agentic-engineering

SKILL.md

Agentic Engineering

Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.

Operating Principles

Define completion criteria before execution.

Decompose work into agent-sized units.

Route model tiers by task complexity.

Measure with evals and regression checks.

Eval-First Loop

Define capability eval and regression eval.

Run baseline and capture failure signatures.

Execute implementation.

Re-run evals and compare deltas.

Task Decomposition
Apply the 15-minute unit rule:

each unit should be independently verifiable

each unit should have a single dominant risk

each unit should expose a clear done condition

Model Routing

Haiku: classification, boilerplate transforms, narrow edits

Sonnet: implementation and refactors

Opus: architecture, root-cause analysis, multi-file invariants

Session Strategy

Continue session for closely-coupled units.

Start fresh session after major phase transitions.

Compact after milestone completion, not during active debugging.

Review Focus for AI-Generated Code

Prioritize:

invariants and edge cases

error boundaries

security and auth assumptions

hidden coupling and rollout risk

Do not waste review cycles on style-only disagreements when automated format/lint already enforce style.

Cost Discipline

Track per task:

model

token estimate

retries

wall-clock time

success/failure

Escalate model tier only when lower tier fails with a clear reasoning gap.