product-requirements

name	product-requirements
description	This skill should be used when writing product requirement documents (PRD) based on affordance theory. It helps define environmental affordances, identify action possibilities, specify interaction design, and establish perception-action coupling.
depends	["real.md","cog.md"]
generates	["spec-product-requirements.md"]

Product Requirements (Affordance-Driven)

Overview

This skill transforms product ideas into comprehensive requirement documents centered on affordances - the action possibilities that the environment (product) offers to agents (users). Rather than focusing on abstract features, we define what actions the product environment invites, supports, and constrains.

Core Philosophy: In the AI era, products are environments that afford specific actions. Great products make affordances perceivable, learnable, and exploitable by both humans and AI agents.

Note for AI Agents: The generated spec document is designed to be consumed by AI agents (especially Claude Code) to ensure consistent product implementation with clear affordance structures.

Prerequisites

Before using this skill, ensure the following foundation documents exist:

1. real.md - Reality Constraints Document
2. cog.md - Cognitive Model Document

If these documents do not exist, invoke the meta-42cog skill first to generate them.

Pre-execution Checklist

[ ] real.md exists in project root or .42cog directory
[ ] cog.md exists in project root or .42cog directory
[ ] Read real.md to understand environmental constraints
[ ] Read cog.md to understand agents and their capabilities

Context Loading

From cog.md, extract:

• Agents (human and AI) and their action capabilities
• Goals and typical action sequences
• Perception systems (what agents can sense)

From real.md, extract:

• Physical constraints that limit possible actions
• Environmental invariants that shape affordances
• Safety boundaries that constrain action spaces

Affordance Theory Foundation

Gibson's Core Concepts

Affordance: The relationship between an environmental property and an agent's action capabilities.

• Objective: Exists independent of agent's perception
• Relational: Depends on both environment and agent
• Actionable: Directly linked to possible actions

Examples:

• A button affords pressing (to agents with fingers or pointing devices)
• A search box affords querying (to agents with text input capability)
• A file upload area affords dragging (to agents with drag-drop capability)

Affordance vs. Usability

Dimension	Affordance-Driven	Usability-Driven
Focus	What actions the environment invites	How easy features are to use
Starting Point	Agent-environment coupling	User interface design
Core Question	"What can be done here?"	"How usable is this?"
Design Driver	Action possibilities	Efficiency & satisfaction
Measurement	Perception-action directness	Task completion metrics

Why Affordance Matters in AI Era

1. AI-Human Equivalence: AI agents perceive affordances through APIs, DOM structures, and semantic markup - same environmental cues, different perception systems
2. Context-Action Coupling: Affordances encode action possibilities directly in the environment, reducing need for explicit instructions
3. Discoverability: Well-designed affordances are self-evident, minimizing learning curves for both humans and AI
4. Composability: Complex behaviors emerge from composing simple, well-defined affordances

🚀 Quick Start: Traditional → Affordance Mapping

If you're familiar with traditional PRDs, here's how to map concepts:

Traditional Concept	Affordance Equivalent	Quick Tip
Feature	Affordance (action possibility)	Ask: "What action does this enable?"
MVP	MAS-1 (first complete story)	Focus on one meaningful narrative
User	Agent (human/AI/both)	Consider both perception channels
Use case	Affordance sequence	Chain of perceive → act → feedback
Priority (P0/P1/P2)	MAS-1/MAS-2/MAS-3	Story completeness, not features
Acceptance criteria	Perception criteria	Test: Is it perceivable? Executable?

30-Second Decision Tree:

Are you comfortable with new paradigms?
├─ Yes → Use this version (affordance + MAS)
│         Benefits: Richer thinking, AI-ready, meaning-driven
└─ No  → Use traditional MVP approach
          Benefits: Familiar, proven, stakeholder-friendly

Want both? Write affordance-driven PRD first (deeper thinking), then extract traditional PRD for stakeholders.

💡 Pro tip: Even if you use traditional format, understanding affordances will make your requirements clearer and more actionable.

