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affaan-m/ai-first-engineering

affaan-m

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output.

global
origin:ECC
New~319
v1.2Saved Jul 14, 2026

AI-First Engineering

Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.

Process Shifts

  1. Planning quality matters more than typing speed.
  2. Eval coverage matters more than anecdotal confidence.
  3. Review focus shifts from syntax to system behavior.

Architecture Requirements

Prefer architectures that are agent-friendly:

  • explicit boundaries
  • stable contracts
  • typed interfaces
  • deterministic tests

Avoid implicit behavior spread across hidden conventions.

Code Review in AI-First Teams

Review for:

  • behavior regressions
  • security assumptions
  • data integrity
  • failure handling
  • rollout safety

Minimize time spent on style issues already covered by automation.

Hiring and Evaluation Signals

Strong AI-first engineers:

  • decompose ambiguous work cleanly
  • define measurable acceptance criteria
  • produce high-signal prompts and evals
  • enforce risk controls under delivery pressure

Testing Standard

Raise testing bar for generated code:

  • required regression coverage for touched domains
  • explicit edge-case assertions
  • integration checks for interface boundaries
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Overall Score

72/100

Grade

B

Good

Safety

95

Quality

68

Clarity

78

Completeness

65

Summary

This skill provides a process framework for engineering teams using AI-assisted code generation, focusing on shifts in planning, review, architecture, hiring, and testing practices. It offers guidance on designing systems that are AI-agent-friendly and establishes expectations for code review and quality assurance in an AI-first environment.

Detected Capabilities

process guidancearchitectural consultationcode review policy definitiontesting strategy recommendationteam hiring and evaluation framework

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

ai-assisted code generationai-first team processagent-friendly architectureai code review standardstesting generated code

Use Cases

  • Design team processes for AI-assisted development workflows
  • Establish code review standards when AI agents generate implementation
  • Define architecture patterns that work well with AI code generation
  • Create hiring and evaluation criteria for AI-first engineering teams
  • Set testing strategies and standards for AI-generated code

Quality Notes

  • Provides actionable guidance organized into clear sections
  • Focuses on high-level process and architectural concerns rather than low-level implementation details
  • Includes concrete review criteria and testing standards
  • Lacks specific examples or case studies to illustrate recommendations
  • No discussion of potential failure modes or anti-patterns
  • Limited guidance on transition strategies for teams moving to AI-first models
  • Does not address tooling, metrics, or measuring success in this model
Model: claude-haiku-4-5-20251001Analyzed: Jul 14, 2026

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Version History

v1.2

Content updated

2026-07-14

Latest
v1.1

Content updated

2026-04-20

v1.0

Seeded from github.com/affaan-m/everything-claude-code

2026-03-16

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