7 posts carrying this tag.
Skills are the best thing to happen to agent workflows in the last year, and we write them ourselves. This is not an argument against using them. It is an argument about what category of thing a skill is, because a large number of teams are now treating a code review skill as the answer to whether AI generated code is safe to merge, and a skill structurally cannot be that.
CodeGraph went from launch on 18 January 2026 to roughly 47,000 stars in five months by mapping symbols and call edges locally, with no server and no cloud embedding call. It deserved every one of those stars. It also answers one class of question completely and another class not at all, and the second class is where production breaks.
The deepest MCP integration anybody has shipped, with 16 tools, 7 resources, skills and Claude Code hooks, running with zero server on a local graph. The developer experience is the best in the category and that is why it grew. Two things are worth thinking through before you build anything organisational on top, and only one of them is technical.
Local deterministic parsing over 36 tree sitter grammars, typed nodes, provenance tagged edges, Apache 2.0, no vector store and no telemetry. That is a clean design and the privacy position is better than most of the category. The problem starts the moment your organisation has more than one repository, and it is not a problem more parsing solves.
Of every commercial tool in automated code review, Greptile made the closest architectural call to ours: index the whole repository first, then review the change against it. That decision is why it catches 82% of bugs against CodeRabbit's 44%. It also arrives with about 11 false positives per run, and the shape of those false positives tells you exactly what an index of symbols can and cannot know about intent.
Repomix is the leader in context packing, with 26,000 stars and 255,000 npm downloads a month, and its tree sitter compression genuinely cuts token count by about 70%. Apply that to 10 million files and you go from three orders of magnitude short of the context window to slightly less than three orders of magnitude short. Every tool in this category publishes a token reduction number. None of them publishes a correctness number.
Serena is the best open source symbol level tool for agents, with 25,200 stars, 170 contributors and over 40 languages behind a clean LSP to MCP design. A language server gives you exact call edges and zero intent. In the MSR 2026 dataset of rejected agentic pull requests, only 36% failed on the code itself and 31% failed on a decision the project had already taken, which is the third no symbol table can reach.
