Ssh Dev Suite/Long Task

Organization

Ascend

ssh-dev-suite/long-task

长耗时任务管理，支持checkpoint记忆、agent休息与恢复

Overview

Publisher	Ascend
Repository	`agent-skills`
Skill name	`ssh-dev-suite/long-task`
Stars	54
Forks	4
Bundled files	1
License	MulanPSL-2.0
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Ascend on GitHub. Read the source before you install it.

Installation

Install the Ssh Dev Suite/Long Task AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Ascend/agent-skills/tree/master/skills/drivingsdk-ascend-model-migration/ssh-connection/long-task
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Ascend/agent-skills/tree/master/skills/drivingsdk-ascend-model-migration/ssh-connection/long-task

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/Ascend/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/drivingsdk-ascend-model-migration/ssh-connection/long-task .claude/skills/ascend-ssh-dev-suite-long-task

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ssh Dev Suite/Long Task 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 Ssh Dev Suite/Long Task 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 Ssh Dev Suite/Long Task 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.

ssh-long-task

概述

管理预计耗时>2分钟的远程任务，通过checkpoint机制支持agent休息和跨会话恢复。

适用场景

模型训练（>10分钟）
大规模数据处理
编译构建任务
数据库迁移

流程步骤

1. 启动任务并写入checkpoint

使用connect/scripts/ssh-job.sh start启动任务，获取job_id后立即写入checkpoint：

bash
bash connect/scripts/ssh-job.sh start <profile> "<command>"
# 输出: 任务已启动: job-20260306-143022-1234

bash long-task/scripts/checkpoint.sh write <profile> <job_id> \
  --task "模型训练 epoch 50" \
  --duration "~2h" \
  --next "检查训练完成,下载模型权重,运行验证" \
  --context "lr=0.001,batch=32"

产出: checkpoint文件 ~/.ssh-jobs/<job_id>/checkpoint.md

2. 告知用户并休息

Agent输出：


✓ 任务已启动: job-20260306-143022-1234
✓ Checkpoint已保存，预计耗时 ~2h

你可以：
1. 稍后输入"检查任务 <job_id>"查看状态
2. 关闭会话，任务继续运行
3. 新会话中我会自动读取checkpoint恢复上下文

3. 恢复时读取checkpoint

新会话或用户请求检查时：

bash
bash long-task/scripts/checkpoint.sh read <profile> <job_id>

产出: 解析checkpoint内容（task, started, duration, next_steps, context）

4. 智能状态检查

根据预计耗时决定检查策略：

bash
bash connect/scripts/ssh-job.sh status <profile> <job_id>

<5分钟: 每30秒检查一次
5-30分钟: 每2分钟检查一次
>30分钟: 每10分钟检查一次，或用户手动触发

5. 输出检查（Token优化）

先查大小:

bash
bash connect/scripts/ssh-job.sh status <profile> <job_id>
# 输出: stdout: 245KB, stderr: 1.2KB

按大小决策:

<10KB: 读取全部 bash connect/scripts/ssh-job.sh output <profile> <job_id>
10KB-100KB: 只读尾部 bash connect/scripts/ssh-job.sh output <profile> <job_id> --tail 50
>100KB: 先搜索错误 bash connect/scripts/ssh-job.sh output <profile> <job_id> --grep 'error|fail|exception'

6. 执行next_steps

任务完成后，按checkpoint中的next_steps继续：逐步执行，每步完成后更新checkpoint或删除。

Checkpoint格式

存储在 ~/.ssh-jobs/<job_id>/checkpoint.md:

yaml
job_id: job-20260306-143022-1234
profile: dev-server
task: "模型训练 epoch 50"
started: 2026-03-06T14:30:22
expected_duration: ~2h
next_steps:
  - 检查训练完成状态
  - 下载模型权重到本地
  - 运行验证脚本
context: "learning_rate=0.001, batch_size=32"

Token约束

输出检查：先查大小，按大小分级读取（全部/尾部/grep）
状态轮询：根据预计耗时调整频率，避免频繁检查
Checkpoint内容：精简关键信息，不存储完整日志

Bundled files

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

scripts/checkpoint.sh2 KB

Frequently asked questions

What does the Ssh Dev Suite/Long Task AI skill do?

长耗时任务管理，支持checkpoint记忆、agent休息与恢复

Why use Ssh Dev Suite/Long Task on TypingMind?

Because you install it once and use it with any model. Ssh Dev Suite/Long Task 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 Ssh Dev Suite/Long Task in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ascend/agent-skills/tree/master/skills/drivingsdk-ascend-model-migration/ssh-connection/long-task. 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 Ssh Dev Suite/Long Task?

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 Ssh Dev Suite/Long Task?

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

Is the Ssh Dev Suite/Long Task AI skill free?

Yes. It is published on GitHub by Ascend under the MulanPSL-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ssh-long-task

概述

适用场景

流程步骤

1. 启动任务并写入checkpoint

2. 告知用户并休息

3. 恢复时读取checkpoint

4. 智能状态检查

5. 输出检查（Token优化）

6. 执行next_steps

Checkpoint格式

Token约束

Bundled files

Frequently asked questions

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