socratic-maturity 0.1.0

Pure, standalone maturity tracking system for project lifecycle management

Socrates Maturity Tracking System

A pure, standalone library for calculating and tracking project maturity across four phases and five quality categories. This is the core foundation of the Socrates AI system's agent coordination.

What is Maturity?

Project maturity is measured across:

Four Phases:

• Discovery (0-25%): Define problem, scope, audience
• Analysis (25-50%): Gather requirements, analyze data
• Design (50-75%): Technology choices, architecture
• Implementation (75-100%): Code development, testing

Five Quality Categories:

• Code Quality
• Testing Coverage
• Documentation
• Architecture
• Performance

Key Features

Smart Calculation Algorithm

The maturity calculation uses a unique algorithm that:

• Never penalizes advancing to new phases
• Only averages phases being worked on
• Example: Discovery 100%, Analysis 0% = 100% overall (not 50%)

Pure Data Transformation

• No side effects
• No external dependencies
• Fully deterministic
• Can be used standalone or composed with other libraries

Comprehensive Category Tracking

• Per-phase category scores (0.0-1.0)
• Weak category identification (< 0.6)
• Category improvement tracking
• Historical event logging

Installation

pip install socrates-maturity

Quick Start

from socrates_maturity import MaturityCalculator

# Calculate overall maturity from phase scores
phase_scores = {"discovery": 1.0, "analysis": 0.3}
overall = MaturityCalculator.calculate_overall_maturity(phase_scores)
# overall = 0.65 (65%)

# Estimate current phase
current_phase = MaturityCalculator.estimate_current_phase(overall)
# current_phase = "design"

# Find weak categories
weak = MaturityCalculator.identify_weak_categories({
    "code_quality": 0.4,
    "testing": 0.8,
    "documentation": 0.3
})
# weak = ["code_quality", "documentation"]

# Get phase completion
completion = MaturityCalculator.get_phase_completion_percentage(overall)
# completion = 60 (60% through current phase)

API Reference

MaturityCalculator

Static utility class providing maturity calculation functions.

calculate_overall_maturity(phase_scores: Dict[str, float]) -> float

Calculate overall project maturity from phase scores.

Args:

• phase_scores: Dict mapping phase names to scores (0.0-1.0)

Returns: Overall maturity as float (0.0-1.0)

Examples:

# Single phase
MaturityCalculator.calculate_overall_maturity({"discovery": 1.0})
# → 1.0

# Two phases (no penalty for new phase)
MaturityCalculator.calculate_overall_maturity({"discovery": 1.0, "analysis": 0.0})
# → 1.0

# Two phases with progress
MaturityCalculator.calculate_overall_maturity({"discovery": 1.0, "analysis": 0.3})
# → 0.65

estimate_current_phase(overall_maturity: float) -> str

Estimate current phase based on overall maturity.

Args:

• overall_maturity: Overall maturity (0.0-1.0 or 0-100)

Returns: Phase name ("discovery", "analysis", "design", "implementation")

Examples:

MaturityCalculator.estimate_current_phase(0.1)   # → "discovery"
MaturityCalculator.estimate_current_phase(0.4)   # → "analysis"
MaturityCalculator.estimate_current_phase(0.65)  # → "design"
MaturityCalculator.estimate_current_phase(0.9)   # → "implementation"

get_phase_completion_percentage(overall_maturity: float) -> int

Get completion percentage within current phase.

Args:

• overall_maturity: Overall maturity (0.0-1.0)

Returns: Completion percentage (0-100)

Examples:

MaturityCalculator.get_phase_completion_percentage(0.1)   # → 40 (40% through discovery)
MaturityCalculator.get_phase_completion_percentage(0.4)   # → 60 (60% through analysis)
MaturityCalculator.get_phase_completion_percentage(0.9)   # → 60 (60% through implementation)

identify_weak_categories(category_scores: Dict[str, float], weak_threshold: float = 0.6) -> List[str]

Identify categories below weakness threshold.

