init

.github/prompts/new.prompt.md

@@ -1,26 +1,29 @@
 ---
-mode: 'agent'
-description: 'Create a proposal'
+mode: "agent"
+description: "Create a proposal"
 ---
 
 I want to build an app. My idea is a mobile app called GitBey that takes a user's github handle and returns 5 Beyonce songs based on the vibe of their last 50 commits. User should be able to play the songs in the app.
 
 Let's join forces to make this idea into a simple, yet impactful proposal.
 
 FIRST:
+
 - Clarify any areas of my proposal that may need more details
 - Suggest new requirements based on the functionality provided
 - Consider edge cases that may not be included in my original proposal.
 - Organize requirements logically, and break them down into units that would make sense as user stories.
 - Raise any important high-level technical considerations, like platforms, languages, frameworks, and overall architecture details
 
 NEXT:
+
 - Iterate with me until I tell you I am satisfied.
 
 FINALLY:
+
 - Once I tell you I am ready, create a plan (or update an existing plan) and use the #tool:github create issue tool to create an issue with this proposal and title: Spec.
 
-The correct repo is githubevents/gitbey
+The correct repo is jcodes101/gitbey
 
 ```
 # Project Name
@@ -37,4 +40,4 @@ The correct repo is githubevents/gitbey
 -Provide a basic MVP scope that could be delivered quickly to validate this idea.
 ```
 
-Output your plan in the directory "../../docs/idea.md"
+Output your plan in the directory "../../docs/idea.md"

.vscode/mcp.json

@@ -0,0 +1,42 @@
+{
+  // 💡 Inputs are prompted on first server start, then stored securely by VS Code.
+  "inputs": [
+    {
+      "type": "promptString",
+      "id": "perplexity-key",
+      "description": "Perplexity API Key",
+      "password": true
+    },
+    {
+      "type": "promptString",
+      "id": "github_token",
+      "description": "GitHub Personal Access Token",
+      "password": true
+    }
+  ],
+  "servers": {
+    // https://github.com/ppl-ai/modelcontextprotocol/
+    "Perplexity": {
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "server-perplexity-ask"],
+      "env": {
+        "PERPLEXITY_API_KEY": "${input:perplexity-key}"
+      }
+    },
+    "github": {
+      "command": "docker",
+      "args": [
+        "run",
+        "-i",
+        "--rm",
+        "-e",
+        "GITHUB_PERSONAL_ACCESS_TOKEN",
+        "ghcr.io/github/github-mcp-server"
+      ],
+      "env": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "${input:github_token}"
+      }
+    }
+  }
+}

