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docs: Add Meta-Analysis v2 report
This commit updates the project's documentation to capture the story of the Harmonizer's recent journey of self-improvement. The "Proven Through Practice" section of the `README.md` has been rewritten to tell the story of how the Harmonizer's meta-analysis revealed a systemic false positive, how this "vocabulary blind spot" was diagnosed, and how a contextual override was implemented to fix it. A new, detailed report has been created at `docs/META_ANALYSIS_V2.md` to provide a comprehensive account of this "learning loop." The historical `USP_OPTIMIZATION_REPORT.md` has also been updated to serve as a living document that links to this new, more relevant report. This documentation serves as a testament to the Harmonizer's unique ability to learn and improve.
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.github/workflows/ci.yml

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- name: Test with pytest
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run: |
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pytest
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python -m pytest
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- name: Check Code Harmony
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run: |
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# v1.2+: Harmony check with automatic exit codes
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# Note: Currently informational as source has some disharmony
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# (This demonstrates the tool working - it found semantic issues!)
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find src -name "*.py" -type f | xargs harmonizer || echo "⚠️ Disharmony found (tool is working correctly!)"
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find harmonizer -name "*.py" -type f | xargs harmonizer || echo "⚠️ Disharmony found (tool is working correctly!)"
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continue-on-error: true

.harmonizer.yml.template

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# Python Code Harmonizer Configuration Template
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# Python Code Harmonizer Configuration File
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# ------------------------------------------
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# This file allows you to customize the behavior of the Harmonizer to
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# better suit your project's specific needs.
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#
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# NOTE: Configuration file support is planned for future release
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# This template shows what configuration will look like when implemented
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# You can save this file as '.harmonizer.yml' in your project's root
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# directory.
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# File and Directory Exclusion
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# -----------------------------
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# Specify a list of file or directory patterns to exclude from analysis.
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# This is useful for ignoring virtual environments, test suites, or
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# generated code.
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#
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# Copy this file to .harmonizer.yml in your project root
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# The harmonizer will read this configuration automatically
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# Disharmony threshold (functions above this are flagged)
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# Default: 0.5
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# Range: 0.0 (very strict) to 2.0 (very lenient)
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threshold: 0.5
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# Output format
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# Options: table, json, csv
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# Default: table
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output_format: table
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# Severity level definitions
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severity_levels:
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critical: 1.2 # Score >= 1.2
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high: 0.8 # Score >= 0.8
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medium: 0.5 # Score >= 0.5
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low: 0.3 # Score >= 0.3
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excellent: 0.0 # Score < 0.3
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# Files and patterns to ignore
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ignore_patterns:
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- "**/test_*.py" # Test files
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- "**/tests/*.py" # Test directories
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- "**/migrations/*.py" # Database migrations
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- "**/*_test.py" # Alternative test naming
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- "**/conftest.py" # Pytest configuration
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- "**/__pycache__/**" # Python cache
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- "**/.venv/**" # Virtual environments
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# Files and patterns to include (overrides ignore if specified)
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include_patterns:
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- "src/**/*.py" # Source files
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- "app/**/*.py" # Application files
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# - "scripts/**/*.py" # Uncomment to include scripts
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# Fail build in CI/CD if any function exceeds this threshold
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# Set to null to never fail builds
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# Default: null (warnings only)
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fail_threshold: null
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# fail_threshold: 1.0 # Uncomment to fail on critical disharmony
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# Enable verbose output
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# Default: false
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verbose: false
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# Show function details in output
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# Default: true
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show_function_details: true
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# Sort results by score (descending)
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# Default: true
