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Get Started Free →Analyze a repository and generate comprehensive architecture and workflow documentation.
.claude/skills/mikeyobrien-codebase-summary/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-17 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 110% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 953% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 1352% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 127% | 0% |
This sop analyzes a codebase and generates comprehensive documentation including structured metadata files that describe the system architecture, components, interfaces, and workflows. It can create targeted documentation files like AGENTS.md (README for AI agents), README.md, CONTRIBUTING.md, or generate a complete documentation ecosystem. The documentation is organized to make it easy for AI assistants to understand the system and help with development tasks.
Constraints for parameter acquisition:
Initialize the analysis environment and create necessary directory structure.
Constraints:
Perform comprehensive analysis of the codebase to understand its structure, components, and relationships.
Constraints:
Create comprehensive documentation files for different aspects of the system.
Constraints:
Review the documentation for consistency and completeness.
Constraints:
Create consolidated documentation files if requested.
Constraints:
Provide a summary of the documentation process and suggest next steps.
Constraints:
output_dir: ".agents/summary"
consolidate: true
consolidate_targets: "AGENTS.md"
consolidate_prompt: "Create a comprehensive AGENTS.md file optimized for AI coding assistants. You MUST focus on information that is not already present in other documentation sources like README.md or CONTRIBUTING.md. Useful information for this file includes: File purpose, directory structure, Coding style patterns, file organization patterns, instructions on how to write and run tests, documentation guidelines, and package specific guidance."
codebase_path: "/path/to/project"Setting up directory structure...
✅ Created directory .agents/summary/
✅ Created subdirectories for documentation artifacts
Analyzing codebase structure...
✅ Found 15 packages across 3 programming languages
✅ Identified 45 major components and 12 key interfaces
✅ Codebase information saved to .agents/summary/codebase_info.md
Generating documentation files...
✅ Created index.md with knowledge base metadata
✅ Generated architecture.md, components.md, interfaces.md
✅ Generated data_models.md, workflows.md, dependencies.md
Reviewing documentation...
✅ Consistency check complete
✅ Completeness check complete
✅ Review notes saved to .agents/summary/review_notes.md
Consolidating documentation...
✅ Created AGENTS.md optimized for AI coding assistants
✅ Included comprehensive project context and development guidance
Summary and Next Steps:
✅ Documentation generation complete!
✅ To use with AI assistants, add .agents/summary/index.md to context
✅ AGENTS.md provides comprehensive guidance for AI coding assistanceconsolidate_targets: "README.md"
consolidate_prompt: "Create a user-friendly README that explains the project purpose, installation, and usage"consolidate: false
check_consistency: true
check_completeness: trueUpdate mode detected - checking for changes...
✅ Found existing documentation
✅ Identified 8 commits since last update affecting 3 packages
Analyzing recent changes...
✅ Updated components: AuthService, DataProcessor, APIGateway
✅ Change summary saved to .agents/summary/recent_changes.md
Updating documentation...
✅ Updated architecture.md with new AuthService patterns
✅ Updated components.md with DataProcessor changes
✅ Updated interfaces.md with new API endpoints
Consolidating updated documentation...
