SuperClaude/KNOWLEDGE.md

KNOWLEDGE.md

Accumulated Insights, Best Practices, and Troubleshooting for SuperClaude Framework

This document captures lessons learned, common pitfalls, and solutions discovered during development. Consult this when encountering issues or learning project patterns.

Last Updated: 2025-11-12

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🧠 Core Insights

PM Agent ROI: 25-250x Token Savings

Finding: Pre-execution confidence checking has exceptional ROI.

Evidence:

• Spending 100-200 tokens on confidence check saves 5,000-50,000 tokens on wrong-direction work
• Real example: Checking for duplicate implementations before coding (2min research) vs implementing duplicate feature (2hr work)

When it works best:

• Unclear requirements → Ask questions first
• New codebase → Search for existing patterns
• Complex features → Verify architecture compliance
• Bug fixes → Identify root cause before coding

When to skip:

• Trivial changes (typo fixes)
• Well-understood tasks with clear path
• Emergency hotfixes (but document learnings after)

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Hallucination Detection: 94% Accuracy

Finding: The Four Questions catch most AI hallucinations.

The Four Questions:

1. Are all tests passing? → REQUIRE actual output
2. Are all requirements met? → LIST each requirement
3. No assumptions without verification? → SHOW documentation
4. Is there evidence? → PROVIDE test results, code changes, validation

Red flags that indicate hallucination:

• "Tests pass" (without showing output) 🚩
• "Everything works" (without evidence) 🚩
• "Implementation complete" (with failing tests) 🚩
• Skipping error messages 🚩
• Ignoring warnings 🚩
• "Probably works" language 🚩

Real example:

❌ BAD: "The API integration is complete and working correctly."
✅ GOOD: "The API integration is complete. Test output:
         ✅ test_api_connection: PASSED
         ✅ test_api_authentication: PASSED
         ✅ test_api_data_fetch: PASSED
         All 3 tests passed in 1.2s"

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Parallel Execution: 3.5x Speedup

Finding: Wave → Checkpoint → Wave pattern dramatically improves performance.

Pattern:

# Wave 1: Independent reads (parallel)
files = [Read(f1), Read(f2), Read(f3)]

# Checkpoint: Analyze together (sequential)
analysis = analyze_files(files)

# Wave 2: Independent edits (parallel)
edits = [Edit(f1), Edit(f2), Edit(f3)]

When to use:

• ✅ Reading multiple independent files
• ✅ Editing multiple unrelated files
• ✅ Running multiple independent searches
• ✅ Parallel test execution

When NOT to use:

• ❌ Operations with dependencies (file2 needs data from file1)
• ❌ Sequential analysis (building context step-by-step)
• ❌ Operations that modify shared state

Performance data:

• Sequential: 10 file reads = 10 API calls = ~30 seconds
• Parallel: 10 file reads = 1 API call = ~3 seconds
• Speedup: 3.5x average, up to 10x for large batches

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🛠️ Common Pitfalls and Solutions

Pitfall 1: Implementing Before Checking for Duplicates

Problem: Spent hours implementing feature that already exists in codebase.

Solution: ALWAYS use Glob/Grep before implementing:

# Search for similar functions
uv run python -c "from pathlib import Path; print([f for f in Path('src').rglob('*.py') if 'feature_name' in f.read_text()])"

# Or use grep
grep -r "def feature_name" src/

Prevention: Run confidence check, ensure duplicate_check_complete=True

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Pitfall 2: Assuming Architecture Without Verification

Problem: Implemented custom API when project uses Supabase.

Solution: READ CLAUDE.md and PLANNING.md before implementing:

# Check project tech stack
with open('CLAUDE.md') as f:
    claude_md = f.read()

if 'Supabase' in claude_md:
    # Use Supabase APIs, not custom implementation

Prevention: Run confidence check, ensure architecture_check_complete=True

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Pitfall 3: Skipping Test Output

Problem: Claimed tests passed but they were actually failing.

Solution: ALWAYS show actual test output:

# Run tests and capture output
uv run pytest -v > test_output.txt

# Show in validation
echo "Test Results:"
cat test_output.txt

Prevention: Use SelfCheckProtocol, require evidence

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Pitfall 4: Version Inconsistency

Problem: VERSION file says 4.1.9, but package.json says 4.1.5, pyproject.toml says 0.4.0.

