
# Unfamiliar Pipeline Test

**Timestamp:** 2026-03-14 00:59:42 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: 795 chars
Tech stack: ['Python (Flask)', 'SQLite', 'TypeScript', 'React', 'Vite', 'pytest', 'Flask-CORS']
Modules described: 30
Security posture: The codebase demonstrates good security practices with configuration loaded from environment variables rather than hardcoded values. Database paths are configurable via environment variables. CLI comm

**Full summary:**

```
LifeLevel is a full-stack fitness tracking application that allows users to log activities, earn XP, and track progress across different skill branches. The backend is built with Flask and uses SQLite for data persistence, organized into feature modules for activities, logs, and progress. The frontend is a React/TypeScript application built with Vite that provides interactive UI components for viewing activities, logging progress, and visualizing XP history. The system supports branching skills into categories (Balance, Core, Mobility, etc.) with level progression based on XP thresholds. Testing is implemented with pytest using in-memory SQLite databases. The application uses environment-based configuration for database paths, CORS settings, and environment modes (production/testing).
```

  [0] Backend Application Core: Flask application factory, configuration management, database connection handlin
  [1] Activities Frontend Components: React components for displaying and interacting with activities including Activi
  [2] Progress Frontend Components: React components for visualizing progress including ProgressOverview, BranchBrea
  [3] Activities Frontend Feature: Activities API client and TypeScript type definitions for fetching activities, l
  [4] Progress Frontend Feature: Progress API client and TypeScript type definitions for fetching overall progres
  [5] Shared Frontend Components: Reusable UI components including ErrorBoundary for error handling and Icon compo
  [6] Backend Activities Feature: Backend module handling activity queries (filtering by branch, exercise form, le
  [7] Backend Logs Feature: Backend module for creating log entries, calculating XP totals, and managing use
  [8] Backend Progress Feature: Backend module for calculating levels based on XP thresholds, fetching progress 
  [9] Backend Shared Utilities: Shared backend functions including level calculation helper and database connect
  [10] Backend Tests: Comprehensive test suite using pytest with in-memory SQLite fixtures, covering a
  [11] Database Models: Data models for skills, branches, levels, activities, log entries, and XP totals
  [12] Database Schema: SQL migration scripts and schema management functions for creating and applying 
  [13] Database Seeding: Functions to populate the database with initial reference data including skills,
  [14] Shared Frontend Utilities: Utility functions for building video URLs, formatting dates, calculating progres
  [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', 'domain_business_logic', 'data_flow', 'dependencies', 'conventions', 'testing', 'technical_debt']
Token budget per category: ~1,142

### Category: architecture

6 missions, 2 chains

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

*The city lights flicker as you trace the path from the first request to the Flask application. Every great detective knows you have to start at the beginning—where the case is born.*

Investigate how the Flask application is instantiated, configured, and exposed through API routes.

**Files to investigate:**
- `backend/app.py:35-35` — API route registration point showing how the app instance is exposed

**Completion:** Identify the app factory function, its configuration settings, and the API route registration pattern.

*You've traced the application's origin. The Flask factory is in place, routes are registered, and the city is ready to serve requests. Case closed.*

### The Database Connection
**Difficulty:** quick

*Behind every great application lies a hidden vault. You're about to crack the code that connects the app to its data—where secrets are stored and retrieved.*

Examine the database connection management, configuration, and how the SQLite database is initialized and managed.

**Files to investigate:**
- `backend/database.py` — Central database connection management module imported by 15 files
- `backend/config.py` — Configuration settings for database paths and environment modes

**Completion:** Locate the database connection function, understand how SQLite is configured, and identify the CLI commands for database operations.

*The database connection is secure. CLI commands are in place for initialization and backup. The vault is locked but accessible. The investigation continues.*

### The Activity Feature
**Difficulty:** medium

*The streets are filled with activities—some legitimate, some not. You need to understand how the system filters and presents them to the public.*

Investigate the activities feature module, including query logic for filtering activities and API routes for listing them.