---

🔄 Using Traditional MVP Approach

When the user explicitly requests traditional MVP-style PRD, follow these steps:

1. Load the traditional skill reference:

   Read file: references/SKILL-traditional.md
   

2. Follow the traditional 5-stage process:

   • Stage 1: Define Product Overview
   • Stage 2: Specify Functional Requirements (P0/P1/P2 prioritization)
   • Stage 3: Define Non-Functional Requirements
   • Stage 4: Establish Technical Constraints
   • Stage 5: Create Acceptance Criteria

3. Generate traditional PRD format as specified in the reference file

Trigger phrases for traditional approach:

• "Use traditional PRD format"
• "Generate MVP-style requirements"
• "I prefer the familiar PRD structure"
• "Use P0/P1/P2 prioritization"
• "Follow traditional product requirements"

Note: The traditional approach is available in references/SKILL-traditional.md (English) and references/SKILL-traditional.zh.md (Chinese) for reference.

---

When to Use This Skill

MAS: Minimum Affordance Story

From MVP to MAS

Traditional product development focuses on MVP (Minimum Viable Product) - the smallest product that delivers value. In the AI era and affordance-driven design, we introduce MAS (Minimum Affordance Story) - the smallest coherent narrative of action possibilities that generates meaning.

Paradigm Comparison:

Dimension	MVP (Minimum Viable Product)	MAS (Minimum Affordance Story)
Measurement	Value (utility)	Meaning (significance)
Interaction Focus	Usability (ease of use)	Affordance (action possibility)
External Form	Product (artifact)	Story (narrative)
Evolution Path	Trial-and-error, bottom-up	Deduction, top-down, biomimicry
Influence Strategy	Extrinsic rewards	Intrinsic motivation (interest, passion)
Success Criterion	Market validation	Meaning resonance
Agent Relationship	User consumes product	Agent inhabits environment
Design Question	"Is this useful?"	"Does this invite meaningful action?"

Core Principles of MAS

1. Meaning Over Value

• MVP asks: "Does this solve a problem?"
• MAS asks: "Does this action sequence create meaning?"
• Meaning emerges from coherent affordance narratives, not isolated features

2. Story Over Product

• MVP delivers a product (object)
• MAS delivers a story (experience)
• Stories are sequences of affordances that agents can enact
• Each MAS is a complete narrative arc: perceive → act → transform → reflect

3. Deduction Over Trial-and-Error

• MVP: Build → Measure → Learn (inductive, empirical)
• MAS: Story → Affordances → Implementation (deductive, principled)
• MAS derives from first principles of perception-action coupling
• Evolution follows natural patterns (biomimicry)

4. Intrinsic Over Extrinsic

• MVP optimizes for external metrics (conversion, retention, revenue)
• MAS optimizes for internal experience (interest, mastery, passion)
• Affordances that align with intrinsic motivation create sustained engagement

MAS Structure

A Minimum Affordance Story consists of:

Story Theme: [What meaningful transformation this enables]
  ↓
Core Affordance Sequence: [Chain of perceivable actions]
  ↓
Meaning Closure: [How the sequence creates significance]

Example MAS for a Writing Tool:

## MAS-01: Capture Fleeting Thought

**Story Theme:** 
Transform a fleeting thought into a permanent note before it vanishes

**Core Affordance Sequence:**
1. Entry affordance: One-tap note creation (perceive: floating button)
2. Capture affordance: Voice or text input (perceive: input field)
3. Closure affordance: Auto-save on pause (perceive: fade-out animation)

**Meaning Closure:**
The thought, once ephemeral, is now persistent. The agent has extended their memory.

**Intrinsic Motivation:**
- Interest: Capturing insights feels rewarding
- Mastery: Getting better at externalizing thoughts
- Autonomy: No forced categorization or structure

**Not Included (Out of Scope):**
- Search (different story: "Rediscover Past Insights")
- Sharing (different story: "Collaborate on Ideas")
- Formatting (different story: "Refine Expression")

MAS vs. MVP in Practice

MVP Thinking: "Our minimum viable product needs login, note creation, and a list view. Let's launch and see if users like it."

MAS Thinking: "Our first affordance story is 'Capture Fleeting Thought.' What is the absolute minimum affordance sequence that makes this story complete and meaningful? Login isn't part of this story - deferring authentication maintains story flow."