docs/plans/gitbey-app.md

@@ -0,0 +1,78 @@
+# Implementation Plan: GitBey App
+
+## Overview
+
+This plan outlines the step-by-step implementation for the GitBey mobile app, which analyzes a user's GitHub commit history and generates personalized Beyoncé playlists based on coding mood.
+
+---
+
+## Step 1: GitHub API Integration
+
+**Objective:** Fetch a user's recent commit history from GitHub.
+
+- Add HTTP and dotenv dependencies.
+- Create a GitHub service class to fetch public events for a username.
+- Use a GitHub token for authenticated requests.
+- Parse and return commit data (limit to 50 most recent commits).
+- Handle errors (network, invalid username, rate limits).
+- UI: Input for username, button to fetch, display commit count and sample message.
+- **User intervention:** Provide a valid GitHub token in `.env`.
+
+---
+
+## Step 2: Sentiment Analysis of Commits
+
+**Objective:** Analyze commit messages to determine coding mood.
+
+- Add a sentiment analysis package or implement a simple algorithm.
+- Preprocess commit messages (lowercase, remove punctuation, etc.).
+- For each commit, compute a sentiment score (positive/negative/neutral).
+- Aggregate scores to determine overall mood (e.g., happy, frustrated, productive).
+- UI: Display mood summary and example analysis.
+- **User intervention:** None required.
+
+---
+
+## Step 3: AI Playlist Generation
+
+**Objective:** Generate Beyoncé song recommendations based on commit sentiment.
+
+- Integrate with an AI model (e.g., OpenAI, GitHub Models API) for song selection.
+- Design prompt templates for the AI (provide mood and context).
+- Send request to AI API and parse response (list of 5 songs).
+- UI: Display recommended songs with mood context.
+- **User intervention:** Provide AI API key in `.env` if required.
+
+---
+
+## Step 4: Audio Integration (Optional)
+
+**Objective:** Enable song playback within the app.
+
+- Integrate with a music API (e.g., Spotify) for audio previews.
+- Set up developer credentials for the music API.
+- Implement audio playback UI (play, pause, etc.).
+- UI: Show song list with play buttons.
+- **User intervention:** Register for music API credentials and add to `.env`.
+
+---
+
+## Step 5: Testing & Debugging
+
+**Objective:** Ensure all features work as expected.
+
+- Write and run unit/widget tests for each feature.
+- Test on iOS and Android simulators.
+- Debug and fix issues as needed.
+- **User intervention:** Run tests and report bugs.
+
+---
+
+## Step 6: Polish & Documentation
+
+**Objective:** Finalize app for release.
+
+- Refine UI/UX for a beautiful, modern look.
+- Add documentation and usage instructions.
+- Prepare for App Store/Play Store submission.
+- **User intervention:** Review and approve final app.

docs/research.md

@@ -0,0 +1,62 @@
+# GitBey Research Summary
+
+## Idea Overview
+
+GitBey is a mobile app that takes a user's GitHub handle, analyzes the vibe of their last 50 commits, and recommends 5 Beyoncé songs accordingly. Users can play the songs within the app.
+
+## Customers
+
+- Software developers and engineers who use GitHub
+- Fans of Beyoncé and pop music
+- Tech-savvy music lovers
+- People interested in novelty or entertainment apps that blend productivity and pop culture
+
+## Problem Statements
+
+- Developers often seek fun, personalized ways to reflect on their coding activity
+- There is a lack of apps that combine code analysis with music recommendations
+- Music can help improve mood and productivity, but current music apps do not personalize based on coding activity
+
+## Possible Competitors
+
+- Spotify Wrapped (personalized music stats, but not code-based)
+- GitHub Skyline (visualizes commits, but no music integration)
+- Mood-based music apps (e.g., Moodagent, but not tied to coding)
+- No direct competitor that combines GitHub commit analysis with music recommendations
+
+## Unmet Needs
+
+- Personalized music recommendations based on developer activity
+- Fun, shareable insights that blend coding and pop culture
+- In-app music playback tied to unique, data-driven experiences
+
+## Differentiators
+
+- Unique combination of GitHub data analysis and Beyoncé music
+- Novelty factor: appeals to both developers and Beyoncé fans
+- Shareable results (potential for social media virality)
+- Potential for expansion to other artists or music genres
+
+## Marketing
+
+- Target developer communities (GitHub, Stack Overflow, Reddit)
+- Collaborate with Beyoncé fan groups and music forums
+- Leverage social media for shareable results (e.g., "My GitBey Vibe")
+- Influencer partnerships in tech and music spaces
+
+## Business Models
+
+- Freemium: free basic use, paid premium features (e.g., more song options, deeper analysis)
+- Affiliate links to music streaming services
+- Sponsored content or partnerships with music labels
+- In-app ads (if user base is large enough)
+
+## Next Steps
+
+- Validate interest with target users
+- Explore technical feasibility (GitHub API, music streaming integration)
+- Prototype core features and test user experience
+
+---
+
+This summary provides a foundation for further research, ideation, and development of the GitBey app.

