360 Secure SkillHub
360 安全技能中心

Agentic Engineering

该 Skill 以纯指导文档形式给出 AI 辅助工程的工作流模式:先定义完成标准与基线评测,再将工作拆成 15 分钟可独立验证的单元,并按复杂度选择模型层级,最后跑评测对比增量。配套三个参考文档分别讲评测循环、分解规则与评审要点,不含脚本或自动执行实现。

djc00p
djc00p
热度 45.71
最近更新 2026-05-11 17:40:32
分类:开发依赖:纯提示词应用场景:代码生成 / 代码审查 / 工作流 / 任务管理 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add agentic-engineering-ecc
Metadata
name
agentic-engineering-ecc
description
Workflow pattern for AI-assisted engineering using eval-first execution, task decomposition, and cost-aware model routing. Trigger phrases: agentic engineering, eval-first workflow, decompose tasks, model routing, cost discipline, task completion criteria.
metadata
{"clawdbot":{"emoji":"🤖","requires":{"bins":[],"env":[]},"os":["linux","darwin","win32"]}}

Agentic Engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Adapted from everything-claude-code by @affaan-m (MIT).

Quick Start

  1. Define completion criteria — write acceptance criteria and success metrics before execution
  2. Create baseline evals — write capability and regression tests that capture current state
  3. Decompose work — break into 15-minute units, each independently verifiable with a single dominant risk
  4. Route models by complexity — Haiku for narrow tasks, Sonnet for implementation, Opus for architecture
  5. Run post-implementation evals — measure deltas, confirm no regressions

Key Concepts

  • Eval-first execution: Run tests before coding; measure against known baseline; catch regressions early
  • 15-minute unit rule: Each task should have one clear risk, one verifiable outcome, be completable in ~15 minutes
  • Model tier matching: Complexity determines model — don't overpay for simple tasks, don't underpay for hard ones
  • Review focus: Prioritize invariants, error boundaries, security, coupling — not style (automation handles that)
  • Session strategy: Continue for coupled units; reset after major phase transitions; compact at milestones

Common Usage

Setting up eval-first for a feature:

1. Define acceptance criteria (user-facing behavior)
2. Write capability eval (can the system do the required task?)
3. Write regression eval (does existing functionality still work?)
4. Execute feature implementation with model routing
5. Re-run evals, compare deltas
6. Document any new risks discovered during review

Model routing example:

  • Haiku: boilerplate generation, narrow edits, classification
  • Sonnet: feature implementation, small refactors, test writing
  • Opus: multi-file changes, root-cause analysis, architecture decisions

Cost discipline: Track per task: model tier, token estimate, retries, wall-clock time, success/failure. Escalate model tier only when lower tier fails with clear reasoning gap, not on uncertainty.

References

  • references/eval-patterns.md — detailed eval-first loop patterns
  • references/decomposition-rules.md — 15-minute unit principle and task breakdown examples
  • references/review-checklist.md — what to focus on in code review (invariants, boundaries, security, coupling)