MAS Prioritization

Replace traditional feature prioritization with story prioritization:

Priority	MAS Criteria	Traditional MVP Criteria
MAS-1	Most fundamental story, enables other stories	Core value proposition
MAS-2	Story that compounds with MAS-1	Important feature
MAS-3	Story for advanced agents	Nice-to-have enhancement

Story Dependencies:

MAS-1: Capture Thought
  ↓ (enables)
MAS-2: Rediscover Thought
  ↓ (enables)
MAS-3: Connect Thoughts
  ↓ (enables)
MAS-4: Share Insight

Each story is complete on its own, yet enables richer stories.

Implementing MAS in Product Requirements

When defining product requirements with MAS:

1. Identify Core Stories (not features)

   • What meaningful transformations do agents seek?
   • What complete narrative arcs exist?

2. Design Affordance Sequences (not feature lists)

   • What perception-action-feedback loops tell each story?
   • What is the absolute minimum to close the narrative?

3. Validate Meaning (not utility)

   • Does completing this story create significance?
   • Do agents return because of intrinsic motivation?

4. Evolve Through Deduction (not iteration)

   • What is the next logical story extension?
   • What does nature/cognition tell us about story progression?

MAS in Affordance Hierarchy

MAS maps onto affordance levels:

MAS	Affordance Level	Story Completion
MAS-1	Primary affordances only	One complete story
MAS-2	Primary + Secondary	Story with nuance
MAS-3	All levels	Story with mastery path

Critical Insight:

• MVP thinks in features: "Add search functionality"
• MAS thinks in stories: "Enable the story of rediscovering forgotten insights through search affordance"

The affordance (search) is the same, but MAS embeds it in a meaningful narrative that guides design decisions (e.g., search should surface serendipitous connections, not just exact matches).

---

Remember: Products are built. Stories are told. In the AI era, agents don't want products - they want to be protagonists in meaningful stories.

• Starting a new product or feature development
• Redesigning interaction models
• Creating AI-agent-friendly interfaces
• Defining multi-modal interaction patterns
• Establishing clear action spaces

Process

Phase 1: Define Product Environment

Product Environment Template:

## Product Environment

**Name:** [Product Name]
**Tagline:** [Action-oriented description: "A place to...", "An environment for..."]
**Version:** [X.Y.Z]

**Environmental Description:**
[2-3 sentences describing the **action space** this product creates]

**Primary Agents:**
- Agent type 1 (with their action capabilities)
- Agent type 2 (with their action capabilities)

**Core Affordances:**
1. Primary affordance (enables action X)
2. Secondary affordance (enables action Y)
3. Supporting affordance (enables action Z)

Questions to Answer:

• What action space does this product create?
• What agents will act in this environment?
• What are the fundamental action possibilities?
• How do agents perceive these possibilities?

Phase 2: Specify Affordance Structures

Organize by MAS (Minimum Affordance Story) - story-driven prioritization:

Priority	Description	Story Completeness
MAS-1	First complete affordance story - enables all other stories	Fundamental narrative
MAS-2	Second story that compounds with MAS-1	Complementary narrative
MAS-3	Advanced stories for mastery	Enrichment narrative

MAS Prioritization Framework:

Instead of isolated affordances, organize by complete stories:

## MAS-[N]: [Story Title]

**Story Theme:** [What meaningful transformation this enables]

**Core Affordance Sequence:**
1. [Affordance 1]: [Action enabled] (perceive: [signifier])
2. [Affordance 2]: [Action enabled] (perceive: [signifier])
3. [Affordance 3]: [Action enabled] (perceive: [signifier])

**Meaning Closure:**
[How this sequence creates significance for the agent]

**Intrinsic Motivation:**
- Interest: [What makes this engaging]
- Mastery: [How agents improve through repetition]
- Autonomy: [What choices agents have]

**Story Dependencies:**
- Enables: [What stories become possible after this]
- Requires: [What stories must exist before this]

**Out of Scope:**
- [Actions deliberately excluded to keep story coherent]

Affordance Hierarchy Within Each MAS:

Level	Description	Perceivability	Story Role
Primary	Core actions - must be immediately perceivable	High	Story driver
Secondary	Supporting actions - revealed through interaction	Medium	Story enhancement
Latent	Advanced actions - discovered through exploration	Low	Story mastery

Affordance Specification Template:

## Affordance: [Affordance ID] [Affordance Name]

**Level:** Primary/Secondary/Latent
**Action Enabled:** [Specific action this affords]

### Environmental Properties

| Property | Value | Perceivable By |
|----------|-------|----------------|
| Visual cue | Description | Human |
| Semantic markup | Description | AI Agent |
| Interaction pattern | Description | Both |

