# Nous Research Hermes Agent AI Code Detector Setup: Automating Code Audits Nous Research Hermes Agent AI code detector setup involves configuring the agent to automatically fetch source files and evaluate them using AI detection heuristics. Paired with Fast.io workspaces, developers can automate code check triggers to reduce manual review overhead while maintaining per-file version history. This step-by-step guide explains how to set up the configuration, write a custom checker skill, and track results. Source: https://fast.io/resources/hermes-agent-ai-code-detector-setup/ Last reviewed: 2026-07-14 ## Why AI-Generated Code Requires Automated Auditing According to the SonarSource 2026 State of Code Developer Survey, 96% of developers do not fully trust AI-generated code to be functionally correct as is, yet AI coding tool adoption has reached 84% to 91% by 2026 [SonarSource 2026 Survey]. This trust gap creates a serious operational bottleneck. Developers are committing machine-generated code faster than manual code reviews can verify it. To address this risk velocity, engineering teams are deploying autonomous agents to audit codebases for AI-generated code, identifying patterns, licenses, and security flaws before they reach production. Setting up code check triggers reduces manual review overhead, ensuring that machine-authored contributions undergo rigorous verification. Nous Research Hermes Agent is an open-source AI agent designed for this kind of persistent, autonomous security auditing. Setting up Hermes Agent as an automated code checker requires configuring the agent to fetch source files, parse repository contexts, and evaluate them using AI detection heuristics. Automated auditing solves the problem of security debt by running checks continuously. When a developer submits a pull request, the agent inspects the code blocks, flags potentially insecure suggestions, and reports back. This method keeps the review pipeline moving without requiring senior engineers to read every line of generated boilerplate. ## How to Structure the Code Checker Workspace To configure Hermes Agent for code auditing, developers choose how the agent interacts with repository files. Running a local agent that reads a direct path on a developer machine works for small individual trials, but it fails to scale when multiple agents or remote developers collaborate. Standard options like local disk storage, raw AWS S3 buckets, or consumer cloud storage (like Dropbox or Google Drive) lack the structured context that AI agents need to evaluate code. A shared workspace in [Fast.io workspaces](/product/workspaces/) solves this problem by serving as the central, persistent storage layer for Hermes Agent. Instead of local checkouts, the codebase is stored in org-owned workspaces with [granular permissions](/product/workspaces/), ensuring that source code remains isolated. Fast.io provides a Model Context Protocol (MCP) server that exposes a consolidated MCP toolset via Streamable HTTP at `/mcp` and legacy SSE at `/sse`. By connecting Hermes Agent to this MCP server, the agent can search folders, read files, and write audit reports directly in the workspace. Fast.io's per-file version history logs every file modification, keeping concurrent agent modifications fully auditable. For larger codebases, enabling [Intelligence Mode](/product/ai/) indexes the code, allowing the agent to run semantic search queries with source citations without exhausting context windows. This configuration ensures that the agent works in a secure environment without copying code to a separate VM or database. ## Hermes Agent AI Code Detector Setup Steps Nous Research Hermes Agent runs on local hardware, a virtual private server, or cloud platforms. All configuration files, state data, and skills live in the `~/.hermes/` directory on the host machine. To begin the setup, run the main installer script from the terminal to create the virtual environment and install dependencies: ```bash curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash ``` Once the installation completes, create a dedicated profile for code detection. Using separate profiles prevents the code detector's memory, tools, and configurations from bleeding into other agent roles. Run the profile command: ```bash hermes profile create code-detector ``` Activate the new profile and run the interactive configuration wizard: ```bash hermes setup --profile code-detector ``` The wizard prompts you to select a large language model provider and set API keys. For code analysis, choose a reasoning model like the Claude series or standard GPT models. You can check the profile configuration or switch models manually using the model command: ```bash hermes model set claude-3-5-sonnet ``` Confirm that the environment is healthy by running the doctor utility: ```bash hermes doctor ``` The doctor utility checks API connectivity, configuration syntax, and folder permissions, offering to resolve issues if they are found. ## How to Connect the Model Context Protocol Server To connect Hermes Agent to your Fast.io workspace, you configure the agent to load the Fast.io MCP server. This allows the agent to inspect folders, read files, and write results directly to the cloud. First, generate an API key in your Fast.io console under workspace settings. Next, open the Hermes configuration file located at `~/.hermes/config.yaml` or run `hermes config edit`. Under the `mcp_servers` block, define the Fast.io server configuration: ```yaml mcp_servers: fastio: url: "https://mcp.fast.io/mcp" headers: Authorization: "Bearer your_fastio_api_key_here" ``` This configuration connects Hermes directly to the remote Fast.io MCP server via Streamable HTTP, passing the API key in the authorization header. Test the storage connection by running the agent