Args:

• category_scores: Dict mapping category names to scores (0.0-1.0)
• weak_threshold: Score threshold for weakness (default 0.6)

Returns: List of weak category names

Examples:

MaturityCalculator.identify_weak_categories({
    "code_quality": 0.4,
    "testing": 0.3,
    "documentation": 0.8
})
# → ["code_quality", "testing"]

calculate_category_improvement(before: Dict[str, float], after: Dict[str, float]) -> Dict[str, float]

Calculate improvement in each category.

Args:

• before: Category scores before (0.0-1.0)
• after: Category scores after (0.0-1.0)

Returns: Dict of category → improvement delta

Examples:

MaturityCalculator.calculate_category_improvement(
    {"quality": 0.4},
    {"quality": 0.6}
)
# → {"quality": 0.2}

Data Models

CategoryScore

Per-category maturity score within a phase.

Fields:

• category: str - Category name
• current_score: float - Current score (0.0-1.0)
• target_score: float - Target score (0.0-1.0)
• confidence: float - Confidence in score
• spec_count: int - Number of specs in category

Properties:

• percentage: float - Percentage of target achieved
• is_complete: bool - Has reached target?

PhaseMaturity

Complete maturity information for a phase.

Fields:

• phase: str - Phase name
• overall_score: float - Phase overall score (0-100%)
• category_scores: Dict[str, CategoryScore] - Per-category scores
• total_specs: int - Total specs in phase
• missing_categories: List[str] - Categories below target
• strongest_categories: List[str] - Categories at/above target
• weakest_categories: List[str] - Weakest categories
• is_ready_to_advance: bool - Can move to next phase?
• warnings: List[str] - Any warnings

MaturityEvent

Historical maturity change event.

Fields:

• timestamp: datetime - When event occurred
• phase: str - Phase involved
• score_before: float - Score before event
• score_after: float - Score after event
• delta: float - Change in score
• event_type: str - Type of event
• details: Dict[str, Any] - Additional details

How It Drives Agent Coordination

This maturity system is used by the Socrates AI agent coordination layer:

1. QualityController analyzes code and calculates maturity
2. SkillGenerator detects weak categories from maturity data
3. SkillGenerator creates adaptive skills for weak areas
4. Target agents receive skills and adjust their behavior
5. LearningAgent tracks which skills were effective
6. System updates maturity as weak areas improve
7. Cycle repeats with higher maturity and better recommendations

Example workflow:

# 1. Calculate maturity
overall = MaturityCalculator.calculate_overall_maturity(
    {"discovery": 1.0, "analysis": 0.3}
)  # → 0.65

# 2. Identify weak categories
current_phase = MaturityCalculator.estimate_current_phase(overall)  # → "design"
weak_categories = MaturityCalculator.identify_weak_categories({
    "functional_requirements": 0.4,
    "non_functional_requirements": 0.5,
    "data_requirements": 0.8
})  # → ["functional_requirements", "non_functional_requirements"]

# 3. SkillGenerator creates skills for weak areas
# (skills generation happens in socratic-agents)

# 4. Agents apply skills, improving weak areas
# → After improvements, maturity increases to 0.72

Testing

Run the test suite:

pytest tests/

With coverage:

pytest --cov=src/socrates_maturity tests/

Design Principles

Pure Functions

All calculation functions are pure - same input always produces same output, with no side effects.

Deterministic

Completely deterministic and stateless. Can be used in parallel or distributed systems.

Flexible

Works with any phase or category naming scheme. Not tied to Socrates specifically.

Extensible

Easy to add new phases, categories, or calculation rules.

License

MIT

Contributing

Contributions welcome! Please ensure:

• Tests pass: pytest
• Code formatted: black src/
• No linting issues: ruff check src/
• Type hints present: mypy src/

More Information

• Full Documentation
• Socrates AI Repository
• Architecture Guide