**Files to investigate:**
- `backend/features/activities/queries.py` — Query logic for filtering activities by branch, exercise form, and level
- `backend/features/activities/routes.py` — API routes for listing activities and daily tasks

**Completion:** Understand how activities are queried by branch, exercise form, and level, and how the API endpoints are structured.

*The activity feature is clear. Queries filter by branch and level, routes serve the data. The system knows exactly what it's serving. Another piece of the puzzle falls into place.*

### The Log Entry
**Difficulty:** medium

*Every action leaves a trace. You're tracking the logs feature—where users' movements are recorded and their progress is tallied.*

Examine the logs feature module, focusing on how log entries are created, XP totals are calculated, and activity history is managed.

**Files to investigate:**
- `backend/features/logs/__init__.py` — Core logs feature module for creating log entries and calculating XP totals

**Completion:** Identify the log entry creation logic, XP calculation mechanisms, and how user activity history is tracked.

*The logs are in order. Entries are created, XP is calculated, history is preserved. The trail of user activity is clear. The investigation moves forward.*

### The Level Progression
**Difficulty:** medium

*Progress is the currency of this city. You're tracking the progress feature—where XP thresholds determine a user's rank and standing.*

Investigate the progress feature module, understanding how levels are calculated based on XP thresholds and progress data is fetched.

**Files to investigate:**
- `backend/features/progress/__init__.py` — Progress feature module for calculating levels and fetching progress data

**Completion:** Understand the level calculation logic, XP threshold progression, and how progress data is retrieved for overall and branch-specific tracking.

*The level progression is clear. XP thresholds define the path, progress data is fetched for tracking. The system knows exactly where each user stands. The case is taking shape.*

### The Test Suite
**Difficulty:** deep_dive
**Prerequisites:** The Flask Factory, The Database Connection

*The city has its secrets, but tests have theirs. You're diving into the test suite—where the system's integrity is verified in the shadows.*

Examine the testing architecture using pytest with in-memory SQLite databases, understanding how tests are structured and fixtures work.

**Files to investigate:**
- `backend/tests/__init__.py` — Test suite initialization and configuration
- `backend/tests/conftest.py:354-354` — API route and app instance setup for testing

**Completion:** Identify the test fixtures, understand how in-memory databases are configured for testing, and trace the test coverage for core modules.

*The test suite is comprehensive. Fixtures are in place, databases are isolated, coverage is thorough. The system's integrity is confirmed. The investigation is complete.*

### Category: domain_business_logic

Cooldown 60s...
5 missions, 1 chains

### The Leveling Up Formula
**Difficulty:** quick

*The numbers don't lie, but they do hide in plain sight. You're looking at a system where every point counts, where the difference between a novice and a master is just a matter of calculation. Someone's been crunching the numbers, and you need to find the formula that turns raw XP into rank and prestige.*

Investigate how the system calculates user levels based on accumulated XP and determines the thresholds required to advance to the next level.

**Files to investigate:**
- `backend/features/level_calculator.py` — Contains the core level calculation logic and XP threshold formulas.
- `backend/tests/test_level_calculator.py` — Test cases that validate the level calculation logic and reveal the underlying formulas.

**Completion:** Identify the mathematical formula used to calculate levels from XP and explain how the XP thresholds increase with each level.

*The formula is laid bare now. It's a simple progression, a staircase where each step requires more effort than the last. The system rewards persistence, but it also demands growth. You've seen how the math works, and now you understand exactly what it takes to climb the ladder.*

### The Activity Ledger
**Difficulty:** quick

*The database is a ledger of movement, a record of every exercise ever logged. But finding what you need in that sea of data takes skill. You're looking at queries that sift through activities, separating the simple from the complex, the beginner from the expert. Someone's been organizing this chaos, and you need to understand how.*

Trace how activities are retrieved from the database, filtered by branch, exercise form, and level requirements.

**Files to investigate:**
- `backend/features/activities/queries.py` — Contains the SQL queries for retrieving and filtering activities.
- `backend/features/activities/routes.py` — API routes that expose activity query functionality to the frontend.
- `backend/tests/test_activities_queries.py` — Test cases that verify activity query logic and filtering behavior.

