
# Unfamiliar Pipeline Test

**Timestamp:** 2026-03-14 00:40:06 UTC
**Target repo:** `/home/erinmeaker/Documents/source/lifeLevel`

## Phase 3: Static Analysis

Files: 61
Modules: 30
Entry points: 2

Top scored files:
  backend/tests/conftest.py: 150 (entry point, hub (imported by 6 files))
  backend/app.py: 100 (entry point)
  backend/config.py: 50 (hub (imported by 3 files))
  backend/database.py: 50 (hub (imported by 15 files))
  backend/tests/test_activities_queries.py: 50 (complex (score: 73), test file)
Module size distribution: {1: 24, 2: 3, 4: 1, 6: 1, 21: 1}
Singleton modules: 24
Total import edges: 54
Edges per file: 0.89
Provider: glm
Model override: glm-4.7-Flash

## Phase 6: Briefing Generation

Token budget: 8,000
Context size: 28,767 chars (~7,191 tokens)
Model class: ChatZhipuAI
Mission model class: ChatZhipuAI
Max output tokens: 4095

### Briefing Result

Context tokens: ~7,191
Summary length: 869 chars
Tech stack: ['Flask (Python web framework)', 'SQLite (database)', 'React (frontend framework)', 'TypeScript (type-safe frontend)', 'Vite (build tool)', 'pytest (testing)', 'Flask-CORS (cross-origin resource sharing)', 'Click (CLI commands)']
Modules described: 30
Security posture: The codebase demonstrates good security practices with configuration loaded exclusively from environment variables, no hardcoded secrets, and configurable database paths. Safety checks are implemented

**Full summary:**

```
LifeLevel is a full-stack skill tracking application that helps users log activities, earn XP, and track progress across different skill branches. The backend is built with Flask and SQLite, providing RESTful APIs for activities, logging, and progress tracking. The frontend is a React/TypeScript application with a component-based architecture featuring activity cards, progress visualizations, and XP graphs. The system uses a gamified XP system with levels and branches (Balance, Core, Mobility, Strength, Coordination, Cardio) for different skating skills. The application includes comprehensive testing with pytest covering queries and routes, and CLI commands for database initialization, seeding, backup, and testing database management. Configuration is managed through environment variables with separate database paths for production and testing environments.
```

  [0] Backend Application Core: Main backend module containing the Flask application factory, configuration mana
  [1] Activities UI Components: React component library for displaying and interacting with activities, includin
  [2] Progress UI Components: React components for visualizing user progress, including ProgressOverview, Prog
  [3] Activities Frontend Feature: Frontend API client and TypeScript type definitions for the activities feature, 
  [4] Progress Frontend Feature: Frontend API client and TypeScript type definitions for the progress feature, pr
  [5] Shared UI Components: Reusable UI components including ErrorBoundary, ErrorState, and Icon used across
  [6] Activities Backend Feature: Backend API routes and queries for managing activities, including filtering by b
  [7] Logging Backend Feature: Backend API routes and queries for logging activity completion, tracking log ent
  [8] Progress Backend Feature: Backend API routes and queries for calculating and retrieving progress data, inc
  [9] Shared Backend Utilities: Backend utilities used across features, including level calculation logic and he
  [10] Database Models: Data models for skills, branches, levels, activities, log entries, and XP totals
  [11] Database Schema & Migrations: Database schema definitions and migration management system for creating and upd
  [12] Backend Test Suite: Comprehensive test suite covering activities queries and routes, logging operati
  [13] Shared Frontend Utilities: Shared frontend utilities including constants, helper functions for video URLs, 
  [14] Frontend Application Entry: Main React application entry point with App component, routing, state management
  [15] backend/requirements.txt: Description not provided by LLM.
  [16] backend/tests/__init__.py: Description not provided by LLM.
  [17] frontend/.prettierrc: Description not provided by LLM.
  [18] frontend/eslint.config.js: Description not provided by LLM.
  [19] frontend/package-lock.json: Description not provided by LLM.
  [20] frontend/package.json: Description not provided by LLM.
  [21] frontend/src/App.tsx: Description not provided by LLM.
  [22] frontend/src/features/activities/components/LevelUpOverlay.tsx: Description not provided by LLM.
  [23] frontend/src/features/progress/components/CompactProgressBar.tsx: Description not provided by LLM.
  [24] frontend/src/main.tsx: Description not provided by LLM.
  [25] frontend/src/shared/branchColors.ts: Description not provided by LLM.
  [26] frontend/src/shared/components/ErrorState.tsx: Description not provided by LLM.
  [27] frontend/src/shared/constants.ts: Description not provided by LLM.
  [28] frontend/src/shared/utils.ts: Description not provided by LLM.
  [29] frontend/vite.config.ts: Description not provided by LLM.
Cooldown 60s before mission generation...

