🧩 vs · browser-extension × copilot

GitHub Copilot vs GreatCTO for browser extension

Short answer: both. GitHub Copilot tight VS Code / JetBrains integration with sub-second suggestions. GreatCTO orchestrates the SDLC around it — gates, parallel reviewers, archetype-specific compliance. Same plugin works inside GitHub Copilot.

$ npx great-cto init 🧩 browser-extension archetype →
What each does well

Different layers of the same problem.

GitHub Copilot

  • ▸tight VS Code / JetBrains integration with sub-second suggestions
  • ▸Category: in-IDE autocomplete + chat
  • ▸Where it stops: at the code. Doesn't enforce gates, doesn't run specialist reviewers, doesn't carry memory across sessions.

GreatCTO on top of GitHub Copilot

  • ✓34 specialist agents (architect → pm → senior-dev pool → reviewers → devops → l3-support)
  • ✓Auto-detects browser-extension archetype → wires the right compliance gates
  • ✓Two human gates per feature (plan, ship) — everything in between runs unattended
  • ✓Memory layer: lessons + decisions persist across sessions and projects
  • ✓Copilot autocompletes individual lines. GreatCTO orchestrates whole-feature delivery: architect, plan, parallel implementation, review, deploy. They run side by side.
Architecture · browser-extension

What gets wired automatically when GreatCTO detects browser-extension.

Run npx great-cto init in your browser-extension project. GreatCTO scans manifests, picks the archetype, attaches the right reviewer agents and compliance gates. You don't write the gates; you override them if your specifics differ.

STAGE 1 · PLAN

architect

Drafts ARCH.md + ADR + cost estimate. You approve scope at gate:plan. No implementation starts before your approval.

STAGE 3 · IMPLEMENT

senior-dev pool (parallel)

GitHub Copilot does the editing. GreatCTO orchestrates which agents claim which tasks (from the PM decomposition), runs them in isolated worktrees, and feeds the diff to reviewers.

STAGE 5 · REVIEW

5 reviewers in parallel

qa-engineer · security-officer · performance-engineer · browser-extension-reviewer · code-reviewer. Verdicts aggregate to a single APPROVED / BLOCKED chip at gate:ship.

STAGE 7 · OPERATE

l3-support + memory loop

P0 incidents extract a lesson. Pattern hash + detection order written to .great_cto/lessons.md. Next iteration's agents read this in Step 0.

Full state machine with every node clickable to its agent on GitHub: /architecture.

When to pick which

Decision tree.

GitHub Copilot alone is enough if

  • You're prototyping; production isn't in scope.
  • The codebase is small enough that one human can review everything end-to-end.
  • No regulated data flows (no PCI, no PHI, no EU AI Act high-risk).
  • You don't need cross-project memory of past incidents.

Add GreatCTO if

  • You ship in a regulated industry (fintech, healthcare, voice-AI, gov, …).
  • Reviews are the bottleneck — you want 5 specialist reviewers in parallel instead of one human + one model.
  • You want explicit gates and an audit trail (SOX, SOC 2, EU AI Act post-market monitoring).
  • You want to compound lessons across features and projects.
Receipts

Don't take my word for it.

01 · ARCHITECTURE

Live state machine

Every box on the diagram is a clickable link to the agent's source on GitHub.
02 · PROOF

One real run, full timeline

Voice-AI pack rollout, 14 timeline steps, ~$3.40 LLM cost, 47 e2e assertions, public artifacts.
03 · METHODOLOGY

94 % MTTR claim, audited

47 paired P0 incidents · 4 memory-miss cases documented · raw data under NDA.
Install

Works in GitHub Copilot today.

$ npx great-cto init
✓ scanning manifests…
✓ archetype: browser-extension
✓ adapting for: GitHub Copilot
✓ 34 agents ready

Free, MIT, runs locally. You pay your own LLM API. No SaaS dashboard, no telemetry by default.