morphological-analysis-triz

Name: morphological-analysis-triz
Author: lyndonkl/claude

$npx mdskill add lyndonkl/claude/morphological-analysis-triz

Systematically explore design spaces and resolve contradictions.

Generates novel solutions for engineering problems and patent opportunities.
Depends on parameter-option matrices and TRIZ inventive principles.
Decides recommendations by evaluating feasible design alternatives.
Delivers structured solutions through morphological boxes and principles.

SKILL.md

.github/skills/morphological-analysis-trizView on GitHub ↗

---
name: morphological-analysis-triz
description: Explores solution spaces systematically through morphological analysis (parameter-option matrices) and resolves technical contradictions using TRIZ inventive principles to generate novel, non-obvious solutions. Use when exploring all feasible design alternatives before prototyping, resolving technical contradictions (speed vs precision, strength vs weight, cost vs quality), generating novel product configurations, finding inventive solutions to engineering problems, identifying patent opportunities, or when user mentions morphological analysis, Zwicky box, TRIZ, inventive principles, systematic innovation, or design space exploration.
---

# Morphological Analysis & TRIZ

## Workflow

Copy this checklist:

```
Morphological Analysis & TRIZ Progress:
- [ ] Step 1: Define problem and objectives
- [ ] Step 2: Choose method (MA, TRIZ, or both)
- [ ] Step 3: Build morphological box (if MA)
- [ ] Step 4: Identify contradictions (if TRIZ)
- [ ] Step 5: Apply TRIZ principles
- [ ] Step 6: Evaluate and select solutions
```

**Step 1: Define problem and objectives**

Clarify problem statement, key objectives, constraints (cost, size, time, materials), and success criteria.

**Step 2: Choose method**

- **Morphological Analysis:** 3-7 clear parameters, each with 2-5 options, goal is comprehensive exploration
- **TRIZ:** Clear contradiction (improving A worsens B), need inventive breakthrough
- **Both:** Complex system with parameters AND contradictions

**Step 3: Build morphological box (if using MA)**

1. Identify 3-7 independent parameters (changing one doesn't force another)
2. List 2-5 distinct options per parameter
3. Create parameter × option matrix

See [resources/template.md](resources/template.md) for structure.

**Step 4: Identify contradictions (if using TRIZ)**

State clearly:
- **Improving parameter:** What to increase?
- **Worsening parameter:** What degrades?
- Look up in TRIZ contradiction matrix

See [resources/template.md](resources/template.md) for 39 TRIZ parameters and contradiction matrix.

**Step 5: Apply TRIZ principles**

1. Review 3-4 principles recommended by matrix
2. Brainstorm applications of each principle
3. Generate solution concepts
4. Combine principles for stronger solutions

See [resources/template.md](resources/template.md) for all 40 principles.

For advanced techniques, see [resources/methodology.md](resources/methodology.md).

**Step 6: Evaluate and select**

**Morphological:** Identify promising combinations, eliminate infeasible, score on objectives, select top 3-5

**TRIZ:** Assess contradiction resolution, check side effects, estimate difficulty, select most promising

Use [resources/evaluators/rubric_morphological_analysis_triz.json](resources/evaluators/rubric_morphological_analysis_triz.json) for quality criteria.

## Common Patterns

### Typical Parameters (Examples)

**Physical Products:** Materials, power source, form factor, control interface, manufacturing method
**Software:** Architecture, data storage, UI, deployment, authentication
**Services:** Delivery channel, pricing model, timing, customization, support level
**Processes:** Automation level, batch size, quality control, scheduling, location

### Common Contradictions

| Improving ↑ | Worsens ↓ | Example TRIZ Principles |
|-------------|-----------|------------------------|
| Speed | Precision | Segmentation, Periodic action |
| Strength | Weight | Anti-weight, Composite materials |
| Reliability | Complexity | Segmentation, Beforehand cushioning |
| Functionality | Ease of use | Segmentation, Universality |
| Capacity | Size | Nesting, Another dimension |

**Full principles list:** See [resources/template.md](resources/template.md) for all 40.

### When to Combine MA + TRIZ

1. Build morphological box → Find promising configurations
2. Identify contradictions in top configurations
3. Apply TRIZ to resolve contradictions
4. Re-evaluate configurations with contradictions resolved

## Guardrails

**Morphological Analysis:**
- **Limit parameters:** 3-7 parameters (too few = incomplete, too many = explosion)
- **Ensure independence:** Changing one parameter shouldn't force changes in another
- **Manageable options:** 2-5 per parameter (practical range)
- **Don't enumerate all:** Focus on promising clusters

**TRIZ:**
- **Verify real contradiction:** Improving A truly worsens B (not just budget limit)
- **Adapt principles:** Use as metaphors, not literal prescriptions
- **Check new contradictions:** Solution may introduce new trade-offs
- **Combine principles:** Often need 2-3 together

**General:**
- Document rationale for parameters/options selected
- Iterate if first pass reveals missing dimensions
- Prototype top concepts - don't just analyze

## Quick Reference

**Resources:**
- `resources/template.md` - Morphological structure, TRIZ contradiction matrix, 40 principles
- `resources/methodology.md` - Advanced TRIZ (trends of evolution, substance-field, ARIZ algorithm)
- `resources/evaluators/rubric_morphological_analysis_triz.json` - Quality criteria