in interactive mode: ```bash hermes chat --profile code-detector ``` Prompt the agent to inspect the workspace: 'List all folders in my Fast.io workspace.' If the agent returns the folder list from your cloud workspace, the MCP storage layer is connected. Hermes Agent can now read the code repository files stored in Fast.io without downloading them to the local server disk. ## How to Write a Custom Code Auditor Skill Hermes Agent uses a modular skill architecture compatible with the agentskills.io standard. To perform automated code checks, the agent needs instructions on how to identify AI-generated code patterns, such as repetitive comment structures, specific formatting habits, or known LLM code conventions. Create a skill folder in the agent directory: ```bash mkdir -p ~/.hermes/skills/code-auditor ``` Inside this directory, write a markdown file named `SKILL.md`. This file contains the name, description, parameters, and instruction rules the agent executes. Save the following content to `~/.hermes/skills/code-auditor/SKILL.md`: ```markdown ### Code Auditor Skill ### metadata name: code_auditor description: Audits code files in a workspace to detect AI-generated code patterns and security vulnerabilities. version: 1.0.0 ### instructions When triggered, perform the following steps to check the target repository files: 1. Use the Fast.io MCP tools to list files in the target directory. 2. Read each file containing source code (.js, .py, .go, .java, .ts, .cpp). 3. Analyze the code for common signs of AI generation: - Repetitive comments explaining obvious operations. - Inconsistent naming conventions that suggest block copying. - Known placeholder patterns or repetitive helper functions. - Security risks common to LLM code outputs, like hardcoded test credentials or SQL injections. 4. Write a detailed markdown report for each file checked, assigning an AI probability score between zero and one hundred. 5. Save the final report to the workspace under the `audit-reports` folder. ``` Because Hermes Agent scans `~/.hermes/skills/` automatically, the new skill becomes available immediately. In a chat session, you trigger this checker using the slash command `/code_auditor` or by instructing the agent to run the auditor on a specific folder. The agent reads the source code via the Fast.io MCP server, processes the logic, and writes the report back to Fast.io. ## How to Configure Webhook Triggers and Handoffs An automated setup should not require developers to manually run CLI commands. Instead, you can listen to Fast.io's WebSocket events feed or poll the realtime activity feed to trigger code checks when a developer uploads new files or modifies code. When a file upload occurs, Fast.io emits an event containing the file path and metadata. Your server receives this event and passes the file path to Hermes Agent via its messaging gateway API, initiating the audit skill automatically. To organize the audit reports, create a [Metadata View](/product/document-data-extraction/) in your Fast.io workspace. [Metadata Views](/product/document-data-extraction/) turn these audit files into a structured, queryable database. Describe the fields you want extracted, such as 'AI Likelihood Score' (Integer), 'Detected Vulnerabilities' (Boolean), and 'Heuristics Flagged' (Text). The system parses the markdown audit reports, populates a spreadsheet, and allows team leads to filter files with high AI probability scores without reading individual reports. Once the audit completes, team leads verify the results. If the workspace was initialized by an agent, the agent can transfer ownership of the workspace and Metadata Views to a human project manager. The human lead gets full admin rights, while the agent retains developer access to continue runs. Finally, share the structured reports with external security teams using Fast.io branded [Exchange shares](/product/sharing/), which can be configured with expiration dates for secure, time-limited access. ## Frequently asked questions ### How do I set up Hermes Agent to audit code? Setting up Hermes Agent to audit code requires configuring a dedicated agent profile, connecting it to a persistent codebase storage layer via the Fast.io MCP server, and dropping a custom audit skill into the agent's skills directory. The agent then reads files from the workspace, analyzes the syntax, and writes markdown reports. ### Can Hermes Agent detect AI-generated code? Hermes Agent detects AI-generated code by scanning source files for typical machine-authored patterns. It checks for verbose comments that explain obvious code blocks, inconsistent naming conventions from copied blocks, known LLM placeholder text, and common security flaws like hardcoded credentials. ### How does Fast.io protect the source code during audits? Fast.io protects source code by keeping files in org-owned workspaces with granular permission controls. The agent connects to the workspace via an encrypted MCP server protocol using dedicated API keys, eliminating the need to copy repository files to insecure third-party servers or local disks. ### Do I need a separate vector database for large repositories? Separate vector databases are unnecessary when auditing codebases with Fast.io. Enabling Intelligence Mode on your workspace automatically indexes your files for semantic search, allowing Hermes Agent to query relevant code snippets with source citations directly through the MCP interface. ## About Fast.io Fast.io provides shared workspaces where people and AI agents work on the same files, with built-in semantic search and citation-backed chat over what they hold. Agents reach it through a remote MCP server at https://mcp.fast.io/mcp, a REST API at https://api.fast.io/current/, and a command line client published on npm as @vividengine/fastio-cli.