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Introduction:
The evolution of Large Language Models (LLMs) has introduced a paradigm shift in how cybersecurity professionals automate repetitive tasks, from generating compliance reports to structuring penetration test findings. A newly surfaced feature called “Skills” within AI represents a significant leap forward, allowing users to create reusable, modular instruction sets that act like macros or automation scripts for the AI. For security experts, this transforms the AI from a simple conversational partner into a programmable assistant capable of executing complex, multi-step workflows—such as formatting Right-to-Left (RTL) text for reports or, more critically, standardizing incident response documentation and code output.
Learning Objectives:
- Understand the architecture and security implications of AI “Skills” as reusable prompt engineering modules.
- Learn to build, deploy, and manage custom Skills for automating cybersecurity documentation and tool outputs.
- Explore practical applications of Skills for standardizing Linux/Windows command outputs and log analysis.
You Should Know:
- Defining the “Skills” Architecture: From Prompt to Module
The core concept behind the “Skills” feature is modularization. Instead of pasting a lengthy, complex prompt every time you need a specific output (e.g., a formatted penetration test report or a structured vulnerability scan), you create a Skill. This Skill is essentially a pre-loaded instruction set that loads into its context window when triggered. For cybersecurity professionals, this is analogous to writing a script or a playbook.
Step‑by‑step guide explaining what this does and how to use it.
– What it does: It isolates complex instructions, ensuring consistency across multiple sessions. For a security analyst, this ensures that every time you ask for a log analysis, the output follows the same MITRE ATT&CK mapping structure.
– How to use it:
1. Define the Scope: In your interface, navigate to the “Skills” or “Project Knowledge” section (depending on the deployment).
2. Create the Skill File: You upload a text file (or define it in the UI) containing the instructions. For a security context, this file might contain:
Security Log Analysis Skill You are a Security Operations Center (SOC) analyst. When presented with raw logs: 1. Identify the timestamp and source IP. 2. Map the event to MITRE ATT&CK tactics (e.g., TA0001 - Initial Access). 3. Output the results in a markdown table with columns: Time, Source, Event, MITRE ID, Recommended Action. 4. If the command is requested for Windows, provide PowerShell syntax. For Linux, provide Bash.
3. Activate: When chatting, simply mention the skill name (e.g., “Use the Security Log Analysis Skill to parse this Sysmon event”) or set it as default for the project.
2. Automating Compliance and Reporting with Modular Skills
Beyond simple text formatting, Skills can be leveraged to enforce compliance standards (like ISO 27001 or NIST) on AI-generated content. In the context of the original post, the user created a skill to solve a specific formatting issue (Hebrew RTL). In cybersecurity, we can solve the issue of inconsistent vulnerability reporting.
Step‑by‑step guide explaining what this does and how to use it.
– Use Case: Standardizing a Penetration Test Executive Summary.
– Step 1: Create the Skill Content. Create a file named pentest_report_formatter.md.
skill_name: "Pentest Report Formatter" description: "Formats raw scan data into a standardized executive report." instructions: | You are a Senior Penetration Tester. When given raw Nessus or Nmap output, generate a report with the following structure: - Executive Summary: Risk rating (Critical/High/Medium/Low) and business impact. - Scope: List IPs and targets. - Findings: Table with Vulnerability, CVSS Score, Affected Asset, Remediation. - Commands: For every finding, provide the exact command used to discover it (e.g., <code>nmap -sV --script vuln <target></code>). Do not include conversational filler text. Output strictly in Markdown.
– Step 2: Integration with Tool Output. If you are working with command-line tools, you can pipe outputs. For example, to analyze a Linux log for anomalies using the skill:
Simulate extracting failed SSH attempts from a Linux system sudo grep "Failed password" /var/log/auth.log > failed_auth.txt
You would then upload `failed_auth.txt` to and activate the “Pentest Report Formatter” skill, asking it to analyze the attempts.
– Step 3: Windows Command Example. For a Windows environment, you might query specific event IDs.
PowerShell: Query for Event ID 4625 (Failed Logon)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625} -MaxEvents 10 | Select-Object -Property TimeCreated, Message | Out-File .\failed_logons.txt
Feed this output to with the skill activated to get a structured analysis of lockout policies and potential brute-force attempts.
