shkSecurity Harness Kit

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Introduction

What security-harness-kit (shk) is, the problems it solves for AI-assisted development, and how the pieces fit together.

On this page

shk is a command-line tool that finds secrets and PII in a project and stops them from reaching AI coding tools, Git commits, and generated files. It runs on your machine and never sends file contents anywhere. It can also encrypt .env files and inject the decrypted values only into the command you run.

shk scan .
bash

Example output:

3 findings

HIGH  secret.openai_api_key  src/app.ts:12    Possible OpenAI API key detected
MED   pii.ja.phone  config/dev.ts:5    Japanese phone number pattern detected
MED   pii.en.ssn  docs/test.md:8    US SSN pattern detected
Failed: findings at or above high (max high).
text

Why

AI coding agents read project files, run shell commands, and write new files from whatever they were given. shk sits in front of those steps: it scans what the agent is about to read or send, masks what you paste into a prompt, and refuses commands such as cat .env before they run.

Scans, masking, and hook checks never make a network request, and audit logs store metadata only. Detection is pattern-based, so treat shk as the check at the AI-tool boundary rather than as your only secret scanner.

What you can do with shk

  • Scan project paths and Git-staged files for common secrets and PII across source code, Markdown, plain text, Office documents (.docx, .xlsx, .pptx), and text-layer .pdf files.
  • Mask sensitive content from stdin, text files, and Office documents before pasting it into a prompt.
  • Pseudonymize CSV, xlsx, and text data with deterministic tokens so shared datasets stay joinable, with an encrypted restore map for bringing originals back.
  • Encrypt .env files, store private keys in the configured secret store, and run commands with decrypted values injected only at runtime.
  • Push dotenv payloads to AWS Secrets Manager or GCP Secret Manager through the provider CLIs, with a PII scan and lint before anything is written.
  • Install Git pre-commit hooks that block staged secrets.
  • Install managed AI hooks for Claude Code, Cursor, Codex, GitHub Copilot, Antigravity, and Windsurf, with an action guard that blocks risky operations before they run.
  • Review metadata-only audit logs to understand blocked hook activity without storing detected values.
  • Audit MCP client configurations statically for unsafe package, credential, HTTP, and filesystem settings.
  • Generate a GitHub Actions workflow that runs shk scan on every pull request.
  • Diagnose ignore file and .env safety coverage.
  • Deploy AI agent skills to Claude Code, Codex, Cursor, GitHub Copilot, Antigravity, and Windsurf project directories.
  • Use the shk Desktop app for local scanning, masking, setup, and audit review with a GUI.

How the pieces fit together

Layer What it protects Entry point
Policy A single shk.toml defines rules, thresholds, masking, and action guard behavior per project. shk init, Configuration
Scan Finds secrets and PII in files, staged changes, or Git history. shk scan
Mask Redacts or pseudonymizes findings in text and documents so they can be shared safely. shk mask, shk clipboard, shk pseudonymize
Hooks Blocks or audits risky content and actions inside Git and AI coding tools. shk hooks install, shk hooks install-ai
Env Encrypts dotenv files, injects values only at runtime, and pushes them to cloud secret managers. shk env, shk secrets push
CI Runs the same scan on every pull request with a pinned, verified release. shk ci init github
Desktop A GUI for scanning, masking, setup automation, and audit review. Desktop App

Where to go next

  • Installation — install the CLI or the desktop app.
  • Quick Start — create a policy, run a scan, and install hooks in a few minutes.
  • Commands — the full CLI reference.
  • Detection Model — how findings are classified and what is covered.