
# Unfamiliar Pipeline Test

**Timestamp:** 2026-03-14 03:08:52 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: 817 chars
Tech stack: ['Flask (Python web framework)', 'SQLite (database)', 'React (frontend framework)', 'TypeScript (type-safe JavaScript)', 'Vite (build tool)', 'pytest (testing framework)', 'Flask-CORS (cross-origin resource sharing)', 'SQLAlchemy (database ORM)']
Modules described: 30
Security posture: The codebase demonstrates good security practices with configuration loaded from environment variables rather than hardcoded values. CLI commands include safety checks to prevent accidental operations

**Full summary:**

```
This is a full-stack skill tracking application for monitoring progress in skating skills. The backend is a Flask REST API built with Python and SQLite, providing endpoints for activities, logging progress, and calculating level progression. The frontend is a React application with TypeScript that displays activities, tracks user progress through XP systems, and visualizes skill development across multiple branches. The system uses a database schema with skills, branches, levels, activities, and log entries, supporting both exercise and daily task tracking. The application includes CLI commands for database management (seed, backup, reset-test) and comprehensive test coverage for backend routes and queries. Configuration is managed through environment variables with safety checks for production operations.
```

  [0] Backend Application: Main Flask application factory with configuration management, database initializ
  [1] Database Layer: SQLite connection management with context managers, schema migrations, and core 
  [2] Activities Feature: Backend module handling activity queries and routes, including filtering by bran
  [3] Logs Feature: Backend module for logging user activity entries, tracking XP gains, and managin
  [4] Progress Feature: Backend module for calculating level progression, tracking XP totals across bran
  [5] Level Calculator: Business logic module for determining user levels based on XP thresholds and bra
  [6] Test Infrastructure: Comprehensive test suite with fixtures, seed data generators, and test cases cov
  [7] Activity Components: React components for displaying activities including ActivityCard, ActivityList,
  [8] Progress Components: React components for visualizing progress including ProgressOverview, BranchBrea
  [9] Activity Feature API: Frontend API module for communicating with backend activity endpoints, including
  [10] Progress Feature API: Frontend API module for fetching progress data and XP history from backend endpo
  [11] Shared Components: Reusable UI components including ErrorBoundary, Icon, and utility functions for 
  [12] Shared Utilities: Frontend utility functions for progress calculations, date formatting, and helpe
  [13] Database Seeding: Module for populating database with initial data from CSV files, including skill
  [14] Schema Migrations: Database schema management system for applying migrations and maintaining databa
  [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: ['data_flow', 'testing']
Token budget per category: ~1,142

### Category: data_flow

6 missions, 2 chains

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

*The city never sleeps, and neither does this Flask application. I'm looking at the factory that builds the app—where the streets meet the database, and where every request begins its journey.*

Investigate how the Flask application is initialized, configured, and routes are registered to handle incoming requests.

**Files to investigate:**
- `backend/app.py` — Main Flask application factory that initializes the app, loads configuration, and registers all routes.
- `backend/config.py` — Configuration management with environment variable loading and production safety checks.

**Completion:** Identify the application factory pattern, understand how configuration is loaded from environment variables, and trace the registration of activity and log routes.

*The factory is running. I've seen how the app is built, how configuration is loaded, and where the routes are registered. The city is ready to handle whatever comes down the pipe.*

### The SQLite Connection
**Difficulty:** quick

*The database is the heart of this operation. I need to understand how connections are made, how the schema is structured, and where the data lives before it hits the streets.*

Examine the database layer to understand how SQLite connections are managed, schemas are defined, and data models are structured.

**Files to investigate:**
- `backend/database.py` — SQLite connection management with context managers and database initialization.
- `backend/schema.py` — Database schema definitions for skills, branches, levels, activities, and log entries.

**Completion:** Identify the database connection context manager, understand the schema structure for skills, branches, levels, activities, and log entries.

*The database blueprint is clear. I've seen how connections are managed, how the schema is organized, and where every piece of data is stored. The foundation is solid.*

### The Route to Query
**Difficulty:** medium
**Prerequisites:** The Flask Factory

*Every request that hits the server has to go somewhere. I'm tracing the path from the HTTP request to the database, following the breadcrumbs that lead to the data.*

Trace how HTTP requests flow through activity routes to database queries, understanding the request-response cycle for activities.

**Files to investigate:**
- `backend/features/activities/routes.py` — HTTP route handlers that receive requests and delegate to query functions.
- `backend/features/activities/queries.py` — Database query functions that retrieve and filter activity data.

**Completion:** Map the complete data flow from HTTP request in routes.py to database queries, identifying how filtering by branch and exercise form works.

*The path is clear. I've seen how requests flow through the routes, how they're processed by queries, and how they're handed off to the database. The data is moving exactly where it should.*

### Logging the Evidence
**Difficulty:** medium
**Prerequisites:** The Route to Query

*Every action leaves a trace. I'm investigating how user activities are captured, how XP is calculated, and where the evidence is stored for later analysis.*

Investigate how user activity entries are captured, stored, and processed through the logs feature, including XP tracking.

**Files to investigate:**
- `backend/features/logs/routes.py` — HTTP route handlers for logging user activities and tracking XP gains.
- `backend/features/logs/queries.py` — Query functions for creating log entries and managing log data structures.

**Completion:** Understand the complete flow of creating log entries, calculating XP gains, and storing the results in the database.

*The evidence is logged. I've seen how activities are captured, how XP is calculated, and how the data flows through the system. Nothing gets lost in the shadows.*

### Leveling Up the System
**Difficulty:** deep_dive
**Prerequisites:** Logging the Evidence

*XP is the currency of this city. I need to understand how it's calculated, how levels are determined, and how progress is tracked across different branches.*

Trace how progress is calculated from log entries, understanding the XP thresholds, level progression, and branch-specific tracking.

**Files to investigate:**
- `backend/features/progress/routes.py` — HTTP route handlers for retrieving progress data and level information.
- `backend/features/progress/queries.py` — Query functions for calculating XP totals and progress across branches.
- `backend/features/progress/calculator.py` — Business logic for determining user levels based on XP thresholds and branch progress.

**Completion:** Map the complete progress calculation flow from log entries to level determination, understanding XP thresholds and branch-specific requirements.

*The system is leveling up. I've seen how XP flows from logs to progress calculations, how levels are determined, and how the data is structured for the frontend. The city is watching.*

### The API Connection
**Difficulty:** medium
**Prerequisites:** The Route to Query

*The streets are watching. I'm tracing how the frontend pulls data from the backend, following the API calls that bridge the gap between the UI and the database.*

Examine how the frontend communicates with the backend API, understanding the fetch functions and data transformation layers.

**Files to investigate:**
- `frontend/src/features/activities/api.ts` — Frontend API module for communicating with backend activity endpoints.
- `frontend/src/features/progress/api.ts` — Frontend API module for fetching progress data and XP history from backend endpoints.

**Completion:** Identify the frontend API functions that communicate with backend endpoints, understand the data types and transformation logic.

*The connection is secure. I've seen how the frontend fetches data, how it transforms it for display, and how it sends requests back to the backend. The city is connected.*

### Category: testing

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

## Summary

Total missions generated: 6
Categories tested: 2
Difficulty breakdown: {'quick': 2, 'medium': 3, 'deep_dive': 1}
Root missions (no prereqs): 2