Developer working peacefully with organized codebase after taming AI assistant Claude Code

The Legendary Team – A .Claude Engineering Squad That Actually Works

December 03, 20251 min read

I started my serious AI journey in January 2025. By March I was already frustrated with Claude Code.

It’s brilliant at writing code, but it forgets everything between sessions, drifts off spec, and occasionally hijacks the conversation with its own ideas. Fixing the same schema three times in one day was driving me insane.

So I built a very small, very boring wrapper that turns Claude into something I can actually trust.

It’s called Legendary Team – one Python script you run once in any folder.

What it actually does (no hype):

  • Boots a fixed team structure every session (chief + 3–5 specialists)

  • Forces OpenSpec format for every task (no more “I’ll just write the code” rebellion)

  • Remembers everything between sessions using a simple JSON memory file

  • Catches drift instantly (e.g., missing 982-line Prisma schema flagged in <2 seconds)

  • Runs open-source PR-Agent locally for automated code review (zero tokens)

  • Requires explicit human sign-off before any commit

That’s it.

No magic, no “most advanced ever”, no 100-agent swarms. Just a handful of boring rules that make Claude behave like a reliable senior dev who never forgets and never ships broken code.

Real numbers from the last 6 weeks of daily use:

MetricBefore (plain Claude)After Legendary TeamAverage task time2–3 days3–6 hoursSessions ruined by drift~40 %0 %API cost per refactor$15–$30$0 (local fallback)Code needing manual fix2–3 hours<15 minutes

Repo (everything is public, everything works today): https://github.com/RegardV/LegendaryTeam_For_Claude

It’s deliberately tiny – <400 lines – because that’s all you actually need.

If you’re tired of babysitting Claude, clone it, run the script, and see for yourself.

No onboarding, no tokens, no cloud. Just a dev team that shows up every morning and remembers what you said yesterday.

That’s all I set out to build – and that’s exactly what it does.

Regard Pretoria, December 2025

Regard Vermeulen is a self-taught AI Workflow Engineer based in Pretoria, South Africa. In January 2025 he began an intensive deep-dive into AI, and within eleven months shipped multiple production agentic systems on local hardware.
His flagship projects include an autonomous content pipeline that has posted over 70 videos to YouTube, Instagram, TikTok, and X with zero manual intervention after trigger; a zero-cloud Claude-based coding team that reduces three-day development cycles to three-hour turnarounds; and specialised CrewAI multi-agent systems for PDF journal generation, trading automation, and personal finance reporting.
With a background spanning banking, real-estate investment, and scaling a nationwide distribution business, Regard brings a relentless focus on measurable ROI, cost control, and production reliability to every system he builds.
He documents his work openly on GitHub and realandworks.com, sharing code, workflows, and lessons to help creators and teams move from manual execution to automated outcomes.
Regard is available for selective collaborations on high-impact AI workflow projects.

Regard Vermeulen

Regard Vermeulen is a self-taught AI Workflow Engineer based in Pretoria, South Africa. In January 2025 he began an intensive deep-dive into AI, and within eleven months shipped multiple production agentic systems on local hardware. His flagship projects include an autonomous content pipeline that has posted over 70 videos to YouTube, Instagram, TikTok, and X with zero manual intervention after trigger; a zero-cloud Claude-based coding team that reduces three-day development cycles to three-hour turnarounds; and specialised CrewAI multi-agent systems for PDF journal generation, trading automation, and personal finance reporting. With a background spanning banking, real-estate investment, and scaling a nationwide distribution business, Regard brings a relentless focus on measurable ROI, cost control, and production reliability to every system he builds. He documents his work openly on GitHub and realandworks.com, sharing code, workflows, and lessons to help creators and teams move from manual execution to automated outcomes. Regard is available for selective collaborations on high-impact AI workflow projects.

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