Flagged: sudo usage, reads sensitive env vars. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by dalemoncayo · Codex Skill · ★ 93
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Fullcase Web A Next.js 16 (App Router) + Firebase application used as a reference project for demonstrating AI/LLM code review with Claude Code and OpenAI Codex. The app itself is a lightweight test-case management tool (projects → modules → test cases → test runs); the interesting part of this repo is how it is reviewed. What this repo demonstrates This repository is a working example of how to wire LLM-based code review into a real Next.js + Firebase codebase. It shows: Project rules as context — , , and at the repo root define the architecture, conventions, and review criteria that every AI reviewer reads before producing findings. Custom subagents — and define three specialized reviewers (quality, performance, security) that run in parallel. Skills — contains the shared review checklist, rubric, and a of known false positives to suppress. Slash commands / prompts — and define the review procedure both tools follow. Tool permissions — the Claude GitHub Action () whitelists a narrow set of commands. The Codex Action () runs with and . Triggered reviews — commenting or on any PR runs the res
| Stars | 93 |
| Forks | 0 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 63.7893218643541/100 |
| Last Updated | 2026-04-23 |
| Created | 2024-05-24 |
| Platforms | browser, claude-code, codex, node |
| Est. Tokens | ~17k |
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fullcase-web is A practical example of integrating AI code review into a real-world codebase. Built with Next.js and Firebase, this repo highlights structured prompts, reusable review skills, and parallel subagent ex. It is categorized as a Codex Skill with 93 GitHub stars.
fullcase-web is primarily written in TypeScript.
You can find installation instructions and usage details in the fullcase-web GitHub repository at github.com/dalemoncayo/fullcase-web. The project has 93 stars and 0 forks, indicating an active community.
The top alternatives to fullcase-web on Agent Skills Hub include theorist. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: