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Hermes 匯入工具

將本機 Hermes 操作員工作流程轉換為經過清理的 ECC 技能和發布套件成品。在準備將 Hermes 工作流程發布至公共 ECC 重用時使用,以避免洩露私人工作區狀態、憑證或僅限本機的路徑。

Hermes 匯入工具 是什麼?

Hermes Imports 是一種 AI 代理技能,旨在安全地將本機 Hermes 操作員工作流程轉換為可發布的 ECC(可重複使用的通用協作層)技能。它主要用於開發者和操作員希望將已穩定重複的 Hermes 工作流程標準化為公共、可重複使用的 ECC 技能的情況,同時確保工作流程中不包含私人工作區狀態、帳戶名稱、憑證或僅限本機的文件路徑。 此技能支援 Claude Code、Cursor 和 Codex 等平台。其工作原理是提供一套嚴格的匯入規則和清理檢查清單,包括將本機路徑轉換為 repo 相對路徑、用角色標籤(如 operator、default profile)取代真實帳戶名稱、按提供者名稱描述憑證需求,以及掃描並移除 API 金鑰、令牌、OAuth 檔案、私人電子郵件等敏感資訊。它還定義了一個清晰的轉換模式:從識別可重複的操作循環開始,剝離私人輸入和輸出,重寫路徑,轉換指令為「使用時機」和簡短流程,添加具體輸出要求,最後在提交 PR 前進行秘密和路徑掃描。 此技能的核心價值在於防止因工作流程發布而意外洩漏私人數據,同時促進團隊間共享穩定的操作模式。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/hermes-imports

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

Hermes 匯入工具 是做什麼的?

當將重複的 Hermes 工作流程轉換為可在 ECC 中安全發布的內容時,請使用此技能。

Hermes 是操作員 shell。ECC 是可重複使用的工作流程層。匯入應將穩定的模式從 Hermes 移入 ECC,而不移動私人狀態。

使用時機

  • Hermes 工作流程已重複足夠多次數而變為可重複使用。
  • 本機操作員提示應成為公共 ECC 技能。
  • 發布、內容、研究或工程工作流程需要經過清理的交接文件。
  • 工作流程提及本機路徑、憑證、個人資料集或必須在發布前移除的私人帳戶名稱。

匯入規則

  • 將本機路徑轉換為 repo 相對路徑或佔位符。
  • 用角色標籤(例如 operator、default profile 或 workspace owner)取代真實帳戶名稱。
  • 僅按提供者名稱描述憑證要求。
  • 保持範例簡潔且具操作性。
  • 不要發布原始工作區匯出檔、令牌、OAuth 檔案、健康資料、CRM 資料或財務資料。
  • 如果工作流程需要私人狀態才能理解,請將其保留在本機。

清理檢查清單

在提交匯入的工作流程之前,掃描以下內容:

  • 絕對路徑,例如 /Users/...
  • ~/.hermes 路徑,除非文件明確在解釋本機設定
  • API 金鑰、令牌、cookies、OAuth 檔案或 bearer 字串
  • 電話號碼、私人電子郵件地址和個人聯絡人圖譜
  • 尚未公開的客戶名稱、家族名稱或帳戶名稱
  • 收入、健康或 CRM 詳細資料
  • 包含來自私人系統的工具輸出的原始記錄檔

轉換模式

  1. 識別可重複的操作循環。
  2. 剝離私人輸入和輸出。
  3. 將本機路徑重寫為 repo 相對範例。
  4. 將一次性指令轉換為「使用時機」部分和簡短流程。
  5. 添加具體的輸出要求。
  6. 在開啟 PR 之前,執行秘密和本機路徑掃描。

範例:發布交接

本機 Hermes 提示:

讀取我的本機工作區檔案並完成發布文案。

ECC 安全版本:

使用 docs/releases/<version>/ 下的公開發布套件。
回傳一條 X 線程、一篇 LinkedIn 貼文、一份錄製檢查清單以及缺少的資產清單。

範例:安靜時段操作員作業

本機 Hermes 作業:

在夜間執行我的私人收件匣、財務和內容檢查。

ECC 安全版本:

描述排程器策略、安靜時段視窗、升級規則以及檢查的類別。請勿包含私人資料來源或憑證。

輸出合約

回傳:

  • 候選 ECC 技能名稱
  • 經過清理的工作流程摘要
  • 所需的公共輸入
  • 已移除的私人輸入
  • 剩餘風險
  • 應建立或更新的檔案

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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