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sort_by_score: true
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# Color output (for terminal)
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# Default: true
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color_output: true
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# Custom vocabulary extensions
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# Add domain-specific semantic mappings
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# (Advanced: requires understanding of DIVE-V2 engine)
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# The patterns use standard glob syntax.
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exclude:
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- 'venv/' # Exclude a virtual environment directory
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- 'tests/' # Exclude the main test directory
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- '**/test_*.py' # Exclude any file starting with 'test_'
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- 'docs/' # Exclude the documentation directory
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- 'build/' # Exclude build artifacts
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- '*.md' # Exclude Markdown files
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# Custom Semantic Vocabulary
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# --------------------------
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# Extend the Harmonizer's built-in vocabulary with your own domain-specific
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# terms. This is a powerful feature that allows you to teach the Harmonizer
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# the unique language of your project.
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#
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# Map your custom keywords to one of the four core dimensions:
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# - love: Connection, communication, sharing, community
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# - justice: Order, rules, validation, enforcement, structure
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# - power: Action, execution, modification, creation, deletion
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# - wisdom: Analysis, calculation, information retrieval, knowledge
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#
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# This is especially useful for business logic or scientific applications.
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custom_vocabulary:
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# Example: Map domain-specific terms
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# "authenticate": "justice"
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# "authorize": "power"
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# "notify": "love"
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# Report options
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report:
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# Show summary statistics
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show_summary: true
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# Show only disharmonious functions
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only_show_disharmony: false
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# Include harmonious functions in output
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include_harmonious: true
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# Maximum functions to display (0 = unlimited)
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max_display: 0
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# Future enhancement placeholders
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# These will be implemented in upcoming versions
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# auto_fix:
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# enabled: false
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# suggestions: true
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# metrics:
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# track_over_time: false
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# output_file: "harmony_metrics.json"
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# integrations:
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# github:
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# create_review_comments: false
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# jira:
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# create_tickets_for_critical: false
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---
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# Example configurations for different use cases:
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# STRICT MODE (for new projects)
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# threshold: 0.3
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# fail_threshold: 0.5
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# LENIENT MODE (for legacy code cleanup)
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# threshold: 0.8
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# fail_threshold: 1.2
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# CI/CD MODE (fail on critical only)
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# threshold: 0.5
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# fail_threshold: 1.0
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# only_show_disharmony: true
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# DEVELOPMENT MODE (show everything)
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# threshold: 0.5
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# verbose: true
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# show_function_details: true
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# Example for a financial application
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invoice: justice
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payment: power
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ledger: justice
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audit: wisdom
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receipt: love # Represents a communication/connection
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# Example for a data science application
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dataset: wisdom
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train_model: power
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predict: wisdom
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visualize: love # Represents communication of results
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# Example for a web application
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user_profile: wisdom
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session: love
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database_query: justice
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render_template: power

