterns to avoid
• Show subtle defects that might pass inspection
• Define clear pass/fail boundaries

Guardrails

Near-Miss Selection:

• Near-misses must be genuinely close to positive examples
• Should reveal specific dimension that fails (not globally bad)
• Avoid trivial failures—focus on subtle distinctions

Contrast Quality:

• Explain why each negative example fails
• Show what dimension violates criteria
• Make contrasts instructive, not just lists

Completeness:

• Cover failure modes across key dimensions
• Don't cherry-pick—include hard-to-classify cases
• Show spectrum from clear pass to clear fail

Actionability:

• Translate insights into decision rules
• Provide guards/checks to prevent failures
• Make criteria operationally testable

Avoid:

• Strawman negatives (unrealistically bad examples)
• Negatives without explanation (show what's wrong and why)
• Missing the "close call" zone (all examples clearly pass or fail)

Quick Reference

Resources:

• resources/template.md - Structured format for anti-goals, near-misses, failure patterns
• resources/methodology.md - Advanced techniques (contrast matrices, boundary mapping, failure taxonomies)
• resources/evaluators/rubric_negative_contrastive_framing.json - Quality criteria

Output: negative-contrastive-framing.md with positive definition, anti-goals, near-misses with analysis, failure patterns, decision criteria

Success Criteria:

• Negative examples span boundary space (not just extremes)
• Near-misses are instructive close calls
• Contrasts clarify ambiguous criteria
• Failure patterns are actionable guards
• Decision criteria operationalized
• Score ≥ 3.5 on rubric

Quick Decisions:

• Clear anti-patterns? → Template only
• Subtle boundaries? → Use methodology for contrast matrices
• Teaching application? → Emphasize near-misses revealing misconceptions
• Quality control? → Focus on failure pattern taxonomy

Common Mistakes:

1. Only showing extreme negatives (not instructive near-misses)
2. Lists without analysis (not explaining why examples fail)
3. Cherry-picking easy cases (avoiding hard boundary calls)
4. Strawman negatives (unrealistically bad)
5. No operationalization (criteria remain fuzzy despite contrasts)

Key Insight: Negative examples are most valuable when they're almost positive—close calls that force articulation of subtle criteria invisible in positive definition alone.