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Logging

Organization
RedHatProductSecurity
logging

Enforce logging of access to sensitive external data sources in AI systems. Use when designing, building, or reviewing audit and logging capabilities for AI systems accessing external databases or data services.

Overview

PublisherRedHatProductSecurity
Repositoryprodsec-skills
Skill namelogging
Stars
56
Forks
9
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/ge-core/logging
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/ge-core/logging .claude/skills/logging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Logging 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 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 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 for External Data Source Access

Security Requirement

If the data contained and served by external data sources is sensitive, accesses to them MUST be logged. The product SHOULD have the functionality to send these events to an external logging facility using industry standards.

What to Log

EventDetails to Capture
Data accessWho accessed what data, when, from where
Data modificationWho modified what data, what changed
Access denialFailed access attempts (authentication or authorization failures)
Bulk operationsLarge data exports or queries
Connection eventsConnection establishment and teardown
Schema changesStructural changes to data sources

Logging Requirements

RequirementDetails
External logging facilityLogs must be sendable to an external SIEM or logging platform
Industry standard protocolsUse syslog, OTLP, Fluentd, or equivalent
Structured formatJSON with consistent field names
Principal identityEvery log entry includes the authenticated principal
ConfigurableCustomers must be able to configure the external log destination
Non-repudiationLogs should not be modifiable by the application

Log Safety

RuleRationale
No secrets in logsCredentials, API keys, and session tokens must never appear in log output
No PII in logsPersonally identifiable information creates compliance and privacy risks
No session identifiersSession IDs in logs can enable session hijacking
Log injection preventionUntrusted input in logs can exploit log viewers (XSS, RCE in parsers); sanitize all values before logging
Trusted system loggingLogging controls must run on a trusted system (server-side), not client-side

What to Log

Log both success and failure of security events:

  • Authentication attempts, especially failures
  • Access control failures
  • Tampering events (unexpected state changes)
  • Invalid or expired session token usage
  • System exceptions
  • Administrative functions and security configuration changes
  • Bulk data operations and large exports

Log Transport

  • Use standard protocols such as Syslog (rsyslog with TLS) or OTLP
  • Encrypt log transport when possible
  • For cloud services, forward to the corporate SIEM (e.g., Splunk)
  • Restrict log access to authorized individuals only

Implementation Checklist

  • Implement access logging for all connections to sensitive external data sources
  • Include principal identity, operation, timestamp, and data accessed in log entries
  • Provide functionality to forward logs to an external logging facility
  • Support industry standard log protocols (syslog, OTLP, Fluentd)
  • Use structured JSON format for log entries
  • Make the external log destination configurable by the deploying organization
  • Log access denials and authentication failures
  • Log authentication successes, admin actions, and security config changes
  • Ensure logs are tamper-protected (forward to immutable storage)
  • No secrets, PII, or session identifiers appear in log output
  • Log entries with untrusted data are sanitized to prevent injection
  • Log transport is encrypted (rsyslog with TLS, OTLP over TLS)
  • Log access is restricted to authorized individuals

Frequently asked questions

What does the Logging AI skill do?

Enforce logging of access to sensitive external data sources in AI systems. Use when designing, building, or reviewing audit and logging capabilities for AI systems accessing external databases or data services.

Why use Logging on TypingMind?

Because you install it once and use it with any model. Logging 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 in TypingMind?

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

Which AI models can use Logging?

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?

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

Is the Logging 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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