Risk Assessment For Changes

via “risk scoring and consequence severity classification”

MCP server for AI agents to evaluate consequences before destructive actions. Analyzes Terraform plans, shell commands, and MCP tool calls.

Unique: Implements quantitative risk scoring for infrastructure and command consequences as part of MCP server, enabling agents to make risk-aware decisions. Uses multi-factor scoring model considering impact scope, reversibility, and resource criticality.

vs others: Provides automated risk scoring integrated into agent workflows, whereas manual risk assessment is subjective and time-consuming; recourse-cli enables consistent, quantitative risk evaluation.

via “deployment risk assessment and change impact analysis”

AI for every step of SW development lifecycle

Unique: Integrates with GitLab's CI/CD pipeline and deployment history to assess risk based on actual system state and change patterns rather than analyzing changes in isolation, enabling risk scores that reflect real deployment consequences

vs others: More contextual than generic change impact tools because it understands GitLab's deployment pipeline, service dependencies, and historical deployment patterns to provide risk assessments specific to the organization's infrastructure

via “risk-assessment-for-changes”

via “upgrade-risk-assessment”