Features
Dead code detection (unused functions, imports, classes, variables)
Security vulnerability scanning (SQL injection, command injection)
Secrets detection for hardcoded credentials (AWS, Stripe, etc.)
Quality checks (complexity, nesting, duplicate literals)
AI defect detection: hallucinated imports, phantom calls, insecure defaults, removed controls
Confidence scoring for findings
CI/CD integration: GitHub Actions, tokenless CI
PR gate for blocking high-confidence regressions
Cloud workspace for shared triage and history
Real repo benchmarks: 98.1% recall, 21× fewer false positives than Vulture
Built-in Python framework coverage (Django, Flask, FastAPI, Pydantic)
Agent workflow detection (Claude Code, Cursor, Codex, Copilot)
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