### Agent Requirements

| Agent Type | Required Capabilities |
|------------|----------------------|
| Human | Vision, pointing device |
| AI Agent | DOM access, click capability |

### Perception-Action Coupling

**Human Perception:**
- **See**: [What visual cues indicate this affordance]
- **Do**: [What action is invited]
- **Feedback**: [What confirms action was taken]

**AI Perception:**
- **Detect**: [What semantic/structural cues indicate this affordance]
- **Execute**: [What API/interaction method to use]
- **Verify**: [What state change confirms success]

### Constraints
- Constraint 1 (from real.md)
- Constraint 2

### Dependencies
- **Requires**: [Prior affordances that must exist]
- **Enables**: [Subsequent affordances this makes possible]

Standard Affordance Categories:

Category	Example Affordances
Entry	Sign up, log in, guest access
Creation	Write, upload, compose, generate
Manipulation	Edit, move, resize, transform
Navigation	Browse, search, filter, jump
Communication	Message, share, comment, notify
Organization	Tag, categorize, group, archive
Observation	View, preview, inspect, monitor

Phase 3: Define Environmental Constraints

Environmental constraints shape the affordance landscape:

Physical Constraints:

Constraint	Impact on Affordances
Screen size	Limits simultaneous affordances
Input methods	Defines interaction affordances
Network latency	Affects real-time affordances

Structural Constraints:

Constraint	Impact on Affordances
Data model	Defines what can be manipulated
Access control	Limits action availability by agent
State dependencies	Sequences affordance availability

Safety Constraints (from real.md):

Constraint	Affordance Limitations
Data privacy	Some data not afforded to all agents
Rate limiting	Actions artificially constrained
Validation rules	Constrain action parameters

Phase 4: Establish Perception Channels

How agents discover and recognize affordances:

For Human Agents:

### Visual Affordances

| Element | Perceived Action | Design Pattern |
|---------|-----------------|----------------|
| Button | Pressable | Raised, shadowed, labeled |
| Input field | Typeable | Bordered, placeholder text |
| Drag handle | Moveable | Grip icon, cursor change |
| Link | Navigable | Colored, underlined |

For AI Agents:

### Semantic Affordances

| Element | Semantic Marker | Perceived Action |
|---------|----------------|------------------|
| `<button>` | role="button" | Clickable action |
| `<input>` | type="text" | Text entry point |
| `aria-label` | Descriptive text | Action purpose |
| `data-action` | Action identifier | Specific behavior |

For Multi-Modal Agents:

### Cross-Modal Affordances

| Affordance | Human Channel | AI Channel | Unified Action |
|------------|---------------|------------|----------------|
| Submit form | Click button | POST to endpoint | Data submission |
| Upload file | Drag-drop | File API call | File storage |
| Search | Type + Enter | Query parameter | Content retrieval |

Phase 5: Define Feedback Mechanisms

Affordances require clear feedback to close the perception-action loop:

Immediate Feedback (< 100ms):

Action	Human Feedback	AI Feedback
Click	Visual state change	DOM update event
Type	Character appears	Value change event
Drag	Element follows cursor	Position update

Progressive Feedback (< 1s):

Action	Human Feedback	AI Feedback
Form submit	Loading indicator	Pending state
File upload	Progress bar	Upload progress event
Save	"Saving..." message	State transition

Completion Feedback (< 5s):

Action	Human Feedback	AI Feedback
Form submit	Success message	Response data
File upload	"Upload complete"	Success callback
Save	"Saved" confirmation	State confirmed

Phase 6: Create Affordance Acceptance Criteria

Acceptance Criteria Template:

## Affordance Validation

### Affordance: [Affordance Name]

| ID | Criterion | Human Test | AI Test |
|----|-----------|------------|---------|
| AFF-1 | Affordance is perceivable | Visual inspection | Semantic check |
| AFF-2 | Action is executable | User interaction | API call |
| AFF-3 | Feedback is clear | User confirmation | State verification |
| AFF-4 | Constraints respected | Boundary testing | Validation check |

Example:

