AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides guidelines and patterns for integrating PostHog log ingestion into Python applications. It emphasizes using the OpenTelemetry protocol for structured logging, ensuring that logs are centralized and actionable. The skill covers configuration requirements, such as using environment variables for sensitive data, and provides specific implementation patterns for the Python SDK to ensure proper event capture and resource management.
What it does
- Provides patterns for structured logging using OpenTelemetry.
- Outlines requirements for Python SDK configuration and initialization.
- Explains how to manage log lifecycle and resource cleanup in Python scripts.
- Offers guidance on distinguishing between events and logs for better observability.
- Details how to use MCP tools to search and query logs within development environments.
- Provides best practices for log content to improve queryability and reduce noise.
When to use it
- When you need to centralize logs from Python applications into PostHog.
- When implementing structured logging to improve production debugging.
- When migrating from event-based tracking to dedicated log ingestion for system events.
- When setting up AI agents to query and analyze production logs via MCP.
Advantages
- Enables correlation between logs, product analytics, and session replays.
- Promotes the use of wide events for more efficient log analysis.
- Ensures consistent handling of sensitive data by separating PII from event properties.
- Supports integration with AI-assisted debugging workflows.
Usage examples
- “How do I initialize the PostHog Python SDK for logging?”
- The agent provides the constructor pattern using the Posthog class, including the enable_exception_autocapture=True setting and instructions to use environment variables for keys.
- “Show me how to search for error logs using MCP.”
- The agent explains the use of the logs-query tool, including how to filter by severity level and service name.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/posthog/logs-python/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip logs-python.zip -d .agents/skills
Documentation by PostHog
PostHog logs for Python
This skill helps you add PostHog log ingestion to Python applications.
Reference files
references/python.md- Python logs installation - docsreferences/start-here.md- Getting started with logs - docsreferences/search.md- Search logs - docsreferences/best-practices.md- Logging best practices - docsreferences/troubleshooting.md- Logs troubleshooting - docsreferences/link-session-replay.md- Link session replay - docsreferences/debug-logs-mcp.md- Debug logs with mcp - docs
Consult the documentation for API details and framework-specific patterns.
Key principles
Environment variables: Always use environment variables for PostHog keys and OpenTelemetry endpoints. Never hardcode them.
Minimal changes: Add log export alongside existing logging. Don't replace or restructure existing logging code.
OpenTelemetry: PostHog logs use the OpenTelemetry protocol. Configure an OTLP exporter pointed at PostHog's ingest endpoint.
Structured logging: Prefer structured log formats with key-value properties over plain text messages.
Framework guidelines
Remember that source code is available in the venv/site-packages directory
posthog is the Python SDK package name
Install dependencies with
pip install posthogorpip install -r requirements.txtand do NOT use unquoted version specifiers like>=directly in shell commandsIn CLIs and scripts: MUST call posthog.shutdown() before exit or all events are lost
Always use the Posthog() class constructor (instance-based API) instead of module-level posthog.api_key config
Always include enable_exception_autocapture=True in the Posthog() constructor to automatically track exceptions
NEVER send PII in capture() event properties — no emails, full names, phone numbers, physical addresses, IP addresses, or user-generated content
PII belongs in identify() person properties, NOT in capture() event properties. Safe event properties are metadata like message_length, form_type, boolean flags.
Register posthog_client.shutdown with atexit.register() to ensure all events are flushed on exit
The Python SDK has NO identify() method — use posthog_client.set(distinct_id=user_id, properties={...}) to set person properties, or use identify_context(user_id) within a context
Files
- ATTRIBUTION.mdAdded by AutoGineer933 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md2.4 KiB
- references/best-practices.md19.6 KiB
- references/debug-logs-mcp.md2.2 KiB
- references/link-session-replay.md4.9 KiB
- references/python.md3.6 KiB
- references/search.md2.5 KiB
- references/start-here.md2.4 KiB
- references/troubleshooting.md3.5 KiB
references/debug-logs-mcp.md
Debug Logs with MCP - Docs
The PostHog MCP server gives AI agents direct access to your Logs. Ask your agent to search, filter, and analyze log data without leaving your code editor.
With MCP, your agents can:
Search and filter logs – Query by severity level, service name, date range, and free text.
Discover log attributes – List available attributes and their values to build targeted queries.
Debug in context – Investigate production issues without switching tools.
Correlate with traces – Use trace IDs and span IDs from log entries to follow request flows across services.
This works in any MCP client – Cursor, Windsurf, Claude Code, and others.
Example prompts
Try these prompts with your MCP-enabled agent:
Show me all error logs from the last hour.What services are logging errors? Search for error logs from the payments service.Find logs related to trace ID abc123.What log attributes are available? Show me the values for the service.name attribute.Show me warning and error logs from the last 24 hours, excluding debug noise.
Logs tools
The MCP server provides three tools for working with logs:
| Tool | Description |
|---|---|
| logs-query | Search and query logs with filters for severity levels (trace, debug, info, warn, error, fatal), service names, date ranges, and free text. Supports pagination for large result sets. |
| logs-list-attributes | List available log attributes in your project to discover what you can filter on. Supports filtering by attribute type (log or resource). |
| logs-list-attribute-values | Get possible values for a specific log attribute. Find service names, log levels, or other attribute values before querying. |
A typical workflow:
Call
logs-list-attributesto discover available filter attributes.Call
logs-list-attribute-valuesto find specific values (e.g., which service names exist).Call
logs-queryto search logs with the right filters.
Get started
See the MCP server documentation for setup instructions.
Community questions
Ask a question
Was this page useful?
HelpfulCould be better
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an API key, Needs Python
Security
What our scanner noticed· Low risk
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- PostHog
- Publisher
- PostHog (Official)
- Source
- PostHog/skills · skills/posthog/logs/skills/python · commit a5d30e7
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- PostHog/skills
AutoGineer is not affiliated with or endorsed by PostHog or PostHog.
Related skills
Claude API
AnthropicOfficial
Comprehensive reference for integrating Anthropic's Claude API, SDKs, and Managed Agents into your applications.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreApache-2.0Medium riskHas scriptsGitHub stars of the source repository188.6k
Transformers
K-Dense· Community
Integrate Hugging Face Transformers for model loading, inference, and fine-tuning across NLP, vision, and audio tasks.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreMITLow riskGitHub stars of the source repository56.4k
Open Notebook
K-Dense· Community
Integrate with the self-hosted Open Notebook platform to automate research ingestion, context-aware chat, and AI-driven analysis.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreMITLow riskHas scriptsGitHub stars of the source repository56.4k
Vision Sft
Seth Hobson· Community
A structured workflow for supervised fine-tuning of vision-language models, focusing on adapter configuration and silent failure prevention.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
LLM Evaluation
Seth Hobson· Community
A comprehensive framework for evaluating LLM application performance using automated metrics, human feedback, and LLM-as-a-judge techniques.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
Lora Qlora Recipes
Seth Hobson· Community
Provides configuration recipes and best-practice hyperparameters for LoRA and QLoRA supervised fine-tuning.
Data and AIClaude CodeClaude appsOpenAI Codex+14 moreMITGitHub stars of the source repository42.3k
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download logs-python.zip