docs/specs/ai-playlist-generation.md

@@ -0,0 +1,24 @@
+# Feature Spec: AI Playlist Generation
+
+## Objective
+
+Generate Beyoncé song recommendations based on commit sentiment.
+
+## Steps
+
+1. Integrate with an AI model (e.g., OpenAI, GitHub Models API) for song selection.
+2. Design prompt templates for the AI (provide mood and context).
+3. Send request to AI API and parse response (list of 5 songs).
+4. UI: Display recommended songs with mood context.
+
+## Pseudocode
+
+- Given mood label:
+  - Create prompt for AI
+  - Send to AI API
+  - Parse response for 5 songs
+- Display songs in UI
+
+## User Intervention
+
+- Provide AI API key in `.env` if required.

docs/specs/audio-integration.md

@@ -0,0 +1,23 @@
+# Feature Spec: Audio Integration (Optional)
+
+## Objective
+
+Enable song playback within the app.
+
+## Steps
+
+1. Integrate with a music API (e.g., Spotify) for audio previews.
+2. Set up developer credentials for the music API.
+3. Implement audio playback UI (play, pause, etc.).
+4. UI: Show song list with play buttons.
+
+## Pseudocode
+
+- For each recommended song:
+  - Fetch audio preview URL
+  - Display play button
+  - On play, stream audio
+
+## User Intervention
+
+- Register for music API credentials and add to `.env`.

docs/specs/github-api-integration.md

@@ -0,0 +1,26 @@
+# Feature Spec: GitHub API Integration
+
+## Objective
+
+Fetch a user's recent commit history from GitHub.
+
+## Steps
+
+1. Add HTTP and dotenv dependencies to the project.
+2. Create a GitHub service class to fetch public events for a username.
+3. Use a GitHub token for authenticated requests.
+4. Parse and return commit data (limit to 50 most recent commits).
+5. Handle errors (network, invalid username, rate limits).
+6. UI: Input for username, button to fetch, display commit count and sample message.
+
+## Pseudocode
+
+- User enters GitHub username
+- On button press:
+  - Call GitHubService.fetchRecentCommits(username)
+  - If success: display commit count and sample message
+  - If error: show error message
+
+## User Intervention
+
+- Provide a valid GitHub token in `.env`.

docs/specs/sentiment-analysis.md

@@ -0,0 +1,26 @@
+# Feature Spec: Sentiment Analysis of Commits
+
+## Objective
+
+Analyze commit messages to determine coding mood.
+
+## Steps
+
+1. Add a sentiment analysis package or implement a simple algorithm.
+2. Preprocess commit messages (lowercase, remove punctuation, etc.).
+3. For each commit, compute a sentiment score (positive/negative/neutral).
+4. Aggregate scores to determine overall mood (e.g., happy, frustrated, productive).
+5. UI: Display mood summary and example analysis.
+
+## Pseudocode
+
+- For each commit message:
+  - Clean/preprocess text
+  - Compute sentiment score
+- Aggregate all scores
+- Map aggregate to mood label
+- Display mood summary in UI
+
+## User Intervention
+
+- None required.

flutter_application_1/.gitignore

@@ -0,0 +1,45 @@
+# Miscellaneous
+*.class
+*.log
+*.pyc
+*.swp
+.DS_Store
+.atom/
+.build/
+.buildlog/
+.history
+.svn/
+.swiftpm/
+migrate_working_dir/
+
+# IntelliJ related
+*.iml
+*.ipr
+*.iws
+.idea/
+
+# The .vscode folder contains launch configuration and tasks you configure in
+# VS Code which you may wish to be included in version control, so this line
+# is commented out by default.
+#.vscode/
+
+# Flutter/Dart/Pub related
+**/doc/api/
+**/ios/Flutter/.last_build_id
+.dart_tool/
+.flutter-plugins
+.flutter-plugins-dependencies
+.pub-cache/
+.pub/
+/build/
+
+# Symbolication related
+app.*.symbols
+
+# Obfuscation related
+app.*.map.json
+
+# Android Studio will place build artifacts here
+/android/app/debug
+/android/app/profile
+/android/app/release