## 1. Project Identity

```yaml
project:
  project_name: "RepoScope 🔎"
  core_concept: |
    RepoScope is a web application that retrieves a GitHub repository identifier and generates an interactive, AI-ready report of the repository's contents. Users can view code file details, filter by file extensions, and optionally leverage AI to gain deeper insights and recommendations for any size of repository.
  project_hook: |
    Supercharge your repository analysis with RepoScope 🔎—a modular, web-based tool that provides structured insights, code-level context, and optional AI-driven intelligence for developers. Streamline your workflow with interactive reports, extension-based filtering, and flexible settings to get exactly the information you need—fast.
  key_features:
    - Interactive repository analysis and file filtering
    - AI-assisted code overview (optional)
    - Structured data output for LLM integration
    - Modular design for future expansions
  technical_constraints:
    - "Must be web-based"
    - "Support large repositories without exceeding context limits"
    - "Expose OpenAI-compatible AI endpoint (optional)"
  target_users: |
    Developers, data scientists, and teams who need to integrate repositories with LLMs or quickly extract structured insights from codebases. Ideal for anyone seeking an extensible, AI-ready approach to repo exploration.
```

---

## 2. Technical Architecture Overview

```yaml
architecture:
  frontend:
    core_ui_components:
      - RepositorySearchBar
      - RepositorySummaryPanel
      - CodeFileTable
      - FilterControls
      - SettingsModal
    state_management: |
      Use a lightweight state management approach with React Context or Redux Toolkit. The state holds repository metadata, user filter settings, and AI analysis parameters.
    data_flow_patterns:
      - "Unidirectional data flow with asynchronous actions for fetching repo and analysis data"
      - "Reactive updates triggered by changes to user settings or AI analysis requests"
    user_interactions:
      - "User enters a repo identifier (e.g., 'localhost:5001/kohya-ss/sd-scripts')"
      - "User applies filters (by file extension or code attributes) and can toggle AI analysis"
  
  backend:
    services_structure:
      - RepositoryDataService: Handles interaction with GitHub (or local GitHub mirror) to fetch repo data
      - AIAnalysisService: Processes or delegates code analysis requests to an LLM or local AI
    api_design:
      endpoints:
        - GET /api/repo/:owner/:repo: Fetch repository metadata and file structure
        - POST /api/analyze: Request AI-driven analysis with user-defined parameters
    data_processing:
      - "Extraction of code file info (e.g., lines of code, file extension, short description)"
      - "Optional AI summarization or classification for each file chunk or extension group"
    external_integrations:
      - "GitHub API for repository data"
      - "OpenAI-compatible endpoint for AI analysis"
  
  data:
    storage_solutions:
      - "In-memory or lightweight DB (e.g., SQLite) caching for ephemeral data"
      - "Optional cloud-based object store (e.g., AWS S3) for large files or logs"
    data_models:
      - "RepositoryModel: { owner, repo, branch, files[] }"
      - "FileModel: { path, extension, size, description, ai_summary? }"
    caching_strategy: |
      Implement server-side caching of recently analyzed repositories. Use a short TTL to ensure updates from GitHub are reflected soon, but reduce redundant queries.
    data_flow: |
      1. User requests repo info → Repo data fetched from GitHub → Processed and cached.
      2. (Optional) AI analysis request → Break files into chunks → Summaries/stats returned → Integrated into repository model.
  
  infrastructure:
    deployment_requirements:
      - "Containerized deployment with Docker for consistent environments"
      - "Configure environment variables for GitHub tokens and AI endpoints"
    scaling_considerations:
      - "Horizontal scaling of backend for large, concurrent analysis requests"
      - "Load balancer to route requests based on region or service capacity"
    service_dependencies:
      - "GitHub API"
      - "OpenAI-compatible LLM service"
```

