Context Engineering in Practice
A system that makes AI development repeatable. Hooks enforce behavior. Skills map to design thinking phases. Agents evaluate project health. One kit, distributed across every project.
Two Problems, Not One
Inconsistent Output
Same prompt, different sessions, different implementations. Tailwind one day, raw CSS the next. Same design, different code. No standards.
Forgotten Corrections
You correct a mistake. The AI says "got it." Next session, same mistake. Every correction resets to zero. No memory.
The obvious problem is inconsistency. But the deeper problem is that corrections don't persist. You're training a model that forgets everything you taught it.
Three Versions to Get It Right
Each version solved a real problem. Each revealed the next layer missing.
Workflows
- •Custom commands that read standards files
- •Context loaded at session start when it worked
- •No guarantee the files actually got read
Enforcement
- •Hooks intercept every tool call
- •Brain stores corrections across sessions
- •Awareness monitors for failure patterns
Design Thinking System
- •Skills map to design thinking phases
- •Agents evaluate project health
- •One kit distributes across all projects
The insight: Enforcement handles HOW the AI should work. Design thinking handles WHAT to do and WHEN. Most people build one layer. You need both.
Three Layers
Enforcement at the bottom. Methodology in the middle. Evaluation at the top.
Event-Driven Enforcement
40+ hooks across five categories: safety, tracking, context injection, quality, and lifecycle. Every tool call passes through them.
block-dangerous stops rm -rf and force pushes
enforce-specs blocks edits until you read the spec
mcp-security-scan catches secrets before they leave the machine
Design Thinking Workflow
Skills map to design thinking phases. Each is a method card with forcing functions, not a hardcoded script. GitHub issues capture decisions, not just tasks.
/research → understand the problem first
/build → commitment point: creates branch, starts work
/commit → docs check, commit, push, PR in one flow
Strategic Agents
Two agents that evaluate instead of execute. They run at key moments and create issues, not code.
Context agent (haiku): session start, evaluates project state
Phase evaluator (sonnet): after commits, checks project health
Both file GitHub issues when they spot gaps
The Development Flow
Every task follows the same rhythm. Hooks enforce it at every step.
/researchUnderstand
/defineScope
/ideateOptions
/buildCreate
/testVerify
/reviewQuality
/commitShip
MergeDone
Non-linear. Jump back when you learn something new. Skip ahead when the answer is obvious.
Design Thinking Applied to AI Tooling
Same methodology I use for everything. Understand the problem, build the smallest thing that tests the assumption, iterate.
I used AI for all my development work
Every project, every task. Real usage across six codebases, not a side experiment.
Workflows were unreliable
Custom commands that read standards files. Sometimes the context loaded, sometimes it didn't. No guarantee.
Enforcement solved reliability
Hooks intercept every action. Specs block edits until standards are read. The model can't skip steps.
But enforcement alone had no methodology
The AI knew HOW to follow rules but not WHAT to do or WHEN. I needed structure around the thinking, not just the execution.
Design thinking gave it structure
Skills map to phases. Agents evaluate project health. GitHub issues capture decisions. One kit distributes everywhere.
The kit is three iterations of the same question: what does AI need to be a reliable work partner? Instructions weren't it. Enforcement was necessary but not sufficient. The answer is enforcement plus methodology plus evaluation.
Interested in AI + Systematic Problem-Solving?
I'm exploring AI evaluation, product, and research roles. If you're working on something interesting, let's talk.
Or explore my other work