name: diagnostics-runner description: Run comprehensive system diagnostics including dependency checks, test suite execution, git status, and pipeline health verification. Use when troubleshooting issues, verifying system readiness, or preparing for a release.

Diagnostics Runner Skill

Comprehensive system health checks and diagnostics for the VideoChunking project.

What This Skill Does

This skill provides multi-level diagnostics to ensure system health:

Dependency Verification: Check all required packages and tools are installed
System Health: Verify FFmpeg, Ollama, and other components are functional
Test Suite Execution: Run pytest with coverage analysis
Configuration Validation: Check party configs, settings, and data files
Git Status Review: Report repository state and uncommitted changes
Data Integrity: Verify session data and knowledge base files
Performance Benchmarks: Test critical operations for performance issues
Cleanup Recommendations: Identify temporary files and optimization opportunities

Diagnostic Levels

Quick Check (30 seconds)

FFmpeg availability
Ollama status
Critical dependencies
Git branch and status

Standard Check (2 minutes)

All dependencies with versions
System health (FFmpeg, Ollama, PyAnnote)
Quick test run (no full suite)
Configuration file validation
Recent session data integrity

Comprehensive Check (5-10 minutes)

Full dependency audit
Complete test suite with coverage
All configuration validation
All session data verification
Performance benchmarks
Disk space and resource analysis
Detailed recommendations

Usage

Quick Diagnostics

User: "Check if the system is ready to process" User: "Is everything working?" User: "Quick health check"

Standard Diagnostics

User: "Run diagnostics" User: "Check system health" User: "Verify the pipeline is healthy"

Comprehensive Diagnostics

User: "Run full diagnostics" User: "Complete system check before release" User: "Thorough health check and tests"

Command Reference

# Quick health check
python cli.py health

# MCP tool: Check pipeline health
# Via MCP: check_pipeline_health

# Run test suite
pytest --cov=src --cov-report=term

# MCP tool: Analyze test coverage
# Via MCP: analyze_test_coverage

# MCP tool: Run full diagnostics
# Via MCP: run_diagnostics_suite

# Validate specific configuration
python cli.py validate-config --party default

# MCP tool: Validate party config
# Via MCP: validate_party_config

MCP Tool Integration

This skill orchestrates multiple MCP tools:

check_pipeline_health

Returns:

{
  "ffmpeg": true/false,
  "ollama": true/false,
  "ollama_models": "list of available models",
  "pyannote_models": true/false,
  "whisper": true/false,
  "dependencies": [
    {"package": "faster-whisper", "installed": true, "version": "1.2.0"},
    {"package": "pyannote.audio", "installed": true, "version": "4.0.1"},
    ...
  ]
}

run_diagnostics_suite

Returns:

{
  "timestamp": "2024-11-03T21:45:00",
  "python_version": "Python 3.10.6",
  "git_status": "modified files...",
  "test_status": "45 tests collected",
  "dependencies": "package status..."
}

analyze_test_coverage

Runs full pytest with coverage and returns detailed report.

validate_party_config

Validates party configuration files:

{
  "configs": [
    {
      "file": "party_default.json",
      "valid": true,
      "player_count": 4,
      "character_count": 4,
      "errors": [],
      "warnings": []
    }
  ]
}

list_available_models

Lists Ollama models for IC/OOC classification.

list_processed_sessions

Shows recently processed sessions to verify data integrity.

Diagnostic Components

1. Dependency Verification

Checks required Python packages:

✅ faster-whisper    v1.2.0
✅ pyannote.audio    v4.0.1
✅ gradio            v5.49.1
✅ torch             v2.9.0
✅ groq              v0.32.0
✅ click             v8.1.7
✅ rich              v13.7.0
❌ opencv-python     NOT INSTALLED

2. System Health

Verifies external tools:

FFmpeg:
  ✅ Installed: v6.0
  ✅ Accessible: f:/Repos/VideoChunking/ffmpeg/bin/ffmpeg.exe
  ✅ Features: Audio encoding, video encoding, filters

Ollama:
  ✅ Running: localhost:11434
  ✅ Models: mistral (active), llama3 (available)
  ✅ Response time: 124ms

PyAnnote:
  ✅ Models downloaded: yes
  ✅ Segmentation model: pyannote/segmentation
  ✅ Embedding model: pyannote/embedding

3. Test Suite Execution

Runs pytest and reports:

