smart-docs
- Repo stars 1,022
- Author updated Live
- Author repo deepwiki-rs
- Domain
- Documentation
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 85 / 100 · community maintained
- Author / version / license
- @sopaco · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- Node.js · Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。; 上游仓库已 206 天未更新,可能与最新 agent 行为不一致。
---
name: smart-docs
description: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifie…
category: documentation
runtime: Node.js / Python
---
# smart-docs output preview
## PART A: Task fit
- Use case: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates C4 model diagrams, and generates professional technical documentation. Use when users need to document codebases, understand software architecture, create technical specs, or generate developer guides. Supports all programming languages. Alternative to Litho/deepwiki-rs that uses Claude Code subscription without external API costs..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates C4 model diagrams, and generates professional technical documentation. Use when users need to document codebases, understand software architecture, create technical specs, or generate developer guides. Supports all programming languages. Alternative to Litho/deepwiki-rs that uses Claude Code subscription without external API costs.”.
- **02** When the source has headings, the agent prioritizes “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; usually needs no extra API key.
## Running Rules
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: smart-docs
description: AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifie…
category: documentation
source: sopaco/deepwiki-rs
---
# smart-docs
## When to use
- AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture pattern…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "smart-docs" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Core Principles / Workflow / Phase 1: Project Discovery (5-10 minutes)
rules -> SKILL.md triggers / order / output contract
runtime -> Node.js / Python | read files, write/modify files | may access external network resources
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Smart Documentation Generator
You are an expert software architect and technical writer. Your task is to generate comprehensive, professional codebase documentation similar to Litho/deepwiki-rs, but using Claude Code's native capabilities without external LLM API calls.
Core Principles
- Progressive Analysis: Analyze codebases incrementally, not all at once
- Pattern Recognition: Identify common architectural patterns
- C4 Model: Structure documentation following C4 model levels
- Mermaid Diagrams: Use Mermaid for all visualizations
- Markdown Output: Generate well-structured markdown files
Workflow
Phase 1: Project Discovery (5-10 minutes)
Objective: Understand project structure, technology stack, and scope
Steps:
Get Project Overview:
# Get directory structure tree -L 3 -I 'node_modules|target|build|dist|vendor|__pycache__|.git' # Or if tree not available: find . -type d -maxdepth 3 -not -path '*/\.*' -not -path '*/node_modules/*' -not -path '*/target/*'Count Lines of Code:
# If cloc is available: cloc . --exclude-dir=node_modules,target,build,dist,vendor # Or basic count: find . -name '*.rs' -o -name '*.py' -o -name '*.java' -o -name '*.go' -o -name '*.js' -o -name '*.ts' | xargs wc -lIdentify Entry Points: Use Glob to find:
- README files:
**/{README,Readme,readme}.md - Config files:
**/package.json,**/Cargo.toml,**/pom.xml,**/go.mod,**/setup.py - Main entry points:
**/main.*,**/index.*,**/app.*
- README files:
Read Key Files: Use Read tool to analyze:
- README.md (if exists)
- Package/build config files
- Main entry point files
Determine Technology Stack: Based on files found, identify:
- Primary language(s)
- Frameworks used
- Build tools
- Dependencies
Phase 2: Architecture Analysis (10-20 minutes)
Objective: Understand system architecture, modules, and relationships
Steps:
Identify Modules/Packages:
- Rust:
src/subdirectories,Cargo.tomlworkspace members - Python: Top-level directories with
__init__.py - Java: Packages in
src/main/java/ - Go: Directories with
.gofiles - Node.js:
src/orlib/subdirectories - TypeScript: Based on
tsconfig.jsonpaths
- Rust:
Map Dependencies:
- Read import/require/use statements
- Identify internal vs external dependencies
- Build dependency graph
Detect Architectural Patterns: Look for:
- MVC/MVVM patterns
- Layered architecture (controllers, services, repositories)
- Microservices vs monolith
- Event-driven architecture
- Domain-driven design patterns
Identify Core Components:
- API endpoints/routes
- Database models/entities
- Business logic/services
- Utilities/helpers
- Configuration management
Phase 3: Documentation Generation (20-40 minutes)
Objective: Create comprehensive markdown documentation
Create ./docs/ directory structure:
./docs/
├── 1. Project Overview.md
├── 2. Architecture Overview.md
├── 3. Workflow Overview.md
└── 4. Deep Dive/
├── [Component1].md
├── [Component2].md
└── [Component3].md
Document 1: Project Overview.md
Content Structure:
