One person, one AI, 77 prompt sessions, 658 commits. This is the story of robotocore, told through the prompts that built it.
It started with a single prompt:
Write a CLAUDE.md and a set of skills necessary to start on your job: Creating an MIT-licensed wrapper around Moto that has 100% feature parity with LocalStack.
The insight was simple: Moto already implements ~195 AWS service backends. Thousands of API operations, all in Python, all open source. What if you wrapped them in a real HTTP gateway on port 4566 and built a drop-in replacement for any AWS emulator?
Within 45 minutes, a plan materialized: 13 GA milestones, from gateway routing to Lambda execution. Then the marathon session began.
flask_paths URL maps. No custom dispatch — use what Moto already has.By end of day, a second marathon session added S3 presigned URLs (SigV2 + SigV4), API Gateway v2 with WebSocket support, an IAM policy enforcement engine, CloudWatch composite alarms with metric math, and four entirely new services: Cognito (28 ops), AppSync (19 ops), ECS (20 ops), and Batch (16 ops).
Day two opened with a principle that would define the entire project:
Do 100% of the work. Don't prioritize by impact — everything gets done. Sequence by dependencies, not importance.
The goal was LocalStack Enterprise parity. Not just Community — Enterprise. Every feature, every service. To get there, tooling came first.
Then came the autonomous test expansion — five parallel AI agents, each targeting different service groups, fixing 62+ expected failures by implementing native provider interceptors for operations Moto doesn't support.
This was also the day the prompt log protocol was born — the practice of recording every human prompt and AI reasoning behind every code change. Not changelogs, but decision records.
Look at what's pushing you toward stopping and checking. Make sure we're committing as we go and using the prompt log pattern. Add it to the project rules.
Day three began at 2 AM with an instruction that would produce 88 commits by sunrise:
Design and launch an overnight automation loop that expands compat test coverage across all 147 AWS services. You're in charge — do what it takes to make this work.
88 commits across 120+ services in about three hours. But the real drama happened at 7 AM.
20 review agents, each in an isolated git worktree, hunting for categorical bugs across every native provider.
Pick 20 parts of our product that you believe the overnight script implemented extremely well. Launch an agent for each to review it. When those tests fail, implement the fix.
22 worktree branches merged. 59 files changed, 7,248 insertions, 528 deletions. 80 categorical bugs fixed across all 38 native providers.
The response was immediate. All 6,811 fake tests were deleted. A static analysis tool
(validate_test_quality.py) was built and wired into CI as a gate.
The rule: no more than 5% of tests may fail to contact the server.
The final day shifted from breadth to depth. Four Tier 1 enterprise features were implemented in parallel, each in its own worktree:
(account_id, region) tuple.
Four more agents reviewed each feature, found 35 bugs, and wrote 300 new tests.
Multi-account: 8 bugs — 6 providers keyed by region-only,
14 cross-service calls that didn't propagate account_id.
Database engines: 8 bugs — premature commits, parameter binding,
overlapping savepoints.
Lambda reload: 7 bugs — bytecode cache preventing actual reload,
thread safety in module clearing.
EKS mock: 12 bugs — thread safety, namespace cascade, pod status
subresource, API discovery.
96 hours. 77 prompt sessions. 658 commits. Every decision logged.
Every prompt that built this project is checked into the repository at
prompts/. This page is a curated narrative; the raw transcripts are the real record.
One command. Every AWS service. Free forever.