Logging And Observability

Organization

RedHatProductSecurity

logging-and-observability

Enforce centralized structured logging and audit trails in MCP servers. Use when building, configuring, or reviewing MCP server logging, observability, or audit capabilities.

Overview

Publisher	RedHatProductSecurity
Repository	`prodsec-skills`
Skill name	`logging-and-observability`
Stars	56
Forks	9
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by RedHatProductSecurity on GitHub. Read the source before you install it.

Installation

Install the Logging And Observability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/logging-and-observability
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/logging-and-observability

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-skills.git /tmp/prodsec-skills
mkdir -p .claude/skills
cp -r /tmp/prodsec-skills/module/skills/logging-and-observability .claude/skills/logging-and-observability

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logging And Observability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Logging And Observability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Logging And Observability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Logging and Observability for MCP Servers

Security Requirement

MCP servers MUST implement comprehensive, centralized, structured logging. This is vital for auditing, troubleshooting, and detecting security incidents.

Centralized and Structured Logging

Requirement	Details
Format	JSON or other structured format (not unstructured text)
Destination	MCP servers must be capable of sending logs to a central system (e.g., OpenShift logging stack, ELK, Splunk)
Protocol	Standard log shipping (syslog, OTLP, Fluentd, or equivalent)
Configurable	Deployers must be able to configure the log destination

Audit Trail Requirements

Logs MUST capture enough detail to reconstruct a security incident:

Field	Purpose
User identity	Who performed the action (from token `sub` claim)
Tool name	Which tool was invoked
Parameters	What parameters were passed (with sensitive data scrubbed)
Timestamp	When the action occurred (ISO 8601, UTC)
Result	Success or failure, including error details
Source	Client IP, request ID, session ID

Sensitive Data Scrubbing

Logs MUST scrub sensitive data before recording:

Redact tokens and credentials (log metadata like jti and sub instead)
Mask PII in parameters (email addresses, names, etc.)
Truncate or hash large prompt content
Never log passwords, API keys, or secret values

Example Audit Log Entry

json
{
  "timestamp": "2026-03-03T14:30:00Z",
  "event": "tool.invocation",
  "user": "user-123",
  "tool": "email.send",
  "parameters": {
    "to": "j***@example.com",
    "subject": "Meeting update"
  },
  "result": "success",
  "request_id": "req-abc-123",
  "source_ip": "10.0.1.42"
}

Implementation Checklist

Use structured logging format (JSON) for all log entries
Configure centralized log destination (OpenShift logging, ELK, Splunk)
Log every tool invocation with user, tool, parameters, and result
Scrub sensitive data (tokens, PII, secrets) from all log entries
Include enough detail to reconstruct security incidents
Include request ID for correlating related log entries
Make log destination configurable by deployers
Log authentication and authorization events (successes and failures)
Set log retention per organizational and regulatory requirements

Frequently asked questions

What does the Logging And Observability AI skill do?

Enforce centralized structured logging and audit trails in MCP servers. Use when building, configuring, or reviewing MCP server logging, observability, or audit capabilities.

Why use Logging And Observability on TypingMind?

Because you install it once and use it with any model. Logging And Observability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Logging And Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/logging-and-observability. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Logging And Observability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Logging And Observability?

As many as you like. As long as a model supports skills, you can use Logging And Observability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Logging And Observability AI skill free?

Yes. It is published on GitHub by RedHatProductSecurity under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Logging and Observability for MCP Servers

Security Requirement

Centralized and Structured Logging

Audit Trail Requirements

Sensitive Data Scrubbing

Example Audit Log Entry

Implementation Checklist

Frequently asked questions

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