Ai First Engineering
Define how a solo or small team plans, reviews, and structures codebases when agents write most of the implementation.
Overview
AI-First Engineering is an agent skill most often used in Build (also Ship, Operate) that defines process, review focus, and agent-friendly architecture for teams where AI agents generate most implementation output.
Install
npx skills add https://github.com/affaan-m/everything-claude-code --skill ai-first-engineeringWhat is this skill?
- Three process shifts: planning over typing speed, evals over anecdotes, review focus on system behavior
- Agent-friendly architecture checklist: explicit boundaries, stable contracts, typed interfaces, deterministic tests
- AI-first code review lens: regressions, security, data integrity, failure handling, rollout safety—not style nitpicks
- Hiring/eval signals: decomposition, measurable acceptance criteria, high-signal prompts, risk controls under pressure
- Raised testing bar for generated code: regression coverage, edge-case assertions, integration boundary checks
- 3 explicit process shifts (planning, evals, review focus)
- 4 architecture requirements for agent-friendly systems
- 5 AI-first code review focus areas
Adoption & trust: 4.6k installs on skills.sh; 210k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You ship fast with agents but lack a consistent model for planning, architecture, review, and tests—so quality and risk feel anecdotal instead of measured.
Who is it for?
Solo builders or two-person teams standardizing how they plan, review, and test when agents write most patches.
Skip if: Builders who only need a one-off integration snippet with no team process or eval discipline.
When should I use this skill?
Designing process, reviews, and architecture for teams shipping with AI-assisted code generation.
What do I get? / Deliverables
You get explicit process shifts, review priorities, architecture rules, and a higher testing standard tailored to generated code so agents implement against clear contracts and evals.
- Adopted AI-first review checklist and architecture norms
- Raised testing expectations for agent-touched code
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Build/PM because the skill centers on planning quality and decomposing work before agents implement—where solo builders first adopt an AI-first operating model. PM subphase matches process design, acceptance criteria, and eval coverage rather than a single integration or frontend task.
Where it fits
Turn a fuzzy feature idea into measurable acceptance criteria and eval cases before the agent touches the repo.
Refactor toward typed interfaces and deterministic tests so agents cannot hide behavior in implicit conventions.
Run a review pass prioritizing security assumptions and data integrity instead of style debates.
Require regression and edge-case assertions on every agent-touched domain before merge.
Post-incident, tighten failure-handling and rollout-safety checks in the agent workflow.
How it compares
Use as a process playbook layered on top of generic code-review skills—not as a linter or single-repo automation skill.
Common Questions / FAQ
Who is ai-first-engineering for?
Solo and indie builders (and very small teams) who delegate implementation to agents and need planning, review, and architecture norms that scale beyond chat sessions.
When should I use ai-first-engineering?
During Build when defining specs and agent-friendly module boundaries; during Ship when reframing code review and regression tests for generated diffs; during Operate when tightening rollout safety and failure handling expectations.
Is ai-first-engineering safe to install?
It is documentation-only process guidance with no shell or network hooks; review the Security Audits panel on this Prism page before trusting any repo copy.
SKILL.md
READMESKILL.md - Ai First Engineering
# 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