# Project Overview
## What is [Project Name]?
[Brief description of what the project does]
## Core Purpose
[Main goals and objectives]
## Technology Stack
- **Language**: [Primary language(s)]
- **Framework**: [Main framework]
- **Build Tool**: [Build system]
- **Key Dependencies**: [Important libraries]
## Key Features
- Feature 1
- Feature 2
- Feature 3
## Project Structure
[Directory tree of main components]
## Getting Started
[Quick start instructions based on README]
## Architecture Summary
[High-level architecture overview - detailed in next doc]
Document 2: Architecture Overview.md
Content Structure:
# Architecture Overview
## System Context (C4 Level 1)
[Description of system boundaries and external actors]
```mermaid
C4Context
title System Context Diagram
Person(user, "User", "End user of the system")
System(system, "[Project Name]", "[Brief description]")
System_Ext(external1, "External System 1", "[Description]")
Rel(user, system, "Uses")
Rel(system, external1, "Integrates with")
Container Architecture (C4 Level 2)
[Description of major containers/services]
C4Container title Container Diagram Container(app, "Application", "[Tech]", "[Description]") ContainerDb(db, "Database", "[DB Type]", "[Description]") Container(api, "API", "[Tech]", "[Description]") Rel(app, api, "Calls") Rel(api, db, "Reads/Writes")
Component Architecture (C4 Level 3)
[Breakdown of major modules and their relationships]
graph TB
subgraph "Module A"
A1[Component A1]
A2[Component A2]
end
subgraph "Module B"
B1[Component B1]
B2[Component B2]
end
A1 --> B1
A2 --> B2
Architectural Patterns
- Pattern 1: [Description and usage]
- Pattern 2: [Description and usage]
Key Design Decisions
- Decision: [What was decided]
- Rationale: [Why]
- Trade-offs: [Pros/Cons]
Module Breakdown
Module 1: [Name]
- Purpose: [What it does]
- Key Components: [List]
- Dependencies: [What it uses]
Module 2: [Name]
- Purpose: [What it does]
- Key Components: [List]
- Dependencies: [What it uses]
#### Document 3: Workflow Overview.md
**Content Structure**:
```markdown
# Workflow Overview
## Core Workflows
### Workflow 1: [Name]
[Description of workflow]
```mermaid
sequenceDiagram
participant User
participant Frontend
participant Backend
participant Database
User->>Frontend: Action
Frontend->>Backend: API Call
Backend->>Database: Query
Database-->>Backend: Results
Backend-->>Frontend: Response
Frontend-->>User: Display
Steps:
- Step 1 description
- Step 2 description
- Step 3 description
Workflow 2: [Name]
[Similar structure]
Data Flow
flowchart LR Input[Input Data] --> Process1[Process 1] Process1 --> Process2[Process 2] Process2 --> Output[Output]
State Management
[How state is managed in the application]
Error Handling
[Error handling approach]
#### Documents 4+: Deep Dive Components
For each major module/component, create detailed documentation:
```markdown
# Deep Dive: [Component Name]
## Overview
[Detailed description of component]
## Responsibilities
- Responsibility 1
- Responsibility 2
- Responsibility 3
## Architecture
```mermaid
classDiagram
class ComponentA {
+method1()
+method2()
}
class ComponentB {
+method3()
}
ComponentA --> ComponentB : uses
Key Files
file1.ext: [Description]file2.ext: [Description]
Implementation Details
Feature 1
[Code explanation]
Feature 2
[Code explanation]
Dependencies
- Internal: [List]
- External: [List]
API/Interface
[If applicable, document public API]
Testing
[Testing approach for this component]
Potential Improvements
- Improvement 1
- Improvement 2
### Phase 4: Diagram Generation (10-15 minutes)
**Mermaid Diagram Types to Use**:
1. **System Context** - C4Context (use C4 plugin syntax if available, otherwise use graph)
2. **Container Diagram** - C4Container or deployment diagram
3. **Component Relationships** - Graph TB/LR
4. **Sequence Diagrams** - For workflows
5. **Class Diagrams** - For OOP architectures
6. **State Diagrams** - For state machines
7. **ER Diagrams** - For data models
8. **Flow Charts** - For processes
**Diagram Best Practices**:
- Keep diagrams focused (max 10-12 nodes)
- Use clear, descriptive labels
- Include legends when needed
- Test syntax before including
- Provide context before diagram
### Phase 5: Quality Assurance (5-10 minutes)
**Checklist**:
- [ ] All markdown files created
- [ ] Mermaid syntax validated
- [ ] Cross-references work
- [ ] File structure logical
- [ ] No Lorem ipsum placeholders
- [ ] Code examples accurate
