8 porting operations for cross-language migration

Porting follows a four-step state machine: port_contract anchors the source-to-target migration, port_inventory enumerates every symbol that must move, port_order resolves dependency ordering, and port_manifest tracks progress through init/add/update/remove/note states. This works between any two of the 163 supported grammars.

30 codebase analyzers and 10 pre-built agent skills

The analyzers measure cohesion, coupling, cycles, chokepoints, hotspots, dead code, layers, seams, clusters, fan balance, migration readiness, and type completeness. The pre-built skills — architecture-audit, code-review, refactoring, code-navigation, code-generation, migration-assessment, boundary-analysis, change-impact, health-check, and architectural-refactoring — compose these operations into common engineering workflows.

What stands out

act is the first dev tool that lets an AI agent actually do language-aware code work — not just chat about it.

This is the critical distinction. Before act, AI agents could only perform whole-file rewrites that lost comments, broke formatting, and offered no undo. act gives agents typed, AST-aware operations that preserve code structure, maintain cross-file consistency, and provide instant rollback. The single native Rust binary has no plugin runtime, no package graph, and no supply-chain attack surface — it parses on call and returns results that are never stale.

Worth checking out if…

You're building with AI coding agents and need them to perform real refactoring — not just generate text. You're planning a language migration and want a structured, agent-driven approach that preserves code semantics. You're analyzing large codebases for coupling, dead code, or architectural boundaries and want results without indexing or caching delays. act is free for personal use and works with any MCP-aware client.