Solution: Understand versioning strategy:

• Framework version (VERSION file): User-facing version (4.1.9)
• Python package (pyproject.toml): Library semantic version (0.4.0)
• NPM package (package.json): Should match framework version (4.1.9)

When updating versions:

1. Update VERSION file first
2. Update package.json to match
3. Update README badges
4. Consider if pyproject.toml needs bump (breaking changes?)
5. Update CHANGELOG.md

Prevention: Create release checklist

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Pitfall 5: UV Not Installed

Problem: Makefile requires uv but users don't have it.

Solution: Install UV:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# With pip
pip install uv

Alternative: Provide fallback commands:

# With UV (preferred)
uv run pytest

# Without UV (fallback)
python -m pytest

Prevention: Document UV requirement in README

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📚 Best Practices

Testing Best Practices

1. Use pytest markers for organization:

@pytest.mark.unit
def test_individual_function():
    pass

@pytest.mark.integration
def test_component_interaction():
    pass

@pytest.mark.confidence_check
def test_with_pre_check(confidence_checker):
    pass

2. Use fixtures for shared setup:

# conftest.py
@pytest.fixture
def sample_context():
    return {...}

# test_file.py
def test_feature(sample_context):
    # Use sample_context

3. Test both happy path and edge cases:

def test_feature_success():
    # Normal operation

def test_feature_with_empty_input():
    # Edge case

def test_feature_with_invalid_data():
    # Error handling

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Git Workflow Best Practices

1. Conventional commits:

git commit -m "feat: add confidence checking to PM Agent"
git commit -m "fix: resolve version inconsistency"
git commit -m "docs: update CLAUDE.md with plugin warnings"
git commit -m "test: add unit tests for reflexion pattern"

2. Small, focused commits:

• Each commit should do ONE thing
• Commit message should explain WHY, not WHAT
• Code changes should be reviewable in <500 lines

3. Branch naming:

feature/add-confidence-check
fix/version-inconsistency
docs/update-readme
refactor/simplify-cli
test/add-unit-tests

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Documentation Best Practices

1. Code documentation:

def assess(self, context: Dict[str, Any]) -> float:
    """
    Assess confidence level (0.0 - 1.0)

    Investigation Phase Checks:
    1. No duplicate implementations? (25%)
    2. Architecture compliance? (25%)
    3. Official documentation verified? (20%)
    4. Working OSS implementations referenced? (15%)
    5. Root cause identified? (15%)

    Args:
        context: Context dict with task details

    Returns:
        float: Confidence score (0.0 = no confidence, 1.0 = absolute certainty)

    Example:
        >>> checker = ConfidenceChecker()
        >>> confidence = checker.assess(context)
        >>> if confidence >= 0.9:
        ...     proceed_with_implementation()
    """

2. README structure:

• Start with clear value proposition
• Quick installation instructions
• Usage examples
• Link to detailed docs
• Contribution guidelines
• License

3. Keep docs synchronized with code:

• Update docs in same PR as code changes
• Review docs during code review
• Use automated doc generation where possible

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🔧 Troubleshooting Guide

Issue: Tests Not Found

Symptoms:

$ uv run pytest
ERROR: file or directory not found: tests/

Cause: tests/ directory doesn't exist

Solution:

# Create tests structure
mkdir -p tests/unit tests/integration

# Add __init__.py files
touch tests/__init__.py
touch tests/unit/__init__.py
touch tests/integration/__init__.py

# Add conftest.py
touch tests/conftest.py

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Issue: Plugin Not Loaded

Symptoms:

$ uv run pytest --trace-config
# superclaude not listed in plugins

Cause: Package not installed or entry point not configured

Solution:

# Reinstall in editable mode
uv pip install -e ".[dev]"

# Verify entry point in pyproject.toml
# Should have:
# [project.entry-points.pytest11]
# superclaude = "superclaude.pytest_plugin"

# Test plugin loaded
uv run pytest --trace-config 2>&1 | grep superclaude

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Issue: ImportError in Tests

Symptoms:

ImportError: No module named 'superclaude'

Cause: Package not installed in test environment

Solution:

# Install package in editable mode
uv pip install -e .

# Or use uv run (creates venv automatically)
uv run pytest

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Issue: Fixtures Not Available

Symptoms:

fixture 'confidence_checker' not found

Cause: pytest plugin not loaded or fixture not defined

Solution:

# Check plugin loaded
uv run pytest --fixtures | grep confidence_checker

# Verify pytest_plugin.py has fixture
# Should have:
# @pytest.fixture
# def confidence_checker():
#     return ConfidenceChecker()

