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Workflow

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zhinkgit
workflow

embeddedskills 的薄编排层，用于在当前 workspace 中发现工程、选择 build/flash/debug/observe 后端、串联 .embeddedskills/state.json，并聚合底层 skill 的结果。 当用户明确输入以下命令之一："一键构建烧录"、"自动诊断"、"串起 build -> flash -> debug -> observe" 或显式调用 /workflow 时触发。

Overview

Publisherzhinkgit
Repositoryembeddedskills
Skill nameworkflow
Stars
739
Forks
94
Bundled files
4
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhinkgit on GitHub. Read the source before you install it.

Installation

Install the Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zhinkgit/embeddedskills/tree/main/workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhinkgit/embeddedskills.git /tmp/embeddedskills
mkdir -p .claude/skills
cp -r /tmp/embeddedskills/workflow .claude/skills/workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Workflow 编排层

本 skill 不重复实现底层逻辑，只做发现、选择、串联和聚合。

支持 Keil / GCC / EIDE 三种构建后端，以及 jlink / openocd / probe-rs 三种 flash/debug/observe 后端。

observe 阶段当前会给出 jlink:rtt、jlink:swo、openocd:semihosting、openocd:itm、probe-rs:rtt 这几类候选观测后端。

命令

bash
python <skill-dir>/scripts/workflow_plan.py --json
python <skill-dir>/scripts/workflow_run.py plan --json
python <skill-dir>/scripts/workflow_run.py build --json
python <skill-dir>/scripts/workflow_run.py build-flash --json
python <skill-dir>/scripts/workflow_run.py build-debug --json
python <skill-dir>/scripts/workflow_run.py observe --json
python <skill-dir>/scripts/workflow_run.py diagnose --json

配置说明

workflow 不再维护独立的工程配置结构，所有工程参数统一从 .embeddedskills/config.json 读取。

配置结构

.embeddedskills/config.json 中的 workflow 段仅包含首选后端配置：

json
{
  "workflow": {
    "preferred_build": "auto",
    "preferred_flash": "auto",
    "preferred_debug": "auto",
    "preferred_observe": "auto"
  }
}

workflow 通过读取 .embeddedskills/config.json 中其他 skill 的配置段来获取工程参数（如 keil.project、eide.project、eide.config、jlink.device、probe-rs.chip 等）。

参数解析顺序

按以下决策树依次判断，命中即停止：

  1. CLI 参数（优先级最高）

    • 条件：用户在命令行传入 --build-backend、--flash-backend 等参数
    • 示例：workflow_run.py build-flash --build-backend=keil --flash-backend=jlink
    • --build-backend 可选值：auto / keil / gcc / eide
    • 行为：直接使用该参数指定的后端，跳过后续步骤

  2. 配置文件（次优先）

    • 条件：CLI 未指定，且 .embeddedskills/config.json 的 workflow 段中对应 preferred_* 字段不为 "auto"
    • 示例："preferred_build": "keil" → 使用 keil 作为构建后端
    • 行为：读取配置值并使用，跳过自动发现

  3. 自动发现（兜底）

    • 条件：CLI 未指定，且配置中 preferred_* 为 "auto" 或字段缺失
    • 示例："preferred_flash": "auto" → 扫描 workspace 自动推断可用 flash 后端
    • 行为：枚举候选后端列表；若唯一则直接使用，若多个则返回列表请用户确认

成功执行后，实际使用的后端会自动写回 .embeddedskills/config.json 的 workflow 段。

规则

  • 发现多个工程或多个候选后端时，只返回候选列表，不自动猜测
  • 构建、烧录、调试、观测之间优先通过 .embeddedskills/state.json 串联
  • observe 只生成推荐命令，不在 workflow 内直接长时间占用观测通道
  • 失败时优先返回哪个阶段失败，以及底层脚本的结构化错误
  • workflow 与其他 Skill 的协同只通过 .embeddedskills/config.json、.embeddedskills/state.json 和子进程调用底层 Skill

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Workflow AI skill do?

embeddedskills 的薄编排层，用于在当前 workspace 中发现工程、选择 build/flash/debug/observe 后端、串联 .embeddedskills/state.json，并聚合底层 skill 的结果。 当用户明确输入以下命令之一："一键构建烧录"、"自动诊断"、"串起 build -> flash -> debug -> observe" 或显式调用 /workflow 时触发。

Why use Workflow on TypingMind?

Because you install it once and use it with any model. Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhinkgit/embeddedskills/tree/main/workflow. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Workflow?

As many as you like. As long as a model supports skills, you can use Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Workflow AI skill free?

Yes. It is published on GitHub by zhinkgit under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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