# 🐝Four deterministic tools for the agent-coding era

> Source: <https://dev.to/diya730/four-deterministic-tools-for-the-agent-coding-era-40ga>
> Published: 2026-07-18 16:06:28+00:00

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😁.