Test Results:
  ✅ Passed: 42
  ❌ Failed: 3
  ⚠️  Skipped: 2

Failed Tests:
  tests/test_classifier.py::test_ic_ooc_accuracy
  tests/test_diarization.py::test_speaker_count
  tests/test_knowledge_extractor.py::test_npc_extraction

Coverage: 78% (target: 80%)
  src/audio_processor.py:    95%
  src/transcriber.py:        92%
  src/classifier.py:         65% ⚠️ LOW
  src/knowledge_extractor.py: 58% ⚠️ LOW

4. Configuration Validation

Checks configuration files:

Party Configurations:
  ✅ party_default.json: Valid (4 players, 4 characters, 1 DM)
  ✅ party_oneshot.json: Valid (5 players, 5 characters, 1 DM)
  ❌ party_custom.json: INVALID - Missing 'dm' field

Settings:
  ✅ src/config.py: Valid
  ✅ .env.example: Valid
  ⚠️  .env: Not found (expected for production)

5. Git Status Review

Reports repository state:

Git Repository:
  Branch: main
  Ahead of remote: 3 commits
  Behind remote: 0 commits

Uncommitted Changes:
  Modified:   src/classifier.py
  Modified:   tests/test_classifier.py
  Untracked:  temp_output/

Recommendations:
  ⚠️  Commit changes before processing new sessions
  ⚠️  Push commits to backup work
  ℹ️  .coverage file can be added to .gitignore

6. Data Integrity

Validates data files:

Campaign Knowledge:
  ✅ File exists: data/campaign_knowledge.json
  ✅ Valid JSON: yes
  ✅ Entity counts: 47 NPCs, 23 locations, 15 quests
  ✅ Last updated: 2024-11-02 18:45:23

Party Configurations:
  ✅ Files: 2 valid, 1 invalid
  ⚠️  party_custom.json has validation errors

Recent Sessions:
  ✅ session_010: Complete (all output files present)
  ✅ session_011: Complete (all output files present)
  ⚠️  session_012: Incomplete (missing knowledge extraction)

Recommendations:
  ⚠️  Re-run knowledge extraction for session_012
  ⚠️  Fix validation errors in party_custom.json

7. Performance Benchmarks

Tests critical operations:

Performance Benchmarks:
  Audio extraction (10s video):    1.2s ✅
  Transcription (1min audio):      8.4s ✅
  Diarization (1min audio):        12.1s ✅
  Classification (100 segments):   3.7s ✅
  Knowledge extraction:            5.2s ✅

Memory Usage:
  Idle: 450MB
  Transcription: 2.1GB ✅
  Diarization: 1.8GB ✅
  Peak: 2.8GB ✅ (within 16GB available)

8. Cleanup Recommendations

Identifies optimization opportunities:

Disk Space:
  Total: 512GB
  Used: 387GB
  Available: 125GB

Temporary Files:
  output/temp/: 2.3GB (45 files)
  .pytest_cache/: 12MB
  __pycache__/: 8MB

Recommendations:
  ℹ️  Clean temporary output files: rm -rf output/temp/
  ℹ️  Remove pytest cache: rm -rf .pytest_cache/
  ℹ️  Clear Python cache: find . -type d -name __pycache__ -exec rm -rf {} +
  ⚠️  Low disk space: Consider archiving old session outputs

Output Formats

Summary View (Default)

Concise overview suitable for quick checks:

System Health: ✅ HEALTHY
Dependencies: ✅ ALL INSTALLED
Tests: ⚠️  3 FAILED (42 passed, 78% coverage)
Configuration: ⚠️  1 INVALID (2 valid)
Data: ✅ INTACT
Git: ⚠️  UNCOMMITTED CHANGES

Action Required:
  1. Fix 3 failing tests
  2. Validate party_custom.json
  3. Commit pending changes

Detailed View

Expanded report with specifics for each component.