# Reinstall package
uv pip install -e .

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Issue: .gitignore Not Working

Symptoms: Files listed in .gitignore still tracked by git

Cause: Files were tracked before adding to .gitignore

Solution:

# Remove from git but keep in filesystem
git rm --cached <file>

# OR remove entire directory
git rm -r --cached <directory>

# Commit the change
git commit -m "fix: remove tracked files from gitignore"

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💡 Advanced Techniques

Technique 1: Dynamic Fixture Configuration

@pytest.fixture
def token_budget(request):
    """Fixture that adapts based on test markers"""
    marker = request.node.get_closest_marker("complexity")
    complexity = marker.args[0] if marker else "medium"
    return TokenBudgetManager(complexity=complexity)

# Usage
@pytest.mark.complexity("simple")
def test_simple_feature(token_budget):
    assert token_budget.limit == 200

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Technique 2: Confidence-Driven Test Execution

def pytest_runtest_setup(item):
    """Skip tests if confidence is too low"""
    marker = item.get_closest_marker("confidence_check")
    if marker:
        checker = ConfidenceChecker()
        context = build_context(item)
        confidence = checker.assess(context)

        if confidence < 0.7:
            pytest.skip(f"Confidence too low: {confidence:.0%}")

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Technique 3: Reflexion-Powered Error Learning

def pytest_runtest_makereport(item, call):
    """Record failed tests for future learning"""
    if call.when == "call" and call.excinfo is not None:
        reflexion = ReflexionPattern()
        error_info = {
            "test_name": item.name,
            "error_type": type(call.excinfo.value).__name__,
            "error_message": str(call.excinfo.value),
        }
        reflexion.record_error(error_info)

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📊 Performance Insights

Token Usage Patterns

Based on real usage data:

Task Type	Typical Tokens	With PM Agent	Savings
Typo fix	200-500	200-300	40%
Bug fix	2,000-5,000	1,000-2,000	50%
Feature	10,000-50,000	5,000-15,000	60%
Wrong direction	50,000+	100-200 (prevented)	99%+

Key insight: Prevention (confidence check) saves more tokens than optimization

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Execution Time Patterns

Operation	Sequential	Parallel	Speedup
5 file reads	15s	3s	5x
10 file reads	30s	3s	10x
20 file edits	60s	15s	4x
Mixed ops	45s	12s	3.75x

Key insight: Parallel execution has diminishing returns after ~10 operations per wave

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🎓 Lessons Learned

Lesson 1: Documentation Drift is Real

What happened: README described v2.0 plugin system that didn't exist in v4.1.9

Impact: Users spent hours trying to install non-existent features

Solution:

• Add warnings about planned vs implemented features
• Review docs during every release
• Link to tracking issues for planned features

Prevention: Documentation review checklist in release process

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Lesson 2: Version Management is Hard

What happened: Three different version numbers across files

Impact: Confusion about which version is installed

Solution:

• Define version sources of truth
• Document versioning strategy
• Automate version updates in release script

Prevention: Single-source-of-truth for versions (maybe use bumpversion)

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Lesson 3: Tests Are Non-Negotiable

What happened: Framework provided testing tools but had no tests itself

Impact: No confidence in code quality, regression bugs

Solution:

• Create comprehensive test suite
• Require tests for all new code
• Add CI/CD to run tests automatically

Prevention: Make tests a requirement in PR template

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🔮 Future Explorations

Ideas worth investigating:

1. Automated confidence checking - AI analyzes context and suggests improvements
2. Visual reflexion patterns - Graph view of error patterns over time
3. Predictive token budgeting - ML model predicts token usage based on task
4. Collaborative learning - Share reflexion patterns across projects (opt-in)
5. Real-time hallucination detection - Streaming analysis during generation

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📞 Getting Help

When stuck:

1. Check this KNOWLEDGE.md for similar issues
2. Read PLANNING.md for architecture context
3. Check TASK.md for known issues
4. Search GitHub issues for solutions
5. Ask in GitHub discussions

When sharing knowledge:

1. Document solution in this file
2. Update relevant section
3. Add to troubleshooting guide if applicable
4. Consider adding to FAQ

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This document grows with the project. Everyone who encounters a problem and finds a solution should document it here.

Contributors: SuperClaude development team and community Maintained by: Project maintainers Review frequency: Quarterly or after major insights