**Completion:** Explain the query logic used to filter activities and identify how the system handles different activity categories and difficulty levels.

*The ledger is organized now, its contents laid out in logical order. You see how the system separates activities by branch and difficulty, how it presents the right challenge to the right user. The queries are clean, the logic sound, and you understand exactly how the database serves up the data you need.*

### The XP Trail
**Difficulty:** medium
**Prerequisites:** The Activity Ledger

*Every action leaves a trace, and in this system, that trace is worth something. You're looking at the moment an activity becomes a log, the instant it starts counting toward a user's total. But there's more to it than simple addition—there's calculation, there's weighting, there's the difference between a casual rep and a true effort. Find the trail before it goes cold.*

Follow the path of user activity logs from creation to XP calculation, understanding how each action contributes to the user's total.

**Files to investigate:**
- `backend/features/logs/queries.py` — Contains the logic for creating log entries and calculating XP totals.
- `backend/features/logs/routes.py` — API routes that handle log creation and XP calculation requests.
- `backend/tests/test_log_queries.py` — Test cases that validate log creation and XP calculation logic.

**Completion:** Explain the complete flow of creating a log entry and how the system calculates the XP awarded, including any multipliers or bonuses.

*The trail is complete now, from the first log entry to the final XP calculation. You've seen how the system rewards effort, how it weighs different activities, and how it builds a user's progress over time. The logic is sound, the math is sound, and you understand exactly what it takes to earn your keep in this system.*

### The Progress Report
**Difficulty:** medium
**Prerequisites:** The XP Trail

*Numbers don't tell the whole story, but they tell enough. You're looking at a system that takes scattered data—activities logged, XP earned, levels achieved—and weaves it into a coherent narrative of progress. Someone's been building a report, and you need to understand how they're piecing together the fragments to show the bigger picture.*

Examine how the system aggregates progress data across activities, logs, and XP totals to provide a comprehensive user progress overview.

**Files to investigate:**
- `backend/features/progress/queries.py` — Contains the logic for aggregating progress data from activities and logs.
- `backend/features/progress/routes.py` — API routes that expose progress data to the frontend.
- `backend/tests/test_progress_queries.py` — Test cases that verify progress data aggregation logic.

**Completion:** Describe the data aggregation logic used to calculate overall progress and explain how it combines data from multiple sources.

*The report is complete now, its pieces fitting together into a coherent whole. You've seen how the system aggregates data from multiple sources, how it calculates progress percentages, and how it presents a comprehensive view of user achievement. The logic is sound, the data is sound, and you understand exactly what it takes to build a true progress report.*

### The Branching Path
**Difficulty:** deep_dive
**Prerequisites:** The Progress Report

*The path diverges here. You're looking at a system where progress isn't just a single line—it's a network of branches, each with its own challenges and rewards. Someone's been tracking these separate paths, and you need to understand how the system weaves them together into a cohesive whole. The branches are distinct, but they're connected, and you need to see the connections.*

Investigate how the system tracks progress across different skill branches (Balance, Core, Mobility, etc.) and calculates branch-specific achievements.

**Files to investigate:**
- `backend/features/progress/queries.py` — Contains the logic for calculating branch-specific progress and XP history.
- `backend/tests/test_progress_queries.py` — Test cases that validate branch-specific progress tracking logic.

**Completion:** Explain how branch-specific progress is calculated and identify the relationship between branch activities and overall XP progression.

*The branches are mapped now, their paths clear and their connections visible. You've seen how the system tracks progress across different skill areas, how it rewards specialization, and how it builds a comprehensive profile of user development. The logic is sound, the structure is sound, and you understand exactly what it takes to navigate the branching path.*

### Category: data_flow

Cooldown 60s...
Failed: Failed to generate missions: The read operation timed out (category 'data_flow', after 5 attempt(s))

### Category: dependencies

Cooldown 60s...
Failed: Failed to generate missions: The read operation timed out (category 'dependencies', after 5 attempt(s))

### Category: conventions

Cooldown 60s...
6 missions, 1 chains

### The Configuration Blueprint
**Difficulty:** quick

*The city never sleeps, and neither does this configuration file. Every decision the application makes is written here, in the shadows of environment variables and secret paths.*

Investigate how the backend application manages environment-specific settings, database paths, and operational modes.

