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    jpoutrin

    pattern-detection

    jpoutrin/pattern-detection
    Productivity
    6
    1 installs

    About

    SKILL.md

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    About

    Identify reusable patterns, best practices, and workflow automations during implementation that could become Product Forge skills, commands, or templates...

    SKILL.md

    Pattern Detection Skill

    Recognize and note reusable patterns during implementation for potential contribution to Product Forge.

    Purpose

    When working on projects, Claude often implements patterns that could benefit the broader Product Forge ecosystem. This skill helps identify those patterns so they can be captured by the feedback hooks and potentially become new skills, commands, or templates.

    Pattern Categories

    Code Patterns

    Implementations that follow consistent, reusable structures:

    • Factory patterns - Test fixtures, mock builders, data generators
    • Service layer patterns - Repository, command, query separation
    • Error handling - Consistent error types, recovery strategies
    • API patterns - Response formatting, pagination, filtering
    • Testing patterns - Fixtures, assertions, mocking strategies

    Workflow Patterns

    Multi-step processes that could be automated:

    • Project setup - Directory structures, config files, initial scaffolding
    • Code review - Checklists, validation steps, quality gates
    • Deployment - Build, test, deploy sequences
    • Documentation - Auto-generation, formatting, templates

    Configuration Patterns

    Settings and configurations that are commonly needed:

    • Tool configurations - Linter rules, formatters, CI/CD
    • Environment setup - Development, staging, production
    • Integration patterns - API keys, service connections

    Recognition Triggers

    Note patterns when you observe:

    1. Repetition: Same structure implemented 3+ times
    2. Best practice: Industry-standard patterns being applied
    3. Automation opportunity: Manual process that could be scripted
    4. Convention enforcement: Rules applied manually that could be automated
    5. Boilerplate reduction: Repeated code that could be templated

    Quality Criteria

    Only patterns worth capturing should be:

    Criterion Description
    Reusable Applies to multiple projects or contexts
    Non-trivial More than simple one-liners or obvious code
    Generalizable Not too specific to one codebase
    Documented Can be explained clearly to others
    Tested Validated in at least one real project

    Examples of Good Patterns

    Django Model Factory Pattern

    # Consistent factory for test fixtures
    class UserFactory:
        @classmethod
        def create(cls, **overrides):
            defaults = {"name": "Test User", "email": "test@example.com"}
            defaults.update(overrides)
            return User.objects.create(**defaults)
    

    Why it's a good pattern: Reusable across Django projects, reduces test boilerplate, follows established factory pattern.

    API Response Wrapper

    # Consistent API response format
    def api_response(data=None, error=None, status=200):
        return {
            "success": error is None,
            "data": data,
            "error": error,
            "timestamp": datetime.now().isoformat()
        }
    

    Why it's a good pattern: Standardizes API responses, easy to implement, improves API consistency.

    Pre-commit Hook Configuration

    # Standard Python project pre-commit
    repos:
      - repo: https://github.com/astral-sh/ruff-pre-commit
        hooks:
          - id: ruff
          - id: ruff-format
      - repo: https://github.com/pre-commit/mirrors-mypy
        hooks:
          - id: mypy
    

    Why it's a good pattern: Common Python setup, enforces code quality, easy to adapt.

    Examples of Poor Patterns

    Too Specific

    # Only works for this exact database schema
    def get_acme_corp_users_with_subscription():
        return User.objects.filter(company="ACME", has_subscription=True)
    

    Problem: Too specific to one project/company.

    Too Trivial

    # Just basic Python
    def add_numbers(a, b):
        return a + b
    

    Problem: Doesn't provide enough value to warrant a pattern.

    Incomplete

    # Missing error handling and edge cases
    def parse_config(path):
        return json.load(open(path))
    

    Problem: Not production-ready, would need significant expansion.

    Integration with Feedback Hooks

    When you recognize a valuable pattern:

    1. Continue implementing - Don't interrupt the user's workflow
    2. Note mentally - The pattern will be captured at session end
    3. Be descriptive - When discussing the implementation, explain why it's valuable

    The Stop hook's Haiku analysis will detect patterns based on:

    • Repeated implementations
    • Best practice discussions
    • "This could be reusable" mentions
    • Template-like code structures

    What Becomes a Pattern

    Captured patterns may become:

    Destination When
    Skill Knowledge/guidelines that Claude applies
    Command User-invoked action or wizard
    Template Scaffolding for new projects/files
    Agent Specialized automated workflow

    Notes

    • This skill helps Claude recognize patterns, not explicitly mark them
    • The feedback hooks capture patterns via AI analysis at session end
    • Use /sync-feedback to review captured patterns
    • Quality over quantity - only genuinely reusable patterns matter
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    Repository
    jpoutrin/product-forge
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