Agentic Engineering Toolkit#
A minimal, portable toolkit of practices, templates, and skills for rigorous AI-assisted development. Built for personal use across any project and any CLI-based agent harness (Claude Code, pi, opencode, etc.).
The goal: have a single, complete setup that can be dropped into any project to enforce disciplined agentic engineering — TDD, adversarial review, fresh contexts, and human-in-the-loop commits — without reinventing the wheel each time.
Workflow#
- Init — Set up a repo with
context-init(project documentation) andagent-container(isolated dev environment) - Work — Launch the agent container with a task, follow the core loop: spec, failing tests, minimal implementation, adversarial review, commit
- Maintain — Review state of work, clean up, update documentation
Structure#
skills/— Portable markdown instruction sets. Each is a self-contained practice (with aSKILL.md) that can be loaded into any agent harness.bin/— Standalone scripts (e.g., adversarial review via a second model's API).
The Core Loop#
Spec ──→ Failing Tests ──→ Minimal Implementation ──→ All Tests Pass
↑ │
│ ↓
│ Adversarial Review
│ (fresh context, hostile)
│ │
│ ┌─── hallucinating? ──→ DONE (converged) ───→ Commit
│ │
└── real findings ──→ Feed back to Builder (fresh session)
Exit condition: When the adversary starts inventing problems that don't exist in the code, you've converged. Ship it.
Principles#
- Specs are truth. Tests serve specs. Code serves tests.
- Tests before code. No implementation without a failing test.
- Fresh context per task. Long sessions degrade. Commit and restart.
- Different model for review. Cognitive diversity catches blind spots.
- Fresh context per review. No relationship drift. Every roast is the first roast.
- Hallucination = exit signal. When the adversary makes things up, you're done.
- You commit manually. The agent proposes. You dispose.