---

## 3. Detailed Component Specifications

```yaml
components:
  - name: "RepositoryDataService"
    purpose: |
      Fetch and process GitHub repository data, including file structures, file metadata, and storing minimal data in a lightweight cache.
    technical_requirements:
      libraries:
        - "Octokit (GitHub REST library) or a similar library"
        - "Lightweight caching (e.g., Node-cache)"
      performance:
        - "Must handle paging for large repos efficiently"
      security:
        - "Use GitHub access tokens securely stored in environment variables"
      integration_points:
        - "Communicates with GitHub API"
    implementation_details:
      data_structures:
        - "RepositoryModel"
        - "FileModel"
      algorithms:
        - "Recursive or paginated fetching to handle large directory trees"
      api_contracts:
        - "Returns a standardized JSON for repo structure"
      error_handling:
        - "Retry on rate limit; respond with 503 if repeated failures"
  
  - name: "AIAnalysisService"
    purpose: |
      Provide an optional AI-driven analysis of repository files, exposing an OpenAI-compatible endpoint.
    technical_requirements:
      libraries:
        - "OpenAI-compatible LLM library (e.g., OpenAI npm client or locally hosted LLM frameworks)"
      performance:
        - "Chunk large files to avoid exceeding context size"
      security:
        - "Only process data from whitelisted repos or after user authentication"
      integration_points:
        - "Interfaces with the LLM (could be cloud-based or self-hosted)"
    implementation_details:
      data_structures:
        - "AnalysisRequest { repoId, files[], maxContextSize, analysisType }"
      algorithms:
        - "File chunking algorithm based on user-defined max tokens or line count"
      api_contracts:
        - "POST /api/analyze (with AnalysisRequest) → returns AnalysisResponse"
      error_handling:
        - "Graceful degradation if AI fails—application remains functional"
  
  - name: "Frontend UI"
    purpose: |
      Present a user-friendly interface for inputting repository details, filtering files, and optionally invoking AI analysis.
    technical_requirements:
      libraries:
        - "React (or Vue/Angular) for the UI"
        - "Redux Toolkit or React Context"
      performance:
        - "Load large file lists lazily to avoid UI blocking"
      security:
        - "Use HTTPS for all data transmission"
      integration_points:
        - "Communicates with the backend endpoints"
    implementation_details:
      data_structures:
        - "React states for user input, repository data, AI analysis results"
      algorithms:
        - "Debounce user input in the search bar to reduce redundant calls"
      api_contracts:
        - "GET /api/repo/:owner/:repo"
        - "POST /api/analyze"
      error_handling:
        - "Display user-friendly messages if the backend is unreachable"
  
  - name: "Settings and Configuration Manager"
    purpose: |
      Enable user-level customization of analysis parameters, filtering options, chunk sizes, and AI usage policies.
    technical_requirements:
      libraries:
        - "JSON-based config management (e.g., config library)"
      performance:
        - "Quick retrieval of user settings with minimal overhead"
      security:
        - "Persist user settings in a secure store, especially if multi-tenant"
      integration_points:
        - "Frontend for user inputs"
        - "Backend to store and retrieve user preferences"
    implementation_details:
      data_structures:
        - "SettingsModel { userId, preferences, defaultValues }"
      algorithms:
        - "Fallback to default if user does not specify settings"
      api_contracts:
        - "GET /api/settings"
        - "POST /api/settings"
      error_handling:
        - "Gracefully handle missing or corrupted settings"
```

