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BytesAgain β€Ί Skills β€Ί AI-First Engineering
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AI-First Engineering

by @djc00p

Engineering operating model for teams shipping with AI-assisted code generation. Process shifts, architecture requirements, code review and testing standards...

Versionv1.0.0
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πŸ“– About This Skill

name: ai-first-engineering description: "Engineering operating model for teams shipping with AI-assisted code generation. Process shifts, architecture requirements, code review and testing standards. Trigger phrases: ai-first engineering, ai-assisted teams, agent code generation, ai team process, shared responsibility review." metadata: {"clawdbot":{"emoji":"πŸ› οΈ","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}}

AI-First Engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Adapted from everything-claude-code by @affaan-m (MIT).

Quick Start

1. Invest in planning quality β€” ambiguous specs cause AI-generated code to fail; write clear acceptance criteria first 2. Raise eval coverage β€” AI code requires higher test standards; regression coverage mandatory for touched domains 3. Shift review focus β€” review for behavior, security, data integrity, failure handling; let automation handle style 4. Design agent-friendly architecture β€” explicit boundaries, stable contracts, typed interfaces, deterministic tests 5. Evaluate hiring signals β€” decomposition skill, measurable criteria definition, prompt quality, risk control discipline

Key Concepts

  • Planning > Speed: Clear specs + good evals trump fast typing. AI can implement fast; humans must specify clearly.
  • Automation is the baseline: Style, formatting, lint issues are solved by automation, not review.
  • Architecture matters more: Implicit conventions break AI systems; use explicit boundaries and typed interfaces.
  • Test coverage is non-negotiable: Generated code needs regression coverage for every touched domain.
  • Shared responsibility: AI generates; human reviews for risk (security, data integrity, rollout safety); human refines when needed.

    • Common Usage

    Code review in AI-first teams β€” focus on:

    Behavior regressions: Did the change break existing functionality?
Security assumptions: Input validation, permission checks, sensitive data handling
Data integrity: Constraints, rollback safety, concurrent access
Failure handling: Network calls, database errors, timeouts, degraded modes
Rollout safety: Feature flags, backward compatibility, canary deploy strategy

    Architecture for AI teams:

  • Explicit boundaries between modules (not implicit conventions)
  • Stable contracts (typed interfaces, documented behavior)
  • Deterministic tests (no flaky tests β€” AI can't debug intermittent failures)
  • Clear error paths (AI struggles with ambiguous error handling)

    • Testing standard raise:

  • Regression coverage for every touched domain (required, not optional)
  • Explicit edge-case assertions (AI may miss corner cases)
  • Integration checks for interface boundaries (behavior across module lines)

    • Hiring Signals for AI-First Engineers

    Strong signals:

  • Decomposes ambiguous work cleanly β†’ clear, testable units
  • Defines measurable acceptance criteria β†’ no scope creep, clear done condition
  • Produces high-signal prompts and evals β†’ AI generates better code from better specs
  • Enforces risk controls under delivery pressure β†’ doesn't skip security or testing for speed

    • Weak signals:

  • "Move fast and break things" mindset
  • Writing code without clear specs or acceptance criteria
  • Skipping regression tests to save time
  • Vague PR descriptions ("fixed bugs," "refactored stuff")

    • References

  • references/process-shifts.md β€” detailed planning, evals, review guidance
  • references/architecture-guide.md β€” designing systems for AI code generation
  • references/testing-standards.md β€” regression coverage, edge-case testing, integration checks

    • πŸ’‘ Examples

    1. Invest in planning quality β€” ambiguous specs cause AI-generated code to fail; write clear acceptance criteria first 2. Raise eval coverage β€” AI code requires higher test standards; regression coverage mandatory for touched domains 3. Shift review focus β€” review for behavior, security, data integrity, failure handling; let automation handle style 4. Design agent-friendly architecture β€” explicit boundaries, stable contracts, typed interfaces, deterministic tests 5. Evaluate hiring signals β€” decomposition skill, measurable criteria definition, prompt quality, risk control discipline