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Dag Best Practices

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xiaotianfotos
dag-best-practices

HomeRail DAG review and execution best practices. Use when designing, running, or reviewing HomeRail fan-out/fan-in DAGs, especially model-backed DAG audits, environment checks, and agent handoff evidence.

Overview

Publisherxiaotianfotos
Repositoryhomerail
Skill namedag-best-practices
Stars
995
Forks
224
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Dag Best Practices 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/xiaotianfotos/homerail/tree/main/assets/orchestrations/dag-best-practices
  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/xiaotianfotos/homerail.git /tmp/homerail
mkdir -p .claude/skills
cp -r /tmp/homerail/assets/orchestrations/dag-best-practices .claude/skills/dag-best-practices
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dag Best Practices 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 Dag Best Practices 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 Dag Best Practices 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.

HomeRail DAG 最佳实践

一句话原则:先统一事实,再分头检查,最后只按证据下结论。

DO

  • 先建立唯一事实源。用 seed 节点拿到代码、版本、输入参数和外部证据,再把同一份事实交给所有下游节点。
  • 让每个节点只回答一个清晰问题,例如源码是否干净、构建是否通过、运行时是否健康、数据是否保留。
  • 让节点真的检查事实。该跑命令就跑命令,该读文件就读文件,该查 API 就查 API,不要只复述提示词。
  • 让节点输出结构化 handoff。至少包含结论、证据、缺口、阻塞原因;失败时给出可行动的修复方向。
  • 让 fan-in 节点只做裁决。它可以汇总和判断,但不应该重新做所有检查,也不应该替上游补证据。
  • 把 hard gate 和 advisory 分开。必须通过的检查失败就阻塞;参考意见只能提示风险,不能替代证据。
  • 把运行环境差异当成默认存在。宿主机、容器、本地、远端、CI 看到的路径、网络、权限、工具链可能都不同。
  • 把会改变运行时的动作放在 DAG 外或明确隔离。部署、迁移、重启服务这类动作不要破坏正在执行 DAG 的基础设施。
  • 给长期状态留 baseline。涉及升级、迁移、用户数据、配置和历史记录时,先记录基准,再用后续运行对比。
  • 留下可复查的证据。每次运行都应该能回答:看了什么、跑了什么、结果是什么、为什么通过或阻塞。
  • 用小 smoke 验证流程,用真实任务验证能力。快速 smoke 证明线路能通,真实模型和真实命令才证明任务完成质量。
  • 使用执行环境真的能访问的地址、路径和凭证。给容器节点的输入,要从容器视角验证可用。

DO NOT

  • 不要让节点各自使用不同事实源。版本不一致时,后面的汇总没有意义。
  • 不要把模糊自然语言当成节点间协议。节点间传递要用结构化字段,不要靠读聊天猜含义。
  • 不要让一个节点承担太多职责。职责越混,失败原因越难定位。
  • 不要把没输出结果的节点算作成功。没有 handoff 就是缺证据。
  • 不要自动补 handoff 后继续判通过。自动补只能说明节点没有按契约完成。
  • 不要让 fan-in 节点靠感觉补全缺失信息。缺少证据就阻塞,而不是猜测通过。
  • 不要把参考意见当成硬结论。LLM review、人工建议、摘要和 smoke 都不能替代 hard gate 的实际证据。
  • 不要把快速跑通说成完整完成。smoke 是线路检查,不是质量证明。
  • 不要默认所有环境都一样。宿主机能访问,不代表容器能访问;本机能跑,不代表 CI 能跑。
  • 不要只保存最终结论,不保存过程证据。没有过程,结论无法复查。
  • 不要隐藏不确定性。没查到、没权限、环境不可达、证据不足,都应该明确写进结果。
  • 不要把 token、本机路径、私有地址、临时目录写死在可复用模板里。需要这些值时,通过运行参数或环境配置传入。

快速检查

  • 有没有唯一事实源?
  • 每个节点的问题是否足够单一?
  • 每个节点是否会产生结构化 handoff?
  • fan-in 是否只基于 handoff 和 evidence 下结论?
  • hard gate 和 advisory 是否分清?
  • smoke 和真实验证是否分清?
  • 运行环境的网络、路径、权限是否从执行节点视角验证过?
  • 失败时是否能直接看出原因和下一步?

Frequently asked questions

What does the Dag Best Practices AI skill do?

HomeRail DAG review and execution best practices. Use when designing, running, or reviewing HomeRail fan-out/fan-in DAGs, especially model-backed DAG audits, environment checks, and agent handoff evidence.

Why use Dag Best Practices on TypingMind?

Because you install it once and use it with any model. Dag Best Practices 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 Dag Best Practices in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xiaotianfotos/homerail/tree/main/assets/orchestrations/dag-best-practices. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dag Best Practices?

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 Dag Best Practices?

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

Is the Dag Best Practices AI skill free?

Yes. It is published on GitHub by xiaotianfotos 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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