**Output:** `morphological-analysis-triz.md` with problem definition, morphological matrix (if used), contradictions, TRIZ principles applied, solution concepts, evaluation, selected solutions

**Success Criteria:**
- Parameters independent and essential (3-7 with 2-5 options each)
- Contradictions clearly stated (improving/worsening parameters)
- Multiple principles applied per contradiction
- Solutions are novel, feasible, address objectives
- Top 3-5 selected with rationale
- Score ≥ 3.5 on rubric

**Quick Decisions:**
- **Simple configuration?** → Morphological only
- **Clear contradiction?** → TRIZ only
- **Complex with trade-offs?** → Both methods
- **Unsure?** → Start TRIZ to identify contradictions, then build morphological box

**Common Mistakes:**
1. Too many parameters (>7 = explosion)
2. Dependent parameters (choosing A forces B)
3. Vague contradiction ("better vs cheaper" - be specific)
4. Literal TRIZ (principles are metaphors)
5. No evaluation (generate but don't filter)

**Examples:**

**Morphological (Portable Speaker):**
```
Power: Battery | Solar | Hybrid
Size: Pocket | Handheld | Tabletop
Audio: Mono | Stereo | Surround
Material: Plastic | Metal | Fabric
Control: Button | Touch | Voice | App
Result: 3×3×3×4×4 = 432 configs → Evaluate top 10
```

**TRIZ (Electric Vehicle Range):**
```
Contradiction: Increase range → worsens cost (battery expensive)
Principles: #6 (Universality - battery is structure), #35 (Parameter change - new chemistry)
Solution: Structural battery pack + high energy density cells
```

**Combined:**
```
Build morphological box for EV architecture → Top config has range/cost contradiction → Apply TRIZ Universality principle → Structural battery resolves both range and cost
```

---

**For detailed principle explanations, contradiction matrix, advanced techniques (substance-field analysis, ARIZ, trends of evolution), and software/service adaptation, see [resources/template.md](resources/template.md) and [resources/methodology.md](resources/methodology.md).**

More from lyndonkl/claude

Skill	Description
abstraction-concrete-examples	Builds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.
academic-letter-architect	Guides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
adr-architecture	Documents significant architectural and technical decisions with full context, alternatives considered, trade-offs analyzed, and consequences understood. Creates a decision trail that helps teams understand why decisions were made. Use when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when user mentions ADR, architecture decision, technical decision record, or decision documentation.
adverse-selection-prior	Produces a Bayesian prior probability that an offered transaction is +EV for the recipient, given that the counterparty chose to propose it. Applies Akerlof market-for-lemons logic -- if they offered it, they believe it is +EV for them, so the prior that it is +EV for us is materially below 50%. Reusable across trade evaluation, waiver drops (another team dropping a player is also adverse selection), job-offer analysis, M&A, and any "someone offered me this" situation. Use when you receive an unsolicited trade/offer/proposal, analyzing incoming trade prior, evaluating why a counterparty proposed a deal, or when user mentions adverse selection, market for lemons, why did they offer this, incoming trade prior, they proposed it, Bayesian adjustment on received offer.
alignment-values-north-star	Creates actionable alignment frameworks that give teams a shared North Star (direction), values (guardrails), and decision tenets (behavioral standards). Enables autonomous decision-making while maintaining organizational coherence. Use when starting new teams, scaling organizations, defining culture, establishing product vision, resolving misalignment, creating strategic clarity, or when user mentions North Star, team values, mission, principles, guardrails, decision framework, or cultural alignment.
analogy-weight-check	For every analogy in a substacker draft, verifies it carries mechanical weight — the analogy does real work explaining the mechanism, not merely decorates it. Cross-references analogy-catalog.md for novelty (is this analogy reused from a prior post?) and domain fit (biology > organizational > sports preferred; physics/military disfavored). Use whenever an analogy appears in the draft. Trigger keywords: analogy weight, decorative, mechanical weight, reused analogy, catalog check, metaphor check.
answer-uncomfortable-question	Takes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords: uncomfortable question, strategic question, evidence reasoning downside, triad.
attribute-performance	For each substacker post that materially over- or under-performs the rolling baseline (\|z\| ≥ 1.0), produces a plain-English attribution paragraph with calibrated confidence (high / medium / low / unexplained). Considers subject-line effect, topic zeitgeist, external share, day-of-week, length effect, and audience-notes signals. Labels unexplained outliers explicitly rather than fabricating a story. Use after compute-baseline when outlier posts exist. Trigger keywords: attribution, why did this post work, outlier explanation, performance analysis.
auction-first-price-shading	Computes the optimal shaded bid for a first-price sealed-bid auction given a true private value, an estimate of the number of competing bidders N, and a value-distribution assumption. Implements the `(N-1)/N` equilibrium shading rule for uniform private values, adjusts for log-normal or empirical value distributions, layers a risk-aversion adjustment, and caps output against the bidder's remaining budget. Domain-neutral auction theory reusable across fantasy sports (baseball FAAB, NBA/NHL waiver auctions), prediction-market limit sizing, sealed procurement bids, and any blind-bid context. Use when user mentions "first-price auction bid", "sealed bid shading", "(N-1)/N", "FAAB bid amount", "auction shading", "optimal bid first-price", "bid for sealed-bid", "blind bid sizing", or when downstream logic needs a principled shade factor rather than an ad-hoc heuristic.
auction-winners-curse-haircut	Applies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".