## Phase 7: Mission Generation

Created fresh model for missions: ChatZhipuAI
Categories: ['architecture']
Token budget per category: ~1,142

### Category: architecture

5 missions, 1 chains

### The Flask Factory Blueprint
**Difficulty:** quick

*The city never sleeps, and neither does this Flask application. I'm looking at the factory blueprint that builds the entire backend from scratch. Every route, every extension—it all starts here.*

Investigate the Flask application factory pattern in the backend, understanding how the app is created, configured, and initialized with routes and extensions.

**Files to investigate:**
- `backend/app.py:1-50` — Contains the create_app factory function that initializes the Flask application with configuration, extensions, and route registration.

**Completion:** Identify the create_app function, list all registered routes, and explain how configuration is loaded from environment variables.

*The factory blueprint is clear now. The app is built with care, configured with precision, and ready to serve requests. The foundation is solid, and the structure is sound.*

### Database Connection Shadows
**Difficulty:** quick

*The database is the heart of this operation, pumping data through the veins of the application. But shadows lurk in the connection pool—how is this data accessed safely?*

Trace the database connection patterns used throughout the application, understanding how SQLite is initialized, managed, and accessed across different modules.

**Files to investigate:**
- `backend/database.py:1-60` — Contains the database initialization and connection management logic used throughout the application.

**Completion:** Locate the database initialization code, identify the connection pool pattern, and explain how the database is accessed from different features.

*The connection shadows are revealed. The database is managed with care, connections are handled properly, and the data flows where it needs to go. The heart beats steady.*

### Activity Routes Undercover
**Difficulty:** medium
**Prerequisites:** The Flask Factory Blueprint

*The activity routes are undercover agents, moving data between the frontend and backend. I need to know their cover stories—the endpoints, the payloads, the filtering logic.*

Investigate the activity API routes to understand how activities are exposed to the frontend, including filtering, listing, and data retrieval patterns.

**Files to investigate:**
- `backend/features/activities/routes.py:1-80` — Defines the RESTful API routes for activities, including endpoints for listing, filtering, and retrieving activity data.

**Completion:** Map out all activity-related endpoints, explain the request/response flow, and identify how activities are filtered by branch and form.

*The activity routes are exposed. Every endpoint is accounted for, every payload is understood, and the filtering logic is clear. The agents are in position, ready to serve.*

### Progress Calculation Patterns
**Difficulty:** medium
**Prerequisites:** The Flask Factory Blueprint

*Progress is what we're chasing, but the calculation patterns are hidden in the shadows. I need to follow the logic from queries to results, understanding how skill advancement is measured.*

Analyze the progress calculation logic to understand how skill progress, branch breakdowns, and XP history are computed and retrieved.

**Files to investigate:**
- `backend/features/progress/queries.py:1-60` — Contains the query logic for calculating and retrieving progress data, including overall skill progress and branch-specific breakdowns.

**Completion:** Trace the progress calculation flow, identify the query patterns used for retrieving progress data, and explain how branch-specific metrics are calculated.

*The progress calculation patterns are clear now. The queries are efficient, the logic is sound, and the results are accurate. The path forward is illuminated.*

### XP Calculation Secrets
**Difficulty:** deep_dive
**Prerequisites:** Database Connection Shadows

*XP is the currency of this city, and the calculation secrets are guarded closely. I'm going to crack the code—how much is earned, when does it level up, and what's the bonus?*

Deep dive into the XP calculation utilities to understand the gamification logic, including level thresholds, bonus XP, and reward distribution patterns.

**Files to investigate:**
- `backend/features/logs/queries.py:1-80` — Contains the XP calculation logic, including level thresholds, bonus XP formulas, and reward distribution patterns.

**Completion:** Trace the XP calculation logic from log entry to level up, identify the threshold formulas, and explain how bonus XP is awarded.

*The XP calculation secrets are mine. The formulas are understood, the thresholds are known, and the bonus logic is clear. The gamification system is transparent now.*

## Summary

Total missions generated: 5
Categories tested: 1
Difficulty breakdown: {'quick': 2, 'medium': 2, 'deep_dive': 1}
Root missions (no prereqs): 2