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Diagnose AI workflow nodes for failure, security, and handoff risks.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.minjikim89/fde-agent" MCP server from askskill: Run: claude mcp add --transport http 'io-github-minjikim89-fde-agent' 'https://fde-mcp.mj-mcp-tools.workers.dev/mcp'
Please inspect this AI support workflow: user question → intent classification → knowledge base retrieval → response generation → human handoff. Analyze failure, security, and handoff risks for each node, and provide a red/amber/green rating with improvement suggestions.
A node-by-node risk assessment with red/amber/green ratings, causes, and optimization recommendations.
Analyze this internal AI automation flow: email intake → content extraction → risk judgment → auto-generate approval notes → send to manager. Identify possible failure points, permission or data leakage issues, and unclear human handoff steps, then output a red/amber/green status for each node.
A node-level evaluation focused on process reliability, security, and handoff clarity.
This is a multi-agent AI workflow: planner agent → retrieval agent → writing agent → review agent → publishing agent. Before launch, diagnose each node for failure risks, security concerns, and inter-agent handoff risks, and mark red/amber/green priorities.
A checklist of node risk levels, key issues, and pre-launch remediation priorities.
No documentation provided
Check the source repo for usage and examples.
Authorize AI agent actions with policy, approval, and audit trails.
Automatically redact secrets before AI coding agents see files or shell output.
Provides exactly-once guards for AI agents to prevent duplicate actions on retries.
Protect AI agents with scans for injections, leaks, risky URLs, and passwords.
Scan AI coding workflows for code, secrets, dependency, and tool security risks.
Set AI agent budgets, enforce spend limits, and audit every decision.