JSON Export

Machine-readable format:

{
  "timestamp": "2024-11-03T21:45:00Z",
  "overall_health": "warning",
  "components": {
    "dependencies": {"status": "ok", "details": {...}},
    "system_health": {"status": "ok", "details": {...}},
    "tests": {"status": "warning", "failed": 3, "passed": 42},
    "configuration": {"status": "warning", "invalid": 1},
    "data": {"status": "ok", "details": {...}},
    "git": {"status": "warning", "uncommitted": 2}
  },
  "recommendations": [...]
}

Automated Diagnostics

Pre-Commit Hook

Run quick diagnostics before commits:

# .git/hooks/pre-commit
pytest tests/ --tb=short
python cli.py health --quick

CI/CD Integration

Run comprehensive diagnostics in CI:

# .github/workflows/diagnostics.yml
- name: Run diagnostics
  run: python cli.py diagnostics --comprehensive --json > report.json

Scheduled Health Checks

Periodic system verification:

# Cron job or Windows Task Scheduler
0 9 * * * cd /path/to/project && python cli.py health --email-report

Troubleshooting Guide

Based on diagnostic results:

FFmpeg Not Found

Symptoms: ffmpeg=false in health check
Solutions:
  1. Install FFmpeg
  2. Add FFmpeg to system PATH
  3. Update config with FFmpeg path
  4. Run: ffmpeg -version to verify

Ollama Not Running

Symptoms: ollama=false in health check
Solutions:
  1. Start Ollama: ollama serve
  2. Check port 11434 is available
  3. Verify model is pulled: ollama list
  4. Test: curl http://localhost:11434/api/tags

Tests Failing

Symptoms: test_status shows failures
Solutions:
  1. Read failure messages carefully
  2. Run specific failed test: pytest tests/test_X.py -v
  3. Check if data dependencies exist
  4. Review recent code changes
  5. Update test fixtures if needed

Low Coverage

Symptoms: coverage <80%
Solutions:
  1. Identify uncovered code: pytest --cov-report=html
  2. Open htmlcov/index.html in browser
  3. Write tests for uncovered functions
  4. Add edge case tests

Invalid Configuration

Symptoms: Configuration validation errors
Solutions:
  1. Review error messages
  2. Compare with .json schema
  3. Check for missing required fields
  4. Verify JSON syntax is valid
  5. Use JSON validator tool

Data Integrity Issues

Symptoms: Corrupt or missing session data
Solutions:
  1. Re-process affected sessions
  2. Restore from backups if available
  3. Manually correct JSON files
  4. Run data migration scripts

Best Practices

Regular Checks: Run diagnostics weekly or before major processing batches
Before Releases: Always run comprehensive diagnostics before tagging releases
After Updates: Check system health after dependency updates
Monitor Trends: Track test coverage and performance over time
Document Issues: Log diagnostic results when reporting bugs
Automate: Set up pre-commit hooks and CI checks
Review Recommendations: Act on cleanup and optimization suggestions

Integration with Other Skills

test-pipeline: Focuses specifically on test execution and coverage
debug-ffmpeg: Deep dive into FFmpeg-specific issues
party-validator: Detailed party configuration validation
session-processor: Use diagnostics before starting session processing
campaign-analyzer: Verify knowledge base integrity

Example Workflows

Pre-Processing Workflow

User: "I'm about to process several new sessions. Is the system ready?"

Assistant uses diagnostics-runner:
1. Runs check_pipeline_health MCP tool
2. Validates party configurations
3. Checks recent session data integrity
4. Verifies sufficient disk space
5. Reviews git status for uncommitted changes
6. Reports: "System healthy, ready to process"

Troubleshooting Workflow

User: "Session processing failed. Help me figure out why."

Assistant uses diagnostics-runner:
1. Runs comprehensive diagnostics
2. Identifies: Ollama not running
3. Provides solution: Start Ollama service
4. Verifies: Re-runs health check
5. Confirms: "Ollama now running, ready to retry"

Release Preparation Workflow

User: "Preparing for v2.0 release. Run full diagnostics."

Assistant uses diagnostics-runner:
1. Runs comprehensive diagnostics
2. Executes full test suite with coverage
3. Validates all configurations
4. Checks git status
5. Generates JSON report
6. Provides release readiness assessment

Advanced Features

Custom Diagnostic Scripts

# custom_diagnostics.py
from src.diagnostics import DiagnosticsRunner

runner = DiagnosticsRunner()
results = runner.run_all()

# Custom checks
if results['disk_space_gb'] < 50:
    print("WARNING: Low disk space")

# Send to monitoring system
send_to_datadog(results)

Diagnostic Dashboard

Create a monitoring dashboard:

# Generate HTML report
python cli.py diagnostics --html > diagnostics.html

# Serve with Python
python -m http.server 8000
# Open: http://localhost:8000/diagnostics.html

Integration with Monitoring

# Export metrics
python cli.py diagnostics --prometheus > metrics.txt
# Scrape with Prometheus for alerting

diagnostics-runner

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