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Hermes 导入工具

将本地 Hermes 操作员工作流转换为经过清理的 ECC 技能和发布包工件。在准备将 Hermes 工作流公开用于 ECC 重用而不泄露私有工作空间状态、凭据或仅限本地的路径时使用。

Hermes 导入工具 是什么?

Hermes Imports 是一个将本地 Hermes 工作流转换为适合在 ECC(可重用工作流层)中公开发布的技能的实用工具。它专注于从反复运行的 Hermes 工作流中提取稳定模式,并在发布前进行清理,以防止泄露私有数据。该工具支持本地到公开的转换,通过自动化的清理流程,确保所有绝对路径、凭据、个人数据集和私有账号名称都被替换为占位符或角色标签。用户可以通过遵循预定义的导入规则和清理清单,将本地的操作员提示或作业转换为 ECC 技能,并附带详细的输出合约和风险评估。最终输出包括候选技能名称、清理后的工作流摘要、所需公共输入、移除的私有输入以及剩余风险列表。

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

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文档

Hermes 导入工具 是做什么的?

当需要将一个反复运行的 Hermes 工作流安全地发布到 ECC 时使用此技能。

Hermes 是操作员外壳。ECC 是可重用工作流层。导入应将稳定模式从 Hermes 迁移到 ECC,而不移动私有状态。

何时使用

  • 某个 Hermes 工作流已经重复运行足够多次,可以重用。
  • 本地的操作员提示应成为一个公开的 ECC 技能。
  • 启动、内容、研究或工程工作流需要经过清理的交接文档。
  • 工作流中提及了本地路径、凭据、个人数据集或私有账号名称,必须在发布前移除。

导入规则

  • 将本地路径转换为仓库相对路径或占位符。
  • 将真实的账号名称替换为角色标签,例如 operator、default profile 或 workspace owner。
  • 凭据要求仅按提供商名称描述。
  • 保持示例简洁且可操作。
  • 不要发布原始的工作空间导出、令牌、OAuth 文件、健康数据、CRM 数据或财务数据。
  • 如果工作流需要私有状态才能理解,请将其保留在本地。

清理清单

在提交导入的工作流之前,请扫描以下内容:

  • 绝对路径,例如 /Users/...
  • ~/.hermes 路径,除非文档明确解释本地设置
  • API 密钥、令牌、cookies、OAuth 文件或 bearer 字符串
  • 电话号码、私人电子邮件地址和个人联系人图谱
  • 客户端名称、家庭成员名称或尚未公开的账号名称
  • 收入、健康或 CRM 详情
  • 包含私有系统工具输出的原始日志

转换模式

  1. 识别可重复的操作员循环。
  2. 移除私有输入和输出。
  3. 将本地路径重写为仓库相对示例。
  4. 将一次性指令转换为 When To Use 部分和一个简短流程。
  5. 添加具体的输出要求。
  6. 在打开 PR 之前运行密钥和本地路径扫描。

示例:启动交接

本地 Hermes 提示:

Read my local workspace files and finalize launch copy.

ECC 安全版本:

Use the public release pack under docs/releases/<version>/
Return one X thread, one LinkedIn post, one recording checklist, and the missing assets list.

示例:静默时段操作员任务

本地 Hermes 任务:

Run my private inbox, finance, and content checks overnight.

ECC 安全版本:

Describe the scheduler policy, the quiet-hours window, the escalation rules, and the categories of checks. Do not include private data sources or credentials.

输出合约

返回:

  • 候选 ECC 技能名称
  • 清理后的工作流摘要
  • 必需的公共输入
  • 已移除的私有输入
  • 剩余风险
  • 应创建或更新的文件

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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