先看看示例
作者提供的示例,实际效果取决于你的输入和使用工具。
输入
为一个功能开发任务拆分可验证单元,并设计回归评估清单。
输出
预期交付:任务拆分单元、评估对比结果与模型路由及审查建议。(编辑说明,非实际运行结果)
方法说明
--- name: agentic-engineering description: Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end. metadata: origin: ECC --- # Agentic Engineering Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls. ## Operating Principles 1. Define completion criteria before execution. 2. Decompose work into agent-sized units. 3. Route model tiers by task complexity. 4. Measure with evals and regression checks. ## Eval-First Loop 1. Define capability eval and regression eval. 2. Run baseline and capture failure signatures. 3. Execute implementation. 4. 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.