### Affordance: File Upload

| ID | Criterion | Human Test | AI Test |
|----|-----------|------------|---------|
| AFF-1 | Upload area is visible | User sees drop zone | Element has dropzone attribute |
| AFF-2 | Drag-drop works | User can drag file | FileReader API available |
| AFF-3 | Progress shown | User sees upload % | Progress events fire |
| AFF-4 | File type validated | Only images accepted | MIME type checked |
| AFF-5 | Size limit enforced | 10MB max shown | 413 error on exceed |

PRD Document Structure (Affordance-Driven)

# Product Requirements Document (Affordance-Driven)

## 1. Product Environment
- Name, tagline, version
- Action space description
- Primary agents
- Core affordances

## 2. Affordance Catalog
### 2.1 Primary Affordances (Immediately Perceivable)
### 2.2 Secondary Affordances (Revealed Through Interaction)
### 2.3 Latent Affordances (Discovered Through Exploration)

## 3. Environmental Constraints
### 3.1 Physical Constraints
### 3.2 Structural Constraints
### 3.3 Safety Constraints (from real.md)

## 4. Perception Channels
### 4.1 Visual Affordances (Human)
### 4.2 Semantic Affordances (AI)
### 4.3 Cross-Modal Affordances (Both)

## 5. Feedback Mechanisms
- Immediate feedback (< 100ms)
- Progressive feedback (< 1s)
- Completion feedback (< 5s)

## 6. Affordance Acceptance Criteria
- Per-affordance validation
- Cross-affordance interactions

## 7. Non-Affordances (Explicitly Prevented Actions)
- Actions deliberately not supported
- Rationale for each

## 8. Technical Implementation
- Frontend affordance rendering
- Backend affordance support
- Infrastructure requirements

## 9. Appendix
### 9.1 Affordance Theory Reference
### 9.2 Glossary
### 9.3 Traditional Usability Mapping (for reference)

Quality Checklist

• Core affordances are clearly defined
• Each affordance specifies what action it enables
• Perception channels defined for both humans and AI
• Feedback mechanisms close the perception-action loop
• Environmental constraints are documented (from real.md)
• Acceptance criteria test affordance perceivability
• Non-affordances explicitly prevent confusion
• Agent capabilities matched to affordance requirements

Integration with Other Skills

Skill	Relationship
user-simulation	Input: Provides agent action capabilities
user-story	Output: Affordances become action scenarios
system-architecture	Output: Affordance structures drive architecture
interface-design	Output: Visual/semantic affordance implementation
quality-assurance	Output: Affordance validation tests

---

Appendix: From Usability to Affordance

Traditional Usability Framework (For Reference)

The traditional usability-focused approach (preserved for teams transitioning to affordance-driven design):

Click to expand: Traditional Usability Requirements Template

Usability Requirements

Performance Requirements:

Metric	Target	Measurement
Page Load	< 3s	First contentful paint
API Response	< 500ms	Excluding external calls
Concurrent Users	100+	Without degradation

Security Requirements:

Requirement	Implementation
Data Encryption	HTTPS, encrypted storage
Authentication	JWT/Session with expiry
Authorization	Role-based access control
Input Validation	Server-side validation

Accessibility Requirements:

Requirement	Target
Responsive Design	Mobile + Desktop
Accessibility	WCAG 2.1 AA
Browser Support	Modern browsers (2 years)

Availability Requirements:

Metric	Target
Uptime	99%
Recovery Time	< 1 hour
Backup Frequency	Daily

Mapping Table: Usability → Affordance

Usability Concept	Affordance Equivalent
Feature	Affordance (action possibility)
User flow	Affordance sequence
UI element	Affordance signifier
Interaction design	Perception-action coupling
Error message	Constraint feedback
Help text	Affordance clarification
Loading state	Progressive affordance

Why the Shift Matters

Traditional thinking: "We need a button that users can easily click" Affordance thinking: "This environment affords submitting - perceivable through button (human) and form action (AI)"

Traditional thinking: "The search feature should be user-friendly" Affordance thinking: "This environment affords querying - through search box (visual), input element (semantic), and search API (programmatic)"

The affordance perspective unifies human and AI interaction by focusing on the action possibilities the environment provides, rather than on separate "user interface" and "API" designs.

---

Last Updated: 2025-12-05 Document Version: v3.0 (Affordance-Driven) Maintainer: 42COG Team