Best fit
- 作为代理工程师，采用评估优先执行、分解和成本感知模型路由进行操作。
affaan-m/ECC
agentic-engineering
作为代理工程师，采用评估优先执行、分解和成本感知模型路由进行操作。
51Collecting
See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/agentic-engineering"Automated source guide
Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
Turn agentic-engineering's source instructions into a guide you can follow
According to the pinned SKILL.md from affaan-m/ECC: 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。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/agentic-engineering"Bring this context
- A concrete task that matches the documented purpose of agentic-engineering.
- The files, examples, or context the task depends on.
- Your constraints, target environment, and definition of done.
Expected outputs
- A result that follows the pinned agentic-engineering instructions.
- A concise record of assumptions, inputs used, and unresolved questions.
- A final check against the source workflow and relevant permission signals.
Key source sections
Read agentic-engineering through these 5 source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
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。
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。
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
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
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。
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。
SkillSignal prompt templates
Provide the task, context, and acceptance criteria
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use agentic-engineering to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned agentic-engineering source to [task]. Pay particular attention to these source sections: “操作原则”, “评估优先循环”, “任务分解”, “模型路由”, “会话策略”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current agentic-engineering result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
Verify each item before delivery
The task matches the purpose documented in the SKILL.md.
The source section “操作原则” has been checked.
The source section “评估优先循环” has been checked.
The source section “任务分解” has been checked.
The source section “模型路由” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
When another Skill is the better fit
agentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailagentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailagentic-engineering
評価ファースト実行、分解、コスト対応モデルルーティングを使用してエージェニックエンジニアとして動作します。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
What does agentic-engineering do?
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。
How do I start using agentic-engineering?
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/agentic-engineering". Inspect the command and pinned source before running it.
Which Agent platforms does it declare?
No dedicated Agent platform is declared in the pinned source record.
Repository stars
234,327
Repository forks
35,711
Quality
51/100
Source repository last pushed
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Documentation11/30
Specificity8/25
Maintenance20/20
Trust signals12/25
Compare before choosing
Related Agent Skills and source variants
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
agentic-engineering by affaan-m
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls.
agentic-engineering by affaan-m
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
agentic-engineering by affaan-m
評価ファースト実行、分解、コスト対応モデルルーティングを使用してエージェニックエンジニアとして動作します。
ab-testing by coreyhaines31
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program
design-intelligence by event4u-app
Grounded design brief from the adopted corpus — style, WCAG-checked color tokens, typography, layout pattern, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart decision.
View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 1 min
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
- 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。
- 使用评估和回归检查进行度量。
评估优先循环
- 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。
- 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。
- Source repo
- affaan-m/ECC
- Skill path
- docs/zh-CN/skills/agentic-engineering/SKILL.md
- Commit SHA
- 4e973d3eaf92
- Repository license
- MIT
- Data collected