**Files to investigate:**
- `backend/config.py` — Contains all environment-based configuration including database paths, CORS settings, and environment modes

**Completion:** Identify all environment variables used for configuration and explain how the application switches between production and testing modes.

*With the configuration blueprint in hand, you now see how the city operates. Every switch, every path, every mode is accounted for. The case is closed.*

### The Entry Point Mystery
**Difficulty:** quick

*Every great detective knows you have to start at the beginning. This file is where the investigation begins, where the Flask app is born and the city comes to life.*

Trace the Flask application factory pattern to understand how the backend initializes and connects all components.

**Files to investigate:**
- `backend/app.py` — Flask application factory that initializes the app, connects to database, and registers CLI commands

**Completion:** Map out the initialization flow from app creation to database connection and identify the CLI commands available.

*The entry point has been exposed. You know how the city wakes up, how it connects to its database, and what commands it responds to. The mystery of the entry point is solved.*

### The Database Connection
**Difficulty:** medium
**Prerequisites:** The Configuration Blueprint

*The city's memory lives in these connections. Every query, every transaction, every save depends on this hidden link to the database.*

Examine the database connection patterns and understand how SQLite is used for data persistence.

**Files to investigate:**
- `backend/database.py` — Database connection management utilities and initialization functions
- `backend/app.py` — Shows how the database connection is established during app initialization

**Completion:** Identify all database operations and explain how the connection is managed throughout the application lifecycle.

*The database connection has been mapped out. You know how the city stores its memories and how it retrieves them. The link between code and data is now clear.*

### The Frontend Linter
**Difficulty:** quick

*The code has a moral compass, and it's written in this configuration. Every rule, every warning, every error is a guard against sloppy work.*

Investigate the ESLint configuration to understand code quality conventions enforced in the frontend.

**Files to investigate:**
- `frontend/eslint.config.js` — ESLint configuration defining code quality rules and conventions

**Completion:** Identify the linting rules configured and explain how they enforce code quality standards.

*The linter's code of conduct is now understood. You know what the code must do to pass inspection and what standards it must uphold. The quality is assured.*

### The Build Configuration
**Difficulty:** medium
**Prerequisites:** The Frontend Linter

*Before the city can be seen, it must be built. This configuration determines how the frontend is assembled, optimized, and delivered to the streets.*

Examine the Vite configuration to understand how the frontend is built and optimized.

**Files to investigate:**
- `frontend/vite.config.ts` — Vite configuration defining build plugins, optimization settings, and development server options

**Completion:** Identify the build plugins, optimization settings, and development server configuration.

*The build process has been uncovered. You know how the frontend is constructed, optimized, and served. The city is ready for the world to see.*

### The Shared Constants
**Difficulty:** medium
**Prerequisites:** The Frontend Linter

*In a city of many parts, there must be common ground. These constants are the meeting place where the frontend finds its shared language.*

Investigate the shared constants file to understand the conventions used across the frontend application.

**Files to investigate:**
- `frontend/src/shared/constants.ts` — Shared constants including branch colors, API endpoints, and other application-wide values

**Completion:** Identify all shared constants and explain how they maintain consistency across the application.

*The shared ground has been mapped. You know the language the frontend speaks and the values it holds dear. Consistency is now assured.*

### Category: testing

Cooldown 60s...
Failed: Failed to generate missions: The read operation timed out (category 'testing', after 5 attempt(s))

### Category: technical_debt

Cooldown 60s...
Failed: Failed to generate missions: The read operation timed out (category 'technical_debt', after 5 attempt(s))

## Summary

Total missions generated: 17
Categories tested: 7
Difficulty breakdown: {'quick': 7, 'medium': 8, 'deep_dive': 2}
Root missions (no prereqs): 10