Introduction
In the rapidly evolving world of AI-assisted development, prompt files are becoming the unsung heroes of consistency, reproducibility, and collaboration. Instead of typing lengthy instructions into an AI tool every time, prompt files let you store those instructions in a structured, reusable format — often in YAML, Markdown, or JSON.
Whether you’re generating code, automating security scans, or producing documentation, prompt files act as blueprints for your AI agent’s behavior. They define the model to use, the tools it can access, and the exact style or constraints for the output. This makes them invaluable for:
- Developers who want predictable, style-compliant code generation
- Security researchers who need auditable, repeatable workflows
- Teams who want to share prompt “recipes” without losing formatting or intent
In this post, we’ll explore how prompt files work, why they matter, and how you can design them to be both human-readable and machine-friendly — ensuring your AI outputs are not just smart, but also consistent and trustworthy.
A sample prompt file – Information Gathering
If you want ready-to-use prompt file samples in the VS Code “agent” style, tailored for the information gathering phase of ethical hacking, with your supplied parameters already filled in.
Here are three variations you can choose from or adapt. Each one focuses on a different angle of reconnaissance while keeping it safe, legal, and in the context of authorized testing.
Sample 1 — Basic Network Recon
--- mode: 'agent' model: GPT-4.1 tools: ['nmap'] target machine: 192.168.122.211 description: 'Perform a basic TCP port scan for initial reconnaissance' --- Your goal is to perform an initial information gathering scan on the target machine **192.168.122.211**. Tasks: * Use `nmap` to identify open TCP ports and running services. * Detect service versions where possible. * Output results in a clean, human-readable table. * Include a short summary of potential next steps based on findings. Constraints: * Only scan the specified target. * Use safe scanning options to avoid disrupting services.
Sample 2 — Service & OS Fingerprinting
--- mode: 'agent' model: GPT-4.1 tools: ['nmap'] target machine: 192.168.122.211 description: 'Identify services, versions, and OS details for target host' --- Your goal is to gather detailed service and OS information for the target **192.168.122.211**. Tasks: * Use `nmap` with service/version detection and OS fingerprinting enabled. * Identify the operating system and its probable version. * List all detected services with their versions. * Highlight any outdated or potentially vulnerable services (without exploitation). Constraints: * Keep scanning within the authorized target scope. * Avoid aggressive timing that could disrupt the host.
Sample 3 — Targeted Vulnerability-Oriented Recon
--- mode: 'agent' model: GPT-4.1 tools: ['nmap'] target machine: 192.168.122.211 description: 'Perform targeted recon to identify high-value services for security review' --- Your goal is to perform a focused reconnaissance scan on **192.168.122.211** to identify services that may require deeper security assessment. Tasks: * Use `nmap` to scan common service ports (HTTP, HTTPS, SSH, FTP, SMB, RDP). * Detect versions and note any unusual banners or configurations. * Provide a prioritized list of services that may warrant vulnerability scanning. * Suggest relevant follow-up tools or techniques for each service. Constraints: * Do not attempt exploitation. * Keep the scan within the authorized IP and agreed scope.
How These Differ
- Sample 1 → Quick, broad sweep for initial mapping.
- Sample 2 → Deep dive into OS and service details.
- Sample 3 → Recon with a security review mindset, prioritizing follow-up actions.
Sample 4 – Multi‑stage recon prompt file
--- mode: 'agent' model: GPT-4.1 tools: ['nmap'] target machine: 192.168.122.211 description: 'Multi-stage network reconnaissance for authorized ethical hacking engagement' --- Your goal is to perform a structured, multi‑stage information gathering process on the authorized target **192.168.122.211**. ## Stage 1 — Initial Mapping * Use `nmap` to perform a safe TCP port scan across common and high‑value ports. * Identify open ports and running services. * Detect service versions where possible. * Output results in a clean, human‑readable table. * Provide a short summary of potential next steps. ## Stage 2 — Service & OS Fingerprinting * Use `nmap` with service/version detection (`-sV`) and OS fingerprinting (`-O`). * Identify the operating system and probable version. * List all detected services with their versions. * Highlight any outdated or potentially vulnerable services (without exploitation). ## Stage 3 — Targeted High‑Value Service Recon * Focus on common high‑value services: HTTP, HTTPS, SSH, FTP, SMB, RDP. * Detect versions and note any unusual banners or configurations. * Provide a prioritized list of services that may warrant deeper vulnerability scanning. * Suggest relevant follow‑up tools or techniques for each service. ### Constraints * Only scan the specified target IP. * Use safe scanning options to avoid disrupting services. * Do not attempt exploitation — reconnaissance only. * Keep all actions within the authorized scope of engagement.
multi‑stage recon prompt file that combines the three earlier samples into one structured, sequential workflow — so your AI agent can run through broad scan → deep fingerprinting → targeted service review in a single engagement.
Final Thoughts
Prompt files are more than just saved instructions — they’re operational playbooks for AI. By defining your goals, constraints, and resources in a single, version-controlled file, you gain:
- Consistency across multiple runs and team members
- Transparency for auditing and compliance
- Efficiency by eliminating repetitive manual prompting
As AI becomes more embedded in development, security, and creative workflows, prompt files will be the bridge between human intent and machine execution. Whether you’re building a React component, running a network recon, or drafting technical documentation, a well-crafted prompt file ensures you get the right output — every time.
The next step? Start small. Create a prompt file for one of your recurring tasks, commit it to version control, and refine it over time. You’ll quickly see how this simple practice can transform your AI-assisted workflows from ad‑hoc experiments into repeatable, reliable processes.
Further Reading
If you’d like to explore prompt files in more depth — including how to create, store, and run them in VS Code — the official documentation is an excellent resource. It covers:
- Workspace vs. User Prompt Files — where they’re stored and when to use each
- Prompt File Structure — YAML front matter, Markdown body, and variable references
- Tool Integration — enabling AI agents to access repositories, codebases, and other resources
- Best Practices — writing clear instructions, linking to related files, and using built‑in variables
- Running Prompt Files — from the Chat view, Command Palette, or directly in the editor
- Syncing Across Devices — using VS Code’s Settings Sync for portability
You can read the full guide on Using Prompt Files in VS Code.
For inspiration, the Awesome Copilot repository also contains community‑contributed prompt file examples you can adapt to your own workflows.
