agentleFS
Sign inStart free
SamuelNDCESamuelNDCE/claude-super-skill-library/skills/agent-ai-engineering/ai-first-engineering/SKILL.md

ai-first-engineering

Skill1 starsChanged 3 months agoMITView on GitHub

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

We split this skill into the rules it gives an agent

3 rules. 3 are common, 0 are distinctive, and 0 are very specific to this project.

  • 3

    Common

    Most of the 10 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

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

    line 9
  2. Common:

    Prefer architectures that are agent-friendly:

    line 19
  3. Common:

    Avoid implicit behavior spread across hidden conventions.

    line 25

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
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

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free