265214ac27
- Add YAML-based enhancement workflow presets shipped inside the package (default, minimal, security-focus, architecture-comprehensive, api-documentation) - Add `skill-seekers workflows` subcommand: list, show, copy, add, remove, validate - copy/add/remove all accept multiple names/files in one invocation with partial-failure behaviour - `add --name` override restricted to single-file operations - Add 5 MCP tools: list_workflows, get_workflow, create_workflow, update_workflow, delete_workflow - Fix: create command _add_common_args() now correctly forwards each --enhance-workflow as a separate flag instead of passing the whole list as a single argument - Update README: reposition as "data layer for AI systems" with AI Skills front and centre - Update CHANGELOG, QUICK_REFERENCE, CLAUDE.md with workflow preset details - 1,880+ tests passing Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
73 lines
2.3 KiB
YAML
73 lines
2.3 KiB
YAML
name: architecture-comprehensive
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description: Deep architectural analysis including patterns, dependencies, and design quality
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version: "1.0"
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applies_to:
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- codebase_analysis
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- github_analysis
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variables:
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depth: comprehensive
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stages:
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- name: base_patterns
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type: builtin
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target: patterns
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enabled: true
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uses_history: false
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- name: architecture_overview
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type: custom
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target: architecture
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uses_history: false
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enabled: true
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prompt: >
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Analyse the architectural patterns and design decisions in this codebase.
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Identify:
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1. Overall architectural style (MVC, microservices, layered, hexagonal, etc.)
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2. Key design patterns used and their purpose
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3. Component boundaries and responsibilities
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4. Data flow between components
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5. External dependencies and integration points
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Output JSON with an "architecture" object containing:
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- "style": primary architectural style
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- "patterns": list of design patterns detected
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- "components": list of key components with descriptions
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- "data_flow": description of data flow
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- "quality_score": 1-10 rating with justification
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- name: dependency_analysis
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type: custom
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target: dependencies
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uses_history: true
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enabled: true
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prompt: >
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Based on the architectural overview, analyse the dependency structure.
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Identify:
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1. Circular dependencies (red flags)
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2. High coupling between modules
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3. Opportunities for dependency injection
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4. Third-party dependency risks (outdated, unmaintained)
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5. Suggested refactoring priorities
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Output JSON with a "dependency_analysis" object.
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- name: improvement_roadmap
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type: custom
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target: roadmap
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uses_history: true
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enabled: true
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prompt: >
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Based on the full architectural analysis, create an improvement roadmap.
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Provide:
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1. Top 3 quick wins (low effort, high impact)
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2. Medium-term improvements (1-3 months)
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3. Long-term architectural goals
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4. Technical debt prioritisation
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Output JSON with a "roadmap" object containing "quick_wins", "medium_term", and "long_term" arrays.
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post_process:
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reorder_sections: []
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add_metadata:
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enhanced: true
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workflow: architecture-comprehensive
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deep_analysis: true
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