Structured Logging Skill

Overview

This skill provides JSON-formatted logging for audit trails, debugging, and compliance monitoring. Logs are written as newline-delimited JSON to daily files (logs/YYYY-MM-DD.json) with automatic 30-day retention.

When to Use

Use this skill to:

• Log agent invocations and iterations
• Track quality metrics over time
• Record errors with full stack traces
• Monitor execution times and performance
• Create audit trails for compliance
• Debug agent behavior and refinement loops

Installation

IMPORTANT: This skill has its own isolated virtual environment (.venv) managed by uv. Do NOT use system Python.

Initialize the skill's environment:

# From the skill directory
cd .agent/skills/structured-logging
uv sync  # Creates .venv (no external dependencies, uses Python stdlib)

No external dependencies - uses Python standard library.

Usage

CRITICAL: Always use uv run to execute code with this skill's .venv, NOT system Python.

Initialize Logger

# From .agent/skills/structured-logging/ directory
# Run with: uv run python -c "..."
from structured_logging import StructuredLogger

# Initialize with defaults
logger = StructuredLogger(
    log_dir="logs",          # Directory for log files
    retention_days=30        # Auto-delete logs older than 30 days
)

Log Agent Operations

from src.models.evaluator_schema import QualityMetrics

# Log successful operation
logger.log(
    log_level="INFO",
    agent_or_skill_name="summary_subagent",
    operation_type="invoke",
    input_summary="Clinical note: 2500 words, cardiology",
    output_summary="Summary generated: 5 key problems, 12 citations",
    execution_time_ms=45000,
    quality_metrics=QualityMetrics(
        citation_coverage=0.92,
        hallucination_rate=0.03,
        jaccard_overlap=0.75
    )
)

Log Errors

from src.models.evaluator_schema import ErrorDetails

# Log error with full context
logger.log(
    log_level="ERROR",
    agent_or_skill_name="ollama_client",
    operation_type="error",
    input_summary="Prompt: Generate clinical summary...",
    execution_time_ms=300500,
    error_details=ErrorDetails(
        error_reference_id="ERR-2025-A3F",
        stack_trace="Traceback (most recent call last)...",
        context={"model": "phi4:14b", "timeout": 300},
        file_paths=["src/skills/ollama_client.py"]
    )
)

Log Iterative Refinement

# Log each iteration in refinement loop
for iteration in range(1, 6):
    logger.log(
        log_level="INFO",
        agent_or_skill_name="main_orchestrator",
        operation_type="iterate",
        input_summary=f"Iteration {iteration}: Refining based on evaluator feedback",
        output_summary=f"Status: {'pass' if metrics_pass else 'fail'}",
        execution_time_ms=iteration_time,
        quality_metrics=current_metrics
    )

Read and Filter Logs

from datetime import datetime

# Read today's logs
entries = logger.read_logs()

# Read specific date
entries = logger.read_logs(date=datetime(2025, 10, 24))

# Filter by log level
errors = logger.read_logs(log_level="ERROR")

# Filter by agent
agent_logs = logger.read_logs(agent_name="evaluator_agent")

Cleanup Old Logs

# Manually trigger cleanup (also runs automatically)
deleted_count = logger.cleanup_old_logs()
print(f"Deleted {deleted_count} expired log files")

Log Format

File Path: logs/YYYY-MM-DD.json

Format: Newline-delimited JSON (one entry per line)

Example Entry:

{
  "timestamp": "2025-10-24T14:30:22Z",
  "log_level": "INFO",
  "agent_or_skill_name": "summary_subagent",
  "operation_type": "invoke",
  "input_summary": "Clinical note: 2500 words",
  "output_summary": "Summary: 5 problems, 12 citations",
  "execution_time_ms": 45000,
  "quality_metrics": {
    "citation_coverage": 0.92,
    "hallucination_rate": 0.03,
    "jaccard_overlap": 0.75
  },
  "error_details": null
}

Querying Logs with jq

# Show all errors from today
cat logs/$(date +%Y-%m-%d).json | jq 'select(.log_level == "ERROR")'

# Show quality metrics for iterations ≥3
cat logs/*.json | jq 'select(.operation_type == "iterate" and .iteration_number >= 3) | .quality_metrics'

# Find error by reference ID
cat logs/*.json | jq 'select(.error_details.error_reference_id == "ERR-2025-A3F")'

# Calculate average execution time
cat logs/$(date +%Y-%m-%d).json | jq '[.execution_time_ms] | add/length'

Best Practices

1. Sanitize PHI: Never log actual clinical content - use summaries only
2. Include Execution Time: Always track performance metrics
3. Use Error References: Generate unique error IDs (ERR-YYYY-NNN) for user-facing messages
4. Log Quality Metrics: Track citation coverage, hallucination rate, Jaccard overlap
5. Rotation: Rely on daily rotation, not manual log management
6. Retention: 30 days is suitable for debugging and compliance

Integration with Agents

All agents and skills should log:

• Start: Before operation begins (input summary)
• End: After operation completes (output summary, execution time)
• Errors: With full stack trace and error reference ID
• Metrics: Quality scores for validation operations

Implementation

See structured_logging.py for the full Python implementation.