python-observability
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill python-observability- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads python-observability automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Python Observability
Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.
When to Use This Skill
- Adding structured logging to applications
- Implementing metrics collection with Prometheus
- Setting up distributed tracing across services
- Propagating correlation IDs through request chains
- Debugging production issues
- Building observability dashboards
Core Concepts
1. Structured Logging
Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats.
2. The Four Golden Signals
Track latency, traffic, errors, and saturation for every service boundary.
3. Correlation IDs
Thread a unique ID through all logs and spans for a single request, enabling end-to-end tracing.
4. Bounded Cardinality
Keep metric label values bounded. Unbounded labels (like user IDs) explode storage costs.
Quick Start
import structlog
structlog.configure(
processors=[
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.JSONRenderer(),
],
)
logger = structlog.get_logger()
logger.info("Request processed", user_id="123", duration_ms=45)
Fundamental Patterns
Pattern 1: Structured Logging with Structlog
Configure structlog for JSON output with consistent fields.
import logging
import structlog
def configure_logging(log_level: str = "INFO") -> None:
"""Configure structured logging for the application."""
structlog.configure(
processors=[
structlog.contextvars.merge_contextvars,
structlog.processors.add_log_level,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.StackInfoRenderer(),
structlog.processors.format_exc_info,
structlog.processors.JSONRenderer(),
],
wrapper_class=structlog.make_filtering_bound_logger(
getattr(logging, log_level.upper())
),
context_class=dict,
logger_factory=structlog.PrintLoggerFactory(),
cache_logger_on_first_use=True,
)
# Initialize at application startup
configure_logging("INFO")
logger = structlog.get_logger()
Pattern 2: Consistent Log Fields
Every log entry should include standard fields for filtering and correlation.
import structlog
from contextvars import ContextVar
# Store correlation ID in context
correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")
logger = structlog.get_logger()
def process_request(request: Request) -> Response:
"""Process request with structured logging."""
logger.info(
"Request received",
correlation_id=correlation_id.get(),
method=request.method,
path=request.path,
user_id=request.user_id,
)
try:
result = handle_request(request)
logger.info(
"Request completed",
correlation_id=correlation_id.get(),
status_code=200,
duration_ms=elapsed,
)
return result
except Exception as e:
logger.error(
"Request failed",
correlation_id=correlation_id.get(),
error_type=type(e).__name__,
error_message=str(e),
)
raise
Pattern 3: Semantic Log Levels
Use log levels consistently across the application.
| Level | Purpose | Examples |
|---|---|---|
DEBUG | Development diagnostics | Variable values, internal state |
INFO | Request lifecycle, operations | Request start/end, job completion |
WARNING | Recoverable anomalies | Retry attempts, fallback used |
ERROR | Failures needing attention | Exceptions, service unavailable |
# DEBUG: Detailed internal information
logger.debug("Cache lookup", key=cache_key, hit=cache_hit)
# INFO: Normal operational events
logger.info("Order created", order_id=order.id, total=order.total)
# WARNING: Abnormal but handled situations
logger.warning(
"Rate limit approaching",
current_rate=950,
limit=1000,
reset_seconds=30,
)
# ERROR: Failures requiring investigation
logger.error(
"Payment processing failed",
order_id=order.id,
error=str(e),
payment_provider="stripe",
)
Never log expected behavior at ERROR. A user entering a wrong password is INFO, not ERROR.
Pattern 4: Correlation ID Propagation
Generate a unique ID at ingress and thread it through all operations.
from contextvars import ContextVar
import uuid
import structlog
correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")
def set_correlation_id(cid: str | None = None) -> str:
"""Set correlation ID for current context."""
cid = cid or str(uuid.uuid4())
correlation_id.set(cid)
structlog.contextvars.bind_contextvars(correlation_id=cid)
return cid
# FastAPI middleware example
from fastapi import Request
async def correlation_middleware(request: Request, call_next):
"""Middleware to set and propagate correlation ID."""
# Use incoming header or generate new
cid = request.headers.get("X-Correlation-ID") or str(uuid.uuid4())
set_correlation_id(cid)
response = await call_next(request)
response.headers["X-Correlation-ID"] = cid
return response
Propagate to outbound requests:
import httpx
async def call_downstream_service(endpoint: str, data: dict) -> dict:
"""Call downstream service with correlation ID."""
async with httpx.AsyncClient() as client:
response = await client.post(
endpoint,
json=data,
headers={"X-Correlation-ID": correlation_id.get()},
)
return response.json()
Detailed worked examples and patterns
Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Best Practices Summary
- Use structured logging - JSON logs with consistent fields
- Propagate correlation IDs - Thread through all requests and logs
- Track the four golden signals - Latency, traffic, errors, saturation
- Bound label cardinality - Never use unbounded values as metric labels
- Log at appropriate levels - Don't cry wolf with ERROR
- Include context - User ID, request ID, operation name in logs
- Use context managers - Consistent timing and error handling
- Separate concerns - Observability code shouldn't pollute business logic
- Test your observability - Verify logs and metrics in integration tests
- Set up alerts - Metrics are useless without alerting
FAQ
What does the python-observability skill do?
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
How do I install python-observability?
Run npx skills add https://github.com/wshobson/agents --skill python-observability in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-observability/ (personal) or .claude/skills/python-observability/ (project).
Which AI agents can use python-observability?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is python-observability safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains python-observability?
It is published by wshobson in wshobson/agents. It has 11,654 recorded installs.
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