CLI tool that audits WordPress site health using Claude Haiku
wp-vitals is a CLI tool that audits a WordPress site's health by checking debug logs, plugin versions, and theme dependencies in one pass. It uses Claude Haiku (Anthropic API) to analyze the data and provide a summary with prioritized action items. It solves the problem of wasting time diagnosing a new client site's issues before starting work.
Visit project ↗Entities
Related
Competitor monitoring tool that tracks pricing, jobs, and changelogs
A free tool that automatically monitors competitors' public signals: pricing pages, job postings, and changelogs. It uses LLMs to summarize changes and alert users, saving solo developers and small teams from manual tracking. Built by a solo developer frustrated with expensive enterprise tools like Klue and Crayon.
Local CLI tool to analyze Claude AI token usage and costs
A Go-based CLI that parses local Claude AI logs to show token usage and cost breakdown by project, session, and model. It helps users understand where their API quota is going and how much they would have spent without caching.
CLI to analyze Claude Code session token costs
Tokenbill is an open-source CLI tool that parses local Claude Code session logs and estimates token costs. It runs entirely offline with no network calls, helping developers understand and optimize their Claude Code usage costs.
Safety layer for AI agents with alerts, kill switch, and dashboard
AgentKavach is a safety layer for AI agents that monitors usage, spending, and behavior. It provides real-time alerts, an automatic kill switch to stop misbehaving agents, and a dashboard with insights. It solves the problem of runaway costs and unintended behavior for teams deploying autonomous AI agents.
whatbroke: CLI that captures context on test/dev server crashes for LLM debugging
whatbroke is a free open-source CLI that wraps any command (e.g., npm test) and, on a crash, captures a bundle of context: the error with parsed stack trace, the diff since the last passing run (recorded as a 'green' commit), and a ranked guess at the responsible file with reasons. This context is designed to be fed to an LLM (like Cursor) to enable accurate debugging, solving the problem of LLMs editing the wrong file when given only a stack trace.