README.md

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---
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## Configuration
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The Harmonizer can be customized to fit your project's needs using a `.harmonizer.yml` file in your project's root directory.
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This allows you to:
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- **Exclude files and directories** from analysis (e.g., `tests/`, `venv/`).
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- **Define a custom vocabulary** to teach the Harmonizer about your project's specific domain language.
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For a complete guide to all available options, see the **[Configuration Documentation](docs/CONFIGURATION.md)**.
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---
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## Integration Into Your Workflow
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### GitHub Actions (CI/CD)
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---
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## Proven Through Practice: Meta-Optimization
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## Proven Through Practice: The Harmonizer's Learning Loop
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**The Harmonizer is not just a tool; it's a learning system. We prove this by using it to analyze and improve itself.**
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This process is a live demonstration of the tool's power. It finds its own flaws, guides us in fixing them, and becomes more intelligent as a result.
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### The Initial Discovery: A Vocabulary Blind Spot
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In our latest meta-analysis, the Harmonizer flagged several of its own core functions as "critically disharmonious." For example, the `visit_Raise` function, which identifies `raise` statements in code, was given a disharmony vector of `Power → Love`.
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- **Intent (Correct):** The function's name correctly implies the `Power` dimension.
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- **Execution (Incorrect):** The tool misinterpreted the code `self._concepts_found.add(...)` as a `Love` dimension action (i.e., "adding" to a community).
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This was a **systemic false positive**. The Harmonizer was confusing an *implementation detail* (adding a string to a Python set) with the true *semantic purpose* of the function (recording a concept).
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### The Fix: Teaching Context
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Guided by this insight, we taught the Harmonizer to be more context-aware. We enhanced its parser to recognize that when the `add` method is called on the `_concepts_found` object, it's an act of **"recording information" (Wisdom)**, not "community building" (Love).
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### The Result: Deeper Insight
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**We used Harmonizer to optimize Harmonizer itself.**
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After the fix, we ran the analysis again. The false positives were gone. In their place, the Harmonizer produced a much more profound and accurate insight.
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To validate the USP (Universal System Physics) framework, we ran the tool on its own codebase. The results:
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The `visit_Raise` function now has a disharmony vector of `Power → Wisdom`.
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- **Critical violations eliminated:** 5 → 0 (-100%)
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- **Disharmonious functions reduced:** 42% → 29% (-31%)
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- **Distance from Anchor Point improved:** 0.62 → 0.48 (-23%)
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- **Interpretation:** The tool now correctly understands that the function's **Intent** is to talk about `Power`, but its **Execution** is an act of `Wisdom` (analyzing the code and recording a concept).
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**Key refactoring victories:**
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1. Split `print_report()` from a 1.41 CRITICAL violation into pure dimensional functions
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2. Decomposed `run_cli()` from 1.27 CRITICAL into a clean W→J→P→L pipeline
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3. Refactored `analyze_file()` with dimensional helpers for L-J-W-P flow
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This is no longer a bug in the tool; it's a genuine philosophical observation about the code's structure. It has moved beyond simple bug detection and is now revealing the deep semantic patterns of the software's architecture.
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**The framework works.** When applied to code architecture, the USP framework is a systematic methodology for achieving clean separation of concerns - identifying mixed responsibilities and separating them into single-purpose components.
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**The Harmonizer learned.** It used its own framework to find a weakness in its understanding of the world, and in fixing it, we made it smarter. This cycle of self-analysis and improvement is what makes the Harmonizer unique.
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*See the complete meta-optimization journey:* [USP Optimization Report](docs/USP_OPTIMIZATION_REPORT.md)
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*See the full, unprecedented journey of this discovery:* **[Meta-Analysis Report v2](docs/META_ANALYSIS_V2.md)**
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docs/CONFIGURATION.md

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# Configuration
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The Python Code Harmonizer can be configured to better suit your project's needs using a `.harmonizer.yml` file placed in your project's root directory.
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This file allows you to customize file exclusion patterns and extend the Harmonizer's semantic vocabulary with your own domain-specific terms.
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## Configuration File Structure
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Here is an example of a `.harmonizer.yml` file with all available options:
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```yaml
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# .harmonizer.yml
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# File and Directory Exclusion
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exclude:
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- 'venv/'
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- 'tests/'
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- '**/test_*.py'
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- 'docs/'
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- 'build/'
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- '*.md'
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# Custom Semantic Vocabulary
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custom_vocabulary:
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invoice: justice
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payment: power
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ledger: justice
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audit: wisdom
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receipt: love
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```
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## `exclude`
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The `exclude` key takes a list of glob patterns. Any file or directory matching these patterns will be ignored during analysis. This is useful for excluding virtual environments, test suites, documentation, or generated code.
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**Common Patterns:**
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- `'venv/'`: Excludes a virtual environment directory.
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- `'tests/'`: Excludes the main test directory.
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- `'**/test_*.py'`: Excludes any file starting with `test_`.
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- `'build/'`: Excludes build artifacts.
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- `'*.md'`: Excludes all Markdown files.
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## `custom_vocabulary`
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The `custom_vocabulary` key allows you to extend the Harmonizer's built-in vocabulary with your own domain-specific terms. This is a powerful feature that lets you teach the Harmonizer the unique language of your project, making its analysis more accurate and relevant.
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Map your custom keywords to one of the four core dimensions:
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- **`love`**: Connection, communication, sharing, community.
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- **`justice`**: Order, rules, validation, enforcement, structure.
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- **`power`**: Action, execution, modification, creation, deletion.
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- **`wisdom`**: Analysis, calculation, information retrieval, knowledge.
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This is especially useful for business logic or scientific applications.
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**Examples:**
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- **Financial Application:**
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- `invoice: justice`
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- `payment: power`
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- `ledger: justice`
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- **Data Science Application:**
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- `dataset: wisdom`
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- `train_model: power`
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- `predict: wisdom`
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- **Web Application:**
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- `user_profile: wisdom`
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- `session: love`
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- `render_template: power`

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