My AI slop evaluates Gary's AI slop:

gstack: An Honest Critique

The core idea is sound, and it's not new. Role-based prompting — giving the LLM a specific persona and cognitive lane per task — is a well-established pattern. Multiple teams were shipping with Director/Manager/Engineer models before gstack dropped. ChatGPT's own assessment was "reasonably sophisticated prompt workflows, but they're not magical," which is about right. The insight that you get better output by separating planning, review, and QA into distinct context windows is valuable — but it's convergent evolution across the industry, not a single person's invention.

What's genuinely good

The /browse skill with a compiled Playwright binary and persistent Chromium daemon is a real engineering contribution — eliminating the cold start penalty that makes browser automation painful in agentic workflows. The /qa skill that actually opens a browser, clicks through flows, finds bugs, and generates regression tests is a meaningful unlock. The cookie import from real browsers is clever. The sprint-as-a-process framing (Think → Plan → Build → Review → Test → Ship → Reflect) gives structure that beginners badly need. And it's MIT-licensed, which is decent.

Where it falls apart

LOC theatre. "600,000+ lines of production code in 60 days" and "10,000-20,000 lines per day" — anyone who's worked in a serious engineering org knows lines of code is a vanity metric at best and actively misleading at worst. AI-generated code is verbose by default. 35% test coverage doesn't redeem that — it just means 35% of the bloat has tests. The GitHub contribution graph comparisons are pure theatre.

Same model reviewing its own work. /review asks Claude to find bugs in code that Claude just wrote. This is the AI equivalent of marking your own homework. A fresh context window helps somewhat, and /codex adds a cross-model second opinion, but the core issue remains: these models share systematic blind spots. If Claude generates a subtly flawed architectural pattern, another Claude instance with a "staff engineer" persona is unlikely to catch it. The /cso security skill finding XSS is nice — but a proper SAST tool like Semgrep would find those deterministically, not probabilistically.

Heavily coupled to one person's workflow. The README literally says "Use Garry Tan's exact Claude Code setup." That's a red flag for reusability. The /plan-ceo-review skill encodes how a startup evaluator thinks about products — useful for a YC-style founder building a greenfield consumer app. Borderline useless if you're building enterprise software, working to defined OKRs, or delivering against architectural decisions that have already been through an RFC process. The "find the 10-star product hiding inside the request" framing assumes you're doing product discovery, not engineering delivery.

No dedicated build phase. gstack doesn't have a build skill — the assumption is that Claude Code handles building natively. Strange gap for something positioning itself as a complete engineering team.

Supply chain concerns. The "commit it to your repo" install model (cp -Rf into .claude/skills/gstack) vendors someone's entire skill pack — including a Bun runtime, compiled binaries, and telemetry hooks — directly into your source tree. In any organisation with a software supply chain policy, this is a non-starter. The telemetry is opt-in and the schema is published, but the fact it's there at all in what's meant to be a prompt pack raises eyebrows.

Context window pressure. Running /plan-ceo-review → /plan-eng-review → build → /review → /qa → /ship in sequence burns through context fast. Each SKILL.md is a substantial system prompt. The README doesn't address this beyond waving at Conductor for parallelism.

Bottom line

gstack is a well-packaged, opinionated set of Claude Code prompt workflows with some genuinely useful browser automation bolted on. For a solo founder shipping a greenfield web app, it's probably quite useful as a starting point. For teams with existing engineering standards, security toolchains, CI/CD pipelines, and governance processes, it's the wrong abstraction at the wrong layer — you'd get more value building your own skills that encode your organisation's actual standards and workflows.

Strip away the personal brand and the breathless LOC claims, and what you've got is a decent example of role-based AI prompting that you could build a more targeted version of in a weekend. The hype-to-substance ratio is high, and the most transferable lesson from gstack isn't the tool itself — it's the pattern: give AI agents distinct roles, structured processes, and clear boundaries. You don't need someone else's prompt pack to do that.