---

## 4. Task Breakdown

```yaml
tasks:
  - id: "TASK-001"
    category: "backend"
    description: |
      Implement the RepositoryDataService to fetch repository information from GitHub using paginated requests.
    technical_details:
      required_technologies:
        - "Node.js"
        - "Octokit"
      implementation_approach: |
        1. Integrate Octokit to authenticate with GitHub tokens.
        2. Implement a function to recursively fetch directory/file information.
        3. Store retrieved data in an in-memory cache with a configurable TTL.
      expected_challenges:
        - "Handling large repositories within rate limits"
        - "Dealing with nested directory structures"
      acceptance_criteria:
        - "Successfully fetch a small, medium, and large repo without timeouts"
        - "Return structured JSON with file metadata"
    complexity:
      estimated_loc: 180
      estimated_hours: 6
    dependencies:
      - "TASK-000"
  
  - id: "TASK-002"
    category: "backend"
    description: |
      Develop the AIAnalysisService that communicates with an OpenAI-compatible LLM. Support file chunking and analyzing code files.
    technical_details:
      required_technologies:
        - "Node.js"
        - "OpenAI Node Client (or alternative LLM library)"
      implementation_approach: |
        1. Implement chunking logic based on user settings (max token/line constraints).
        2. Define a standardized request payload for /api/analyze.
        3. Return summarized or classified data for each file chunk.
      expected_challenges:
        - "Token limit handling"
        - "Managing cost and performance for large repos"
      acceptance_criteria:
        - "AI analysis completes within a set time for typical repos"
        - "Detailed summaries returned for each file chunk"
    complexity:
      estimated_loc: 150
      estimated_hours: 7
    dependencies:
      - "TASK-001"
  
  - id: "TASK-003"
    category: "frontend"
    description: |
      Build the React-based UI (RepositorySearchBar, RepositorySummaryPanel, CodeFileTable, FilterControls).
    technical_details:
      required_technologies:
        - "React"
        - "Typescript (optional)"
      implementation_approach: |
        1. Create reusable components for searching and displaying file details.
        2. Implement a filter panel to refine displayed files by extension or AI-based tags.
        3. Integrate with the backend to fetch repository data and trigger analysis.
      expected_challenges:
        - "Ensuring responsive design for large file lists"
        - "Maintaining consistent state between multiple components"
      acceptance_criteria:
        - "User can successfully filter files by extension"
        - "Displays repository metadata on search"
    complexity:
      estimated_loc: 150
      estimated_hours: 6
    dependencies:
      - "TASK-001"
  
  - id: "TASK-004"
    category: "frontend"
    description: |
      Implement the AI analysis toggle and display AI results inline with the file data.
    technical_details:
      required_technologies:
        - "React / Redux Toolkit or Context"
      implementation_approach: |
        1. Add a button or switch to enable AI-based analysis per repository.
        2. Show aggregated AI analysis on each file or chunk basis.
        3. Provide loading states and handle errors gracefully.
      expected_challenges:
        - "Handling partial or delayed AI responses"
        - "Balancing UI responsiveness with chunk-based updates"
      acceptance_criteria:
        - "User can enable/disable AI analysis with a single click"
        - "Analysis results appear next to relevant file data"
    complexity:
      estimated_loc: 120
      estimated_hours: 5
    dependencies:
      - "TASK-002"
      - "TASK-003"

  - id: "TASK-005"
    category: "backend"
    description: |
      Create and integrate the Settings and Configuration Manager, storing user preferences for chunk sizes, AI usage, and file extension filters.
    technical_details:
      required_technologies:
        - "Node.js"
        - "A simple JSON-based or database-based settings store"
      implementation_approach: |
        1. Define the SettingsModel for user preferences.
        2. Create endpoints /api/settings GET and POST.
        3. Integrate these settings into the AIAnalysisService and RepositoryDataService flow.
      expected_challenges:
        - "Synchronization of local settings changes with database"
        - "Validation of user preferences"
      acceptance_criteria:
        - "User can update and retrieve their personal settings"
        - "Analysis respects updated user preferences"
    complexity:
      estimated_loc: 160
      estimated_hours: 6
    dependencies:
      - "TASK-001"
      - "TASK-002"
```

---

## 5. Implementation Dependencies

1. **GitHub API (Octokit or equivalent)**  
   - Required for fetching repository data and file structure.
   - Must handle authentication tokens and rate-limiting strategies.

2. **OpenAI-Compatible LLM**  
   - Optional but recommended for AI analysis features.
   - Could be replaced or supplemented by a self-hosted model if desired.

3. **Caching Layer**  
   - In-memory or lightweight DB (e.g., Redis, SQLite) for storing fetched repo data and partially analyzed results.
   - Reduces repeated fetches from GitHub.

4. **Frontend Framework**  
   - React or a similar library with robust state management (e.g., Redux Toolkit).
   - Must facilitate modular, easily testable UI components.

5. **Infrastructure Services**  
   - Container orchestration (e.g., Docker, Kubernetes).
   - Reverse proxy/load balancer (e.g., Nginx) for production.

---

## Why This App Is Going To Be Amazing

RepoScope 🔎 not only offers a fast and efficient way to analyze repositories but also provides an optional AI-driven layer to handle everything from code summarization to advanced classification—without limiting usability for those who prefer a standalone experience. Its modular design means you can easily add new features (like a prompt manager or advanced analytics) down the road, ensuring it grows with your needs. With its flexible architecture, emphasis on performance, and optional AI enhancements, RepoScope promises to be a game-changer in how developers understand, document, and leverage their codebases.