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Data Engineering Path  ·  Data & AI
AWS CORE PLATFORM CASE STUDY

Repo Architecture, Explained

What's actually inside aidlc-workflows (v2.8.1), and why it's split the way it is.

In plain English: Almost all of the "smarts" live in one shared place (core/). Each tool (Claude Code, Cursor, etc.) just gets a tiny translator folder on top, so the same brain works everywhere without being rewritten seven times.


Top-Level Layout

Folder What's in it
core/ The engine — stages, agents, hooks, knowledge, sensors, scopes. Shared by every tool.
harness/<name>/ A thin adapter per tool — one manifest + a few tool-specific files.
plugins/<name>/ Optional add-ons — extra stages, sensors, agents.
scripts/ Build tooling. package.ts is the compiler.
docs/ guide/ · harness-engineering/ · reference/
tests/ Smoke, unit, integration, e2e — one matrix per harness.
dist/, dist-release/ Generated, git-ignored — appear only after a build.

Why Split This Way

flowchart LR
    C["core/agents/architect.md"] -->|manifest.ts maps it| H1["dist/claude/.claude/agents/"]
    C -->|same file| H2["dist/cursor/.cursor/agents/"]
    C -->|same file| H3["dist/kiro/.kiro/agents/"]

One core/ file, mapped into every tool's own folder shape by that tool's manifest.ts (a small "translation map" — it says "this core file goes here in Cursor's format, there in Claude's format"). Rebuilding must produce byte-identical output (run the build twice, get the exact same files, down to the last byte) — enforced by bun scripts/package.ts --check in CI (an automatic check that runs on every change, so nobody can accidentally break that guarantee). Most of the intelligence lives in the ~30% of the repo that's shared; harness/<name>/ is just a thin costume on top.


The Methodology, in Numbers

A quick glossary before the table: a Phase is one of the 5 big chapters of building something (Ideation, Inception, Construction, Operation, plus Initialization). Each phase is broken into Stages — smaller, checkable steps, like "write the requirements" or "design the database." An Agent is one of the AI's specialist personas (e.g., "architect," "tester"). A Workflow profile is a preset — how deep or how skipped-over the process is for a given job (a tiny bug fix doesn't need all 33 stages; a new product probably does). An Audit event is one line in the permanent record — "this stage started," "this file was approved," and so on.

Phases 5
Stages 33
Agents 14 (11 experts + 2 reviewers + 1 composer)
Workflow profiles 11
Audit events 95

Human approval gates sit at every stage boundary — meaning after each of those 33 steps, the AI stops and waits for you. You always get exactly two buttons: Request Changes (send it back to redo this step) or Continue to Next Stage (looks good, move on).


How We Verified This

Read straight from the source — manifest.ts files, core/templates/onboarding.md, the per-harness guides under docs/guide/harnesses/ — not from marketing copy.


Continue to: How We Use This Repo or What It Installs, Tool by Tool

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