Best fit
- 面向ECC的以证据为先的自动化清单与重叠审计工作流。当用户希望在修复任何内容之前了解哪些作业、钩子、连接器、MCP服务器或包装器是活跃的、损坏的、冗余的或缺失时使用。
affaan-m/ECC
面向ECC的以证据为先的自动化清单与重叠审计工作流。当用户希望在修复任何内容之前了解哪些作业、钩子、连接器、MCP服务器或包装器是活跃的、损坏的、冗余的或缺失时使用。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/automation-audit-ops"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: 当用户询问哪些自动化正在运行、哪些任务出现故障、哪里存在重叠,或者哪些工具和连接器当前正在实际发挥作用时,请使用此技能。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/automation-audit-ops"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
在相关时,将这些 ECC 原生技能引入工作流程:
用户询问"我有哪些自动化"、"什么在运行"、"什么出故障了"或"什么重叠了" 任务涉及 cron 任务、GitHub Actions、本地钩子、MCP 服务器、连接器、包装器或应用集成 用户想知道从其他代理系统移植了什么,以及哪些还需要在 ECC 内部重建 工作区积累了多种执行同一任务的方式,用户希望有一条规范的路径
除非用户明确要求修复,否则以只读方式开始 区分: 已配置 已验证身份 最近已验证 过时或损坏 完全缺失 不要仅仅因为某个技能或配置引用了某个工具,就声称该工具正在运行 在证据表存在之前,不要合并或删除重叠的表面
仓库钩子和本地钩子脚本 GitHub Actions 和计划工作流 MCP 配置和已启用的服务器 基于连接器或应用的集成 包装器脚本和特定仓库的自动化入口点
仓库钩子和本地钩子脚本 GitHub Actions 和计划工作流 MCP 配置和已启用的服务器 基于连接器或应用的集成 包装器脚本和特定仓库的自动化入口点
SkillSignal prompt templates
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 automation-audit-ops 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 automation-audit-ops source to [task]. Pay particular attention to these source sections: “技能栈”, “使用时机”, “防护栏”, “工作流程”, “1. 盘点真实表面”. 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 automation-audit-ops 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
The task matches the purpose documented in the SKILL.md.
The source section “技能栈” has been checked.
The source section “使用时机” has been checked.
The source section “防护栏” has been checked.
The source section “工作流程” 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
Evidence-first automation inventory and overlap audit workflow for ECC. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailECC用の証拠ベースの自動化インベントリとオーバーラップ監査ワークフロー。ユーザーがどのジョブ、フック、コネクタ、MCPサーバー、またはラッパーがライブか、壊れているか、冗長であるか、修正前に不足しているかを知りたい場合に使用します。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailUse when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
当用户询问哪些自动化正在运行、哪些任务出现故障、哪里存在重叠,或者哪些工具和连接器当前正在实际发挥作用时,请使用此技能。
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/automation-audit-ops". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Evidence-first automation inventory and overlap audit workflow for ECC. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything.
ECC用の証拠ベースの自動化インベントリとオーバーラップ監査ワークフロー。ユーザーがどのジョブ、フック、コネクタ、MCPサーバー、またはラッパーがライブか、壊れているか、冗長であるか、修正前に不足しているかを知りたい場合に使用します。
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
当用户询问哪些自动化正在运行、哪些任务出现故障、哪里存在重叠,或者哪些工具和连接器当前正在实际发挥作用时,请使用此技能。
这是一项以审计为先的操作技能。其任务是在重写任何内容之前,生成一份有证据支持的清单以及一套保留/合并/删除/下一步修复的建议集。
在相关时,将这些 ECC 原生技能引入工作流程:
workspace-surface-audit 用于连接器、MCP、钩子和应用清单knowledge-ops 当审计需要将实时仓库的真实情况与持久上下文进行核对时github-ops 当答案依赖于 CI、计划工作流、议题或 PR 自动化时ecc-tools-cost-audit 当真正的问题是兄弟应用仓库中的 webhook 扇出、队列任务或计费消耗时research-ops 当需要将本地清单与当前平台支持或公开文档进行比较时verification-loop 用于证明修复后的状态,而不是依赖假设的恢复在理论化之前,先读取当前的实时表面:
按表面分组:
对于每个发现的自动化,标记:
然后对问题类型进行分类:
为每个重要声明提供具体来源:
如果当前状态不明确,请直接说明,而不是假装审计已完成。
对于每个重叠或可疑的表面,返回一个决策:
其价值在于将杂乱的自动化整合到一条规范的 ECC 路径中,而不是保留每一条历史路径。
当前表面
- 自动化
- 来源
- 实时状态
- 证据
发现
- 活跃故障
- 重叠
- 过时状态
- 缺失能力
建议
- 保留
- 合并
- 删除
- 下次修复
下一步ECC行动
- 需加强的具体技能/钩子/工作流/应用通道