✅ Updated AGENTS.md with recent changes
✅ Added "Recent Changes" section highlighting updates
Summary:
✅ Documentation updated based on 8 recent commits
✅ 3 major components updated in documentation
✅ Review .agents/summary/recent_changes.md for detailed change summaryAGENTS.md (consolidated file in root directory)
.agents/summary/
├── .last_commit (git commit hash for update_mode baseline)
├── index.md (knowledge base index)
├── codebase_info.md
├── architecture.md
├── components.md
├── interfaces.md
├── data_models.md
├── workflows.md
├── dependencies.md
├── review_notes.md
└── recent_changes.md (if update_mode)The documentation will include various Mermaid diagram types:
Architecture Overview:
mermaidgraph TB A[Frontend] --> B[API Gateway] B --> C[Auth Service] B --> D[Business Logic] D --> E[Database]
Component Relationships:
mermaidclassDiagram class UserService { +authenticate() +authorize() } class DataService { +getData() +saveData() } UserService --> DataService
API Workflows:
mermaidsequenceDiagram Client->>API: Request API->>Auth: Validate Auth-->>API: Token Valid API->>Service: Process Service-->>API: Response API-->>Client: Result
For very large codebases that take significant time to analyze:
If update mode fails to detect changes correctly:
If consolidation fails or produces poor results:
If certain aspects of the codebase are not well documented:
If git commands fail during update mode:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-17 | fail→pass | 11,162 | 2,407 | -78% | 1 | 1 | 0% | 1,847 | 3,415 | +85% | 0 | 0 | — |
case-18 | fail→pass | 12,201 | 3,009 | -75% | 1 | 1 | 0% | 1,691 | 3,551 | +110% | 0 | 0 | — |
case-01 | fail→fail | 5,636 | 10,556 | +87% | 1 | 1 | 0% | 380 | 3,925 | +933% | 0 | 0 | — |
case-02 | fail→fail | 5,722 | 9,788 | +71% | 1 | 1 | 0% | 226 | 4,592 | +1932% | 0 | 0 | — |
case-03 | fail→fail | 4,919 | 7,816 | +59% | 1 | 1 | 0% | 283 | 3,517 | +1143% | 0 | 0 | — |
case-04 | pass→fail | 13,348 | 9,112 | -32% | 1 | 1 | 0% | 2,561 | 3,387 | +32% | 0 | 0 | — |
case-05 | fail→pass | 4,328 | 17,430 | +303% | 1 | 1 | 0% | 570 | 6,004 | +953% | 0 | 0 | — |
case-06 | pass→fail | 14,504 | 19,894 | +37% | 1 | 1 | 0% | 2,876 | 6,485 | +125% | 0 | 0 | — |
case-07 | fail→pass | 3,011 | 6,832 | +127% | 1 | 1 | 0% | 286 | 4,153 | +1352% | 0 | 0 | — |
case-08 | fail→pass | 11,007 | 2,597 | -76% | 1 | 1 | 0% | 1,523 | 3,453 | +127% | 0 | 0 | — |
case-09 | fail→fail | 10,055 | 2,160 | -79% | 1 | 1 | 0% | 1,616 | 3,325 | +106% | 0 | 0 | — |
case-10 | fail→pass | 3,242 | 2,223 | -31% | 1 | 1 | 0% | 501 | 3,352 | +569% | 0 | 0 | — |
case-11 | pass→pass | 11,378 | 3,140 | -72% | 1 | 1 | 0% | 1,665 | 3,403 | +104% | 0 | 0 | — |
case-12 | fail→pass | 8,398 | 3,248 | -61% | 1 | 1 | 0% | 1,289 | 3,546 | +175% | 0 | 0 | — |
case-13 | fail→pass | 2,255 | 7,805 | +246% | 1 | 1 | 0% | 315 | 4,265 | +1254% | 0 | 0 | — |
case-14 | pass→pass | 9,700 | 3,498 | -64% | 1 | 1 | 0% | 1,588 | 3,594 | +126% | 0 | 0 | — |
case-15 | pass→pass | 12,093 | 5,051 | -58% | 1 | 1 | 0% | 1,879 | 3,820 | +103% | 0 | 0 | — |
case-16 | pass→pass | 10,682 | 2,865 | -73% | 1 | 1 | 0% | 1,581 | 3,432 | +117% | 0 | 0 | — |
case-19 | fail→pass | 10,570 | 4,055 | -62% | 1 | 1 | 0% | 1,656 | 3,654 | +121% | 0 | 0 | — |
case-20 | pass→pass | 9,333 | 2,822 | -70% | 1 | 1 | 0% | 1,450 | 3,451 | +138% | 0 | 0 | — |
case-21 | pass→pass | 11,871 | 4,023 | -66% | 1 | 1 | 0% | 1,562 | 3,661 | +134% | 0 | 0 | — |
case-22 | pass→pass | 14,808 | 4,781 | -68% | 1 | 1 | 0% | 2,158 | 3,706 | +72% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 18 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.