3. Verified Commands and Configurations for AI-Assisted Hardening
The combination of AI Skills and command-line utilities allows for a dynamic hardening workflow. Instead of searching for static guides, you can create a “Hardening Skill” that outputs commands verified by the AI based on your specific OS version.
Step‑by‑step guide explaining what this does and how to use it.
– Scenario: You need to harden a new Ubuntu 22.04 server.
– The Skill Definition:
System Hardening Skill You are a Linux Systems Architect. When asked to harden a service: 1. Verify the OS version using `lsb_release -a` if provided. 2. Output specific `ufw` or `iptables` rules to limit access. 3. Provide `sysctl` settings for network security. 4. Output the exact commands to run. 5. Provide the rollback commands in case of failure.
– Execution:
1. User Input: “Harden SSH on Ubuntu 22.04 to allow only key-based auth and limit root login.”
2. AI Output (using the Skill): The AI will generate a step-by-step guide.
Verify configuration sudo nano /etc/ssh/sshd_config Lines to set: PermitRootLogin no PasswordAuthentication no PubkeyAuthentication yes Restart service sudo systemctl restart sshd UFW rules sudo ufw allow from 192.168.1.0/24 to any port 22 proto tcp sudo ufw enable
– Windows Equivalent: For a Windows Server hardening skill, the AI would output PowerShell DSC (Desired State Configuration) snippets or `netsh` commands:
Disable SMBv1 via PowerShell Set-SmbServerConfiguration -EnableSMB1Protocol $false -Force Set firewall rule to restrict RDP netsh advfirewall firewall set rule group="remote desktop" new enable=Yes
4. API Security and Payload Testing with Skills
Cybersecurity professionals often need to test APIs. A “Skills” module can be built to ensure acts as a security tester, generating specific payloads or validating OAuth flows without straying into generic coding help.
Step‑by‑step guide explaining what this does and how to use it.
– The Skill Definition:
API Security Tester Skill
You are an API Security Engineer.
When provided with an OpenAPI (Swagger) spec:
1. Identify endpoints that lack authentication.
2. Generate `curl` commands to test for IDOR (Insecure Direct Object References) by altering IDs in the URL path.
3. Generate `curl` commands to test for SQL injection using single quotes (') and sleep commands.
4. For each test, explain the expected response (200 vs 403) and the risk level.
– Usage Example:
– Input: “Analyze this endpoint: GET /api/user/{id}”
– Output using Skill:
Test for IDOR: Attempt to access user 2 while logged in as user 1 curl -X GET "https://target.com/api/user/2" -H "Authorization: Bearer <token_of_user1>" Test for SQL Injection curl -X GET "https://target.com/api/user/1' OR '1'='1" -H "Authorization: Bearer <token>"
What Undercode Say:
- Modular Automation is Key: The “Skills” feature validates the shift from ad-hoc prompting to modular, reusable AI logic. For cybersecurity teams, this means AI can now enforce departmental standards (CIS Benchmarks, NIST frameworks) automatically, reducing human error in documentation.
- Operational Security (OpSec) Considerations: While building these Skills, professionals must be cautious. Skills that contain sensitive commands or specific network architecture details should be treated as configuration files. They should be stored securely and never shared publicly if they contain internal IP ranges or proprietary remediation steps.
- Bridging the Gap: The ability to generate both Linux and Windows commands within a single, structured workflow allows for a unified approach to hybrid environments. This reduces the cognitive load on analysts who must switch between operating systems during incident response.
Prediction:
The proliferation of AI “Skills” will lead to the emergence of “AI-Driven Security Playbooks” within the next 12-18 months. We will likely see the rise of open-source repositories (similar to GitHub Gists) where security experts share “Skills” for specific tools like Metasploit, Wireshark, or Splunk. This will democratize advanced security analysis, allowing junior analysts to execute complex workflows—such as memory forensics or cloud misconfiguration detection—by simply activating a verified community Skill, provided they strictly control the AI’s permissions to avoid unintended system changes or data leakage.
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Reported By: Danelschwartz %D7%92%D7%99%D7%9C%D7%99%D7%AA%D7%99 – Hackers Feeds
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