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Agentic Engineering

作者 Deonte Cooper · GitHub ↗ · v1.0.0 · MIT-0
linuxdarwinwin32 ✓ 安全检测通过
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在 OpenClaw 中安装
/install agentic-engineering-ecc
功能描述
Workflow pattern for AI-assisted engineering using eval-first execution, task decomposition, and cost-aware model routing. Trigger phrases: agentic engineeri...
使用说明 (SKILL.md)

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)
安全使用建议
This skill is a set of best-practice instructions (eval-first, 15-minute units, model-tier routing) and contains no code, installs, or credential requests—so it is internally coherent. Before enabling: confirm you understand the platform's model access and billing (the skill recommends routing to model tiers named Haiku/Sonnet/Opus but does not provision them), avoid feeding sensitive secrets into prompts when testing, and consider whether you want the agent to invoke skills autonomously (the platform default) in your environment. If you need higher assurance, ask the author for provenance or run the guidance in a sandbox before applying it to real projects.
功能分析
Type: OpenClaw Skill Name: agentic-engineering-ecc Version: 1.0.0 The skill bundle provides a structured workflow for AI-assisted engineering, focusing on evaluation-driven development, task decomposition, and cost-aware model routing. The instructions in SKILL.md and the reference files (decomposition-rules.md, eval-patterns.md, review-checklist.md) are purely methodological and do not contain any malicious commands, data exfiltration logic, or prompt injection attacks designed to subvert the agent's behavior.
能力评估
Purpose & Capability
Name/description (agentic engineering, eval-first, decomposition, model routing) matches the content of SKILL.md and reference files; nothing in the package asks for unrelated capabilities (no cloud creds, no unusual binaries).
Instruction Scope
SKILL.md and references limit themselves to process guidance (write evals, decompose tasks, route by model tier, review checklist). They do not instruct the agent to read arbitrary files, export secrets, or call external endpoints.
Install Mechanism
No install spec and no code files—this is instruction-only, so nothing is downloaded or written to disk during installation.
Credentials
The skill declares no environment variables, credentials, or config paths; the guidance about model tiers is conceptual and does not require additional secrets from the user.
Persistence & Privilege
always:false (default) and no install actions that modify agent state are present. The skill is user-invocable and can be invoked autonomously by the agent (platform default), which is expected for skills of this type.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agentic-engineering-ecc
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agentic-engineering-ecc 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release. Eval-first agentic execution patterns with 15-min task units and model routing tiers. Adapted from everything-claude-code by @affaan-m (MIT)
元数据
Slug agentic-engineering-ecc
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Agentic Engineering 是什么?

Workflow pattern for AI-assisted engineering using eval-first execution, task decomposition, and cost-aware model routing. Trigger phrases: agentic engineeri... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 137 次。

如何安装 Agentic Engineering?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agentic-engineering-ecc」即可一键安装,无需额外配置。

Agentic Engineering 是免费的吗?

是的,Agentic Engineering 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Agentic Engineering 支持哪些平台?

Agentic Engineering 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(linux, darwin, win32)。

谁开发了 Agentic Engineering?

由 Deonte Cooper(@djc00p)开发并维护,当前版本 v1.0.0。

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