Inkog Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Inkog Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Security companion for AI agent development in Claude, Cursor, and Claude Code.
Ask your AI pair-programmer to build an agent. Inkog checks it as you code — scanning for vulnerabilities, explaining findings in plain English, verifying AGENTS.md governance, and auditing agent-to-agent delegation. All inside the same conversation, no context switch.
Available in Claude Desktop, Cursor, Claude Code, ChatGPT, and any MCP-compatible client.
Inkog is designed to live inside the conversation where you build the agent — not as a post-hoc gate:
"Scan this with Inkog and show me any CRITICAL or HIGH findings.""Explain the top finding. What's the risk, and how do I fix it?""Verify my AGENTS.md against the code" and "Audit the agent-to-agent delegation".Read the full walkthrough: Building Secure AI Agents with Claude Code and the Inkog MCP.
inkog-io/inkog@v1 to GitHub Actions for automated security gates on every PRAdd to your claude_desktop_config.json:
{
"mcpServers": {
"inkog": {
"command": "npx",
"args": ["-y", "@inkog-io/mcp"],
"env": {
"INKOG_API_KEY": "sk_live_your_api_key"
}
}
}
}Add to your Cursor MCP settings:
{
"mcpServers": {
"inkog": {
"command": "npx",
"args": ["-y", "@inkog-io/mcp"],
"env": {
"INKOG_API_KEY": "sk_live_your_api_key"
}
}
}
}npm install -g @inkog-io/mcpINKOG_API_KEY environment variable| Tool | Description |
|---|---|
inkog_scan | Static analysis for logic flaws and security risks |
inkog_verify_governance | Validate AGENTS.md declarations match actual code behavior |
| Tool | Description |
|---|---|
inkog_compliance_report | Generate EU AI Act, NIST, OWASP compliance reports |
inkog_explain_finding | Get detailed remediation guidance for findings |
inkog_audit_mcp_server | Audit any MCP server before installation |
inkog_generate_mlbom | Generate ML Bill of Materials (CycloneDX, SPDX) |
| Tool | Description |
|---|---|
inkog_audit_a2a | Audit Agent-to-Agent communications |
Static analysis for AI agent code - finds logic flaws and security risks.
Arguments:
path (required) File or directory path to scan
policy (optional) Analysis policy: low-noise, balanced, comprehensive, governance, eu-ai-act
output (optional) Output format: summary, detailed, sarifExample: "Scan my LangChain agent for logic flaws"
Validate that AGENTS.md declarations match actual code behavior. This is Inkog's unique differentiator - no other tool does governance verification.
Arguments:
path (required) Path to directory containing AGENTS.md and agent codeExample: "Verify my agent's governance declarations"
Generate compliance reports for regulatory frameworks.
Arguments:
path (required) Path to scan
framework (optional) eu-ai-act, nist-ai-rmf, iso-42001, owasp-llm-top-10, all
format (optional) markdown, json, pdfExample: "Generate an EU AI Act compliance report for my agent"
Get detailed explanation and remediation guidance for a security finding.
Arguments:
finding_id (optional) Finding ID from scan results
pattern (optional) Pattern name (e.g., prompt-injection, infinite-loop)Example: "Explain how to fix prompt injection vulnerabilities"
Security audit any MCP server from the registry or GitHub.
Arguments:
server_name (optional) MCP server name from registry (e.g., "github", "slack")
repository_url (optional) Direct GitHub repository URLExample: "Audit the GitHub MCP server for security issues"
Generate a Machine Learning Bill of Materials listing all AI components.
Arguments:
path (required) Path to agent codebase
format (optional) cyclonedx, spdx, json
include_vulnerabilities (optional) Include known CVEs (default: true)Example: "Generate an MLBOM for my AI project"
Audit Agent-to-Agent communications for security risks.
Arguments:
path (required) Path to multi-agent codebase
protocol (optional) a2a, crewai, langgraph, auto-detect
check_delegation_chains (optional) Check for infinite loops (default: true)Example: "Audit my CrewAI multi-agent system for security risks"
Inkog works with all major AI agent frameworks:
All configuration is done via environment variables:
| Variable | Description | Default |
|---|---|---|
INKOG_API_KEY | Your API key (required) | - |
INKOG_API_URL | API base URL | https://api.inkog.io |
INKOG_API_VERSION | API version | v1 |
INKOG_API_TIMEOUT | Request timeout (ms) | 30000 |
INKOG_LOG_LEVEL | Log level | info |
INKOG_LOG_FORMAT | Log format (json/text) | json |
# Install dependencies
npm install
# Build
npm run build
# Run in development mode
npm run dev
# Run tests
npm test
# Lint
npm run lintMost AI agent security tools run after the code is written. Inkog lives inside the conversation where you build the agent — so findings get fixed before they land in a PR, not three weeks later.
Inkog is the only tool that can validate your agent's governance declarations against its actual code behavior. This is essential for:
Unlike traditional code scanners (Snyk, Semgrep, SonarQube), Inkog understands AI-specific issues:
Inkog's Universal IR (Intermediate Representation) works with any agent framework. Add one integration, get analysis for all frameworks.
Apache-2.0 - see LICENSE
Built with security by Inkog.io
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.