🐝Four deterministic tools for the agent-coding era

A developer built four deterministic developer tools that use static analysis and graph algorithms instead of LLM calls. The tools include a linter for SKILL.md files, an MCP schema change monitor, a PR impact analyzer, and a swarm trace viewer for debugging multi-agent orchestrations. All are MIT-licensed and available on GitHub.

Over the past few weeks I built four small, focused developer tools — all sharing one idea: as more of our code gets written and reviewed by AI, we need ways to check that work that don't themselves require an LLM call. No API cost, no black-box judgment, just static analysis and graph algorithms doing exactly what they're told. Here's what each one does. skillcheck An ESLint-style linter for SKILL.md files — the instruction files that Claude Code, Codex, and Cursor agents read to learn a skill. It checks frontmatter completeness, catches broken relative links, flags descriptions that are too short or too long, and enforces a token budget so a skill file doesn't blow out an agent's context window before it even starts working. Ships as a CLI and a GitHub Action. github.com/DIYA73/skillcheck mcp-schema-watch MCP servers can change their tool schemas without warning, and if you're depending on one, that breaking change shows up as a silent failure in production. This polls the MCP servers you configure, diffs each tool's schema against the last known snapshot, and tells you whether a change is breaking or just additive — with Postgres history, BullMQ-scheduled polling, a REST API, and Slack alerts on breaking changes only. github.com/DIYA73/mcp-schema-watch pr-blast-radius The deterministic version of "does this PR touch files it shouldn't." Parses the real AST of every file in the repo, builds an import graph, and checks which changed files are actually connected to each other — versus which ones look like unrelated scope creep. Runs as a GitHub Action that comments on the PR and updates that comment on every push instead of spamming new ones . github.com/DIYA73/PR-Blast-Radius- swarm-trace-viewer The newest one, and still just the foundation layer. When an orchestrator fans out into hundreds of subagents, finding out where a run actually went wrong is hard — was this failure the real cause, or just a downstream cascade from something else? This builds the agent tree from a flat event list, classifies every failure as a root cause or a cascade, and flags statistical outliers among sibling agents with high-fan-in "hub" nodes excluded so one shared dependency doesn't make everything look connected . Includes a fully deterministic synthetic trace generator for testing and demos, since there's no public 1,000-agent orchestrator to record real traces from yet. Live streaming WebSockets, Redis pub/sub, Postgres history and the actual tree/timeline UI are next. github.com/DIYA73/swarm-trace-viewer All four are MIT licensed and open for issues or contributions. If you're building with agents and one of these solves a problem you have, I'd love to hear about it😁.