Skill: djc00p/ai-first-engineering (version 1.0.0)

--- 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":"🛠️","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** — ambiguous specs cause AI-generated code to fail; write clear acceptance criteria first 2. **Raise eval coverage** — AI code requires higher test standards; regression coverage mandatory for touched domains 3. **Shift review focus** — review for behavior, security, data integrity, failure handling; let automation handle style 4. **Design agent-friendly architecture** — explicit boundaries, stable contracts, typed interfaces, deterministic tests 5. **Evaluate hiring signals** — 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 — focus on:** ```text 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 — 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 → clear, testable units - Defines measurable acceptance criteria → no scope creep, clear done condition - Produces high-signal prompts and evals → AI generates better code from better specs - Enforces risk controls under delivery pressure → 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` — detailed planning, evals, review guidance - `references/architecture-guide.md` — designing systems for AI code generation - `references/testing-standards.md` — regression coverage, edge-case testing, integration checks


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