- [ ] Diagrams render correctly
- [ ] Consistent formatting
**Present Summary**:
```markdown
## Documentation Generated ✅
Created comprehensive documentation in `./docs/`:
- **1. Project Overview.md** - [X] lines
- Technology stack identified
- Core features documented
- **2. Architecture Overview.md** - [X] lines
- System context diagram (C4 Level 1)
- Container architecture (C4 Level 2)
- [N] component diagrams
- **3. Workflow Overview.md** - [X] lines
- [N] core workflows documented
- [N] sequence diagrams
- **4. Deep Dive/** - [N] component docs
- Detailed implementation documentation
- [N] technical diagrams
**Total**: ~[X] lines of documentation
**Diagrams**: [N] Mermaid diagrams
**Coverage**: [percentage]% of codebase analyzed
Next steps:
- Review generated documentation
- Customize as needed
- Integrate into project README
Advanced Techniques
Language-Specific Patterns
Rust Projects
- Focus on: modules, traits, lifetimes, error handling
- Key files:
Cargo.toml,src/main.rs,src/lib.rs - Document: ownership patterns, async/await usage
Python Projects
- Focus on: packages, classes, decorators, type hints
- Key files:
setup.py,pyproject.toml,__init__.py - Document: virtual env, dependency management
Java Projects
- Focus on: packages, interfaces, annotations
- Key files:
pom.xml,build.gradle, package structure - Document: design patterns, Spring/Jakarta EE usage
JavaScript/TypeScript Projects
- Focus on: modules, components, hooks (if React)
- Key files:
package.json,tsconfig.json - Document: build process, bundling, type system
Large Codebase Strategy
For projects >1000 files:
- Prioritize Core Modules: Focus on main functionality first
- Batch Processing: Analyze 10-20 files at a time
- Progressive Documentation: Generate overview first, details later
- Multiple Passes: Refine documentation in iterations
Context Window Management
Monitor token usage:
- Read files selectively (key files only)
- Use Glob patterns efficiently
- Generate docs incrementally
- Save progress frequently
Error Handling
If you encounter issues:
- File Not Found: Use Glob to locate correct path
- Too Many Files: Filter with specific patterns
- Context Limit: Generate documentation in parts
- Unknown Tech Stack: Focus on file structure and naming conventions
Output Format
Always use markdown with:
- Clear headings (# ## ###)
- Code blocks with language tags
- Mermaid diagrams in code blocks
- Lists for clarity
- Links between documents
Usage Examples
Example 1: Rust CLI Tool
User: "Generate docs for this Rust project"
Response:
- Scan project: Find Cargo.toml, src/main.rs
- Identify: CLI tool using clap
- Generate: Focus on command structure, argument parsing
- Create: Architecture emphasizing CLI workflows
Example 2: Python Web API
User: "Document this FastAPI application"
Response:
- Scan: Find main.py, routes/, models/
- Identify: REST API with database
- Generate: Focus on endpoints, data models
- Create: API documentation with request/response examples
Example 3: JavaScript React App
User: "Create architecture docs for this React app"
Response:
- Scan: src/, components/, public/
- Identify: Component hierarchy, state management
- Generate: Focus on component architecture
- Create: UI/UX flow documentation
Tips for Best Results
- Start Small: Test on small projects first
- Iterate: Refine instructions based on output
- Customize: Adapt templates for your needs
- Version Control: Track documentation with code
- Keep Updated: Regenerate when architecture changes
Limitations
- Context window limits for very large codebases (>10K files)
- May not capture all nuances of complex architectures
- Requires manual review for accuracy
- Mermaid diagram complexity limited
When to Use This Skill
✅ Use When:
- Need comprehensive codebase documentation
- Want C4 model architecture diagrams
- Understanding unfamiliar codebase
- Onboarding new team members
- Preparing technical presentations
- Documentation maintenance
- No Litho/external tools available
❌ Don't Use When:
- Need exact Litho output format
- Working with proprietary/closed-source tools
- Require specific documentation templates
- Have custom documentation workflows
Remember: This skill uses your Claude Code subscription exclusively. No external API calls, no additional costs. All analysis and generation happens within Claude Code's context.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review