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affaan-m/ECC/docs/zh-CN/skills/agentic-engineering/SKILL.md

agentic-engineering

Use it for engineering tasks; the detail page covers purpose, installation, and practical steps.

Source repository stars
234,327
Declared platforms
0
Static risk flags
0
Last source update
2026-07-27
Source checked
2026-07-28
DecisionCompatibilityInstallWorkflowPermissionsEvidenceSource

Decision brief

What it does—and where it fits

在 AI 智能体执行大部分实施工作、而人类负责质量与风险控制的工程工作流中使用此技能。

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/agentic-engineering"
    Safe inspection promptEditorial

    Inspect the Agent Skill "agentic-engineering" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/docs/zh-CN/skills/agentic-engineering/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      操作原则

      1. 在执行前定义完成标准。 2. 将工作分解为智能体可处理的单元。 3. 根据任务复杂度路由模型层级。 4. 使用评估和回归检查进行度量。

      在执行前定义完成标准。将工作分解为智能体可处理的单元。根据任务复杂度路由模型层级。
    2. 02

      评估优先循环

      1. 定义能力评估和回归评估。 2. 运行基线并捕获失败特征。 3. 执行实施。 4. 重新运行评估并比较差异。

      定义能力评估和回归评估。运行基线并捕获失败特征。执行实施。
    3. 03

      任务分解

      每个单元应可独立验证 每个单元应有一个主要风险 每个单元应暴露一个清晰的完成条件

      每个单元应可独立验证每个单元应有一个主要风险每个单元应暴露一个清晰的完成条件

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score51/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars234,327SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guidecatalog recordEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    affaan-m/ECC
    Skill path
    docs/zh-CN/skills/agentic-engineering/SKILL.md
    Commit
    4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38
    License
    MIT
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    智能体工程

    在 AI 智能体执行大部分实施工作、而人类负责质量与风险控制的工程工作流中使用此技能。

    操作原则

    1. 在执行前定义完成标准。
    2. 将工作分解为智能体可处理的单元。
    3. 根据任务复杂度路由模型层级。
    4. 使用评估和回归检查进行度量。

    评估优先循环

    1. 定义能力评估和回归评估。
    2. 运行基线并捕获失败特征。
    3. 执行实施。
    4. 重新运行评估并比较差异。

    任务分解

    应用 15 分钟单元规则：

    • 每个单元应可独立验证
    • 每个单元应有一个主要风险
    • 每个单元应暴露一个清晰的完成条件

    模型路由

    • Haiku：分类、样板转换、狭窄编辑
    • Sonnet：实施和重构
    • Opus：架构、根因分析、多文件不变量

    会话策略

    • 对于紧密耦合的单元，继续使用同一会话。
    • 在主要阶段转换后，启动新的会话。
    • 在里程碑完成后进行压缩，而不是在主动调试期间。

    AI 生成代码的审查重点

    优先审查：

    • 不变量和边界情况
    • 错误边界
    • 安全性和身份验证假设
    • 隐藏的耦合和上线风险

    当自动化格式化/代码检查工具已强制执行代码风格时，不要在仅涉及风格分歧的审查上浪费周期。

    成本纪律

    按任务跟踪：

    • 模型
    • 令牌估算
    • 重试次数
    • 实际用时
    • 成功/失败

    仅当较低层级的模型失败且存在清晰的推理差距时，才升级模型层级。

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