I spent 3 weeks building a rulebook for my AI coding agent. Week 1 after launch: 20 stars, 8 forks, and one distribution lesson I didn't see
I'm an engineer, not a marketer. So naturally, I spent 95% of my time on the product and 5% on distribution. Week 1 taught me that ratio is exactly backwards.
Here's what happened — and more importantly, what I built and why it might matter to you.
The problem I was trying to solve
If you've spent serious time with Claude Code or Google Antigravity, you've hit this: the agent does something you didn't ask for. It creates a file. It refactors something adjacent. It makes an assumption and runs with it.
It's not the model's fault. You didn't give it a rulebook.
I spent three weeks building that rulebook. The result is Full Stack HQ — a CLAUDE.md + agent stack that installs in 30 seconds and turns your AI from an unpredictable collaborator into something closer to a disciplined senior engineer.
The core idea is permission-first:
You: "Add JWT authentication"
Agent: Here's my plan:
Phase 1 — Create auth module
Phase 2 — Add guards to protected routes
Phase 3 — Implement refresh token rotation
[APPROVAL NEEDED] Proceed with Phase 1?
You: PLAN APPROVED
Agent: [implements Phase 1 only, then stops]
The agent never acts without your explicit sign-off. It sounds simple. The difference in practice is significant.
What's in the box
10 specialist agents — frontend, backend, database, architect, security, DevOps, and more. Each triggered by name, each with deep context for its domain.
28 skill modules — Next.js, NestJS, Prisma, TypeScript, auth patterns, Docker, GitHub Actions. The agent knows your stack before you explain it.
10 slash command workflows — /plan, /debug, /preview, /orchestrate, /ui-ux-pro-max. Repeatable processes, not one-off prompts.
One install command — works with Claude Code and Google Antigravity IDE out of the box.
curl -fsSL https://raw.githubusercontent.com/sabahattink/antigravity-fullstack-hq/main/install.sh | bash
Week 1 numbers
I launched quietly. No existing audience. No newsletter. No Twitter following worth mentioning.
Metric Value
GitHub stars 20
Forks 8
Clones (14 days) 37
Unique cloners (48h) 22
Dev.to readers 21
Hacker News 6 points
Not viral. But not zero — and the trajectory matters more than the snapshot.
The distribution lesson I didn't expect
I assumed the technical audience would be the easiest to reach. Developers already use Claude Code. They already feel the pain. Just show them the solution, right?
Wrong. Two things surprised me:
- The framing mattered more than the feature set.
"Permission-first workflow" didn't land immediately. People had to think about what it meant. But "your AI asks before it acts" — that clicked in one sentence. Same concept. Different words. Completely different conversion.
- Karma gates are real.
Reddit was my primary distribution plan. Turns out r/ClaudeAI and r/webdev both have karma thresholds that blocked me from posting links on day one. I was sitting on a finished product with nowhere to put it.
The fix: spend the week before launch commenting genuinely in technical threads. Build the number first. It feels backward but it's the actual sequence.
What I'd do differently
Start distribution 3 weeks before launch, not after. The GitHub stars, the Reddit karma, the IH presence — all of it compounds. Trying to build it in parallel with the product means you're always behind.
Pick one audience and go deep before going wide. I tried to reach Claude Code users, Antigravity users, enterprise IT leaders, and vibe coders simultaneously. Spreading that thin meant none of them felt like I was talking specifically to them.
The product page is permanent. The launch day isn't. Product Hunt taught me this — the page lives forever and keeps accumulating traffic. Treating launch day as the only moment is the wrong mental model. Every good comment, every honest update, every genuine reply is still working six months from now.
What's next
Reddit r/ClaudeAI launch post (waiting on karma)
Submit to more awesome-claude lists
v1.1 with a web installer for non-technical users
Enterprise CLAUDE.md template variant
If you're using Claude Code or Antigravity and want to stop being surprised by your agent — give it a look.
And if you've launched a dev tool with no audience before: what was the one channel that actually moved the needle for you?
The strongest insight here is that you’re not really selling a “CLAUDE.md stack.” You’re selling control over agent behavior. That is a much sharper frame because every serious developer using AI agents eventually hits the same fear: the agent starts acting beyond the boundary they had in mind.
“Your AI asks before it acts” is the line I’d build around. It is clearer than permission-first workflow because it immediately describes the pain and the fix. The enterprise angle also becomes more believable from there, because teams do not just need faster coding agents. They need predictable, auditable, approval-based execution.
One thing I’d watch is the Full Stack HQ name. It explains the developer scope, but it may feel too broad and tutorial-like if this becomes a governed agent workflow layer. For a harder AI-devtool/security direction, Davoq.com would carry the product with more infrastructure weight.
This reframe landed. "Control over agent behavior" is more precise than anything I've written so far — it names the actual fear, not the mechanism.
The auditable/approval-based angle for enterprise is something I've been circling but not stating directly. Teams don't just want speed, they want to be able to explain to a manager exactly what the agent did and why. That's a different sale than "better CLAUDE.md."
On the name: you're right that Full Stack HQ skews tutorial. It made sense for the initial positioning (broad config kit for full-stack devs) but if this moves toward governed agent workflows, that name creates the wrong expectation. Davoq is interesting — curious what made that one come to mind specifically. Infrastructure weight is real but it also needs to hint at the control layer, not just the tooling layer.
What would you build first if you were going harder on the enterprise/security direction — the audit log, the approval workflow, or the policy layer?
One practical thought here.
Since you are already deciding between approval workflow, audit logs, policy layer, and the broader enterprise/security direction, this may be worth pressure-testing properly instead of only through thread replies.
I do focused naming and positioning audits for early products: current name risk, category framing, domain/name ceiling, buyer perception, and what the sharper brand direction should be before more users, docs, landing pages, or product memory build around the current name.
For this, the audit would be very specific:
Whether Full Stack HQ can carry governed agent workflows
Whether the product should be framed as approval-first agent control, policy infrastructure, or auditability
Where Davoq fits if the product moves toward enterprise AI-devtool/security infrastructure
What the strongest positioning and naming path should be before the category gets fixed
Not a long consulting thing. Just a sharp written breakdown you can use while deciding the product direction.
I’m doing a few of these at $99 while refining the format. If useful, connect here and I can put together a clear outside read:
https://www.linkedin.com/in/aryan-y-0163b0278/
This comment was deleted 4 months ago
Happy to answer questions about the permission-first architecture or the CLAUDE.md structure if anyone's curious. What's your current AI coding setup look like?