devenkhatri/everything-claude-code · Archived

ai-first-engineering

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

First seen Apr 6, 2026

Installation

$ npx skills add devenkhatri/everything-claude-code --skill ai-first-engineering

Stronger alternatives

This repository is archived — consider an actively maintained alternative.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from devenkhatri/everything-claude-code · top by installs.

npx skills add devenkhatri/everything-claude-code

Browse all from devenkhatri/everything-claude-code

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

License LICENSE
Default branch main
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,336 B
  • docs SUMMARY.md 130 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

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