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Bazhuayu Skill

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infometa
bazhuayu-skill

八爪鱼云采集 MCP 技能 - 搜索模板、启动与管理任务、查询进度、导出结构化数据

Overview

Publisherinfometa
Repositoryworkbuddyskills
Skill namebazhuayu-skill
Stars
376
Forks
124
Bundled files
Instructions only
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 infometa on GitHub. Read the source before you install it.

Installation

Install the Bazhuayu Skill 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/infometa/workbuddyskills/tree/main/connectors/bazhuayu/skills
  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/infometa/workbuddyskills.git /tmp/workbuddyskills
mkdir -p .claude/skills
cp -r /tmp/workbuddyskills/connectors/bazhuayu/skills .claude/skills/bazhuayu-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bazhuayu Skill 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 Bazhuayu Skill 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 Bazhuayu Skill 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.

八爪鱼 Connector Skill

本 Skill 指导 AI 在 WorkBuddy 中正确使用「八爪鱼」MCP 连接器。

鉴权方式为 OAuth(浏览器授权)。除非 OAuth 失败且客户端明确要求其它方式,否则不要向用户索要 API Key。

首发工具范围

本连接器仅暴露以下 6 个工具:

工具用途
search_templates发现或精确解析云采集模板
execute_task创建并启动云采集(单次最多等待约 45 秒)
get_task_status查询标准化状态 / 已采集行数 / 批次号
export_data读取某一采集批次的一页 JSON 数据
search_tasks在当前授权账号下查找已有任务
start_or_stop_task启动或停止已有任务

不要编造其它工具名(无优惠券、电商专用、平台内容搜索等工具)。

推荐工作流

  1. 发现模板:用 search_templates 的语义 query(写清站点 + 数据类型 + 采集意图)。
  2. 补全 Schema(需要时):用精确 id 或 slug 再调 search_templates,拿到完整 inputSchema / sourceTree。
  3. 启动任务:execute_task 传入 templateName + parameters(JSON 对象字符串)。强烈建议带唯一 taskName。
  4. 若仍在运行:用 taskId 调 get_task_status 轮询至 completed 或 stopped(不要无限卡在一次调用上)。
  5. 读数据:export_data 传入 taskId + lotNo(来自 execute/status),用 page / pageSize 分页。
  6. 管理已有任务:先 search_tasks,再 start_or_stop_task 或 export_data。

鉴权说明

  • 用户连接本 Connector 时,由 WorkBuddy 自动完成 OAuth。
  • 所有工具都需要有效用户会话;未登录会返回 401 及 OAuth 发现信息。
  • 若长时间闲置后出现 unauthorized / invalid_token,引导用户重新连接本连接器(refresh_token 可能已过期)。
  • 禁止在对话、日志示例中输出 access token。

工具说明

search_templates — 搜索模板

用户要抓取 / 采集 / 抽取网页数据、需要云模板时使用。

每次调用只能选一种选择器:

参数类型必填说明
querystring三选一语义搜索:完整描述站点、数据与采集意图
idnumber三选一按模板 ID 精确查询
slugstring三选一按模板 slug / 别名精确查询
limitnumber否仅可与 query 同用;1–20,默认 10

规则:

  • 优先选 score 高、且支持云端执行(executionMode)的模板。
  • 语义搜索后,若需要完整 inputSchema 与 sourceTree 再执行任务,请用 id 或 slug 精确查询。
  • 不要传已废弃参数 keyword、page。

示例:

  • 语义:query = 「亚马逊美国站无线耳机搜索结果 商品标题 价格」
  • 精确:id = 12345

execute_task — 启动云采集

创建任务并启动云端运行,最多等待约 45 秒返回进度快照。

参数类型必填说明
templateNamestring是来自 search_templates 的模板名
parametersstring(JSON 对象)否key 使用 inputSchema[].field;默认 {}
taskNamestring否友好且唯一的任务名;强烈建议填写,便于 search_tasks 找回

参数类型约定:

  • Input / Dropdown → 字符串
  • MultiInput / CheckboxList → 字符串数组(即使只有一个值)
  • 依赖 source 的字段 → 传精确查询 sourceTree 中选项的 key;若仍缺依赖,工具返回 input_required 与下一层 sourceOptions,补全后再次调用(参数未就绪时不会创建任务)

结果要点: taskId、lotNo、collectedRows、status(running / completed / stopped)。

规则:

  • collectedRows 默认可能为 0;需结合 status 区分「仍在跑暂无数据」与「终态无数据」。
  • 若担心客户端超时,务必保留 taskId / taskName,后续用 get_task_status / search_tasks 续查。
  • 在 export_data(或大结果 curl 导出)成功前,不要声称数据已全部导出。

get_task_status — 查询任务状态

参数类型必填说明
taskIdstring是来自 execute_task 或 search_tasks

返回: status(running / completed / stopped)、collectedRows、可用时的 lotNo。

规则:

  • running 时退避轮询,终态停止。
  • 同一任务优先用本工具续查,不要重复 execute_task。

export_data — 导出一页数据

参数类型必填说明
taskIdstring是八爪鱼任务 ID
lotNostring/number是精确批次号(来自 execute/status)
pagenumber否默认 1
pageSizenumber否默认 20,最大 100

返回: 当前页 Body(data、分页信息等);数据量大时可能含 directAccess(签名 URL / curlTemplate)——大结果优先用 curl,避免占满模型上下文。


search_tasks — 搜索已有任务

参数类型必填说明
pagenumber否页码
sizenumber否每页条数,默认 10,最大 100
keywordstring否任务名等关键词
statusenum否Running / Stopped / Completed / Failed
taskIdsstring[]否指定任务 ID 列表,最多 100

用户提到已有任务但没有 taskId 时,先用本工具再导出或启停。


start_or_stop_task — 启停任务

参数类型必填说明
taskIdstring是已有任务 ID
actionenum是start 或 stop

规则:

  • 对未在运行的任务执行 stop,可能返回成功且状态为 already_stopped。
  • start 可能因积分、权限、模板不可用、限流失败——直接展示工具返回说明,不要盲目重试。
  • 可能的情况下先查状态,再决定是否 start。

错误处理

情况处理建议
401 / invalid_token请用户重新连接本 Connector(OAuth)
execute_task 返回 input_required按返回补参数 / source 选项后重试
模板找不到放宽 query 或改用精确 id/slug
execute 后仍为 running轮询 get_task_status,不要编造已完成
积分不足 / 权限不足展示工具原文;勿盲目重试
export_data 为空核对 lotNo、任务状态与页码
长采集超时用 taskId / taskName 恢复,继续轮询状态

回复风格

  • 进度更新宜短,突出 taskId、lotNo、行数等关键字段。
  • 若已有 directAccess.curlTemplate,不要把超大 data 数组整段贴进对话。
  • 若界面已展示列表,口头摘要即可,不必逐条重复。

相关链接

Frequently asked questions

What does the Bazhuayu Skill AI skill do?

八爪鱼云采集 MCP 技能 - 搜索模板、启动与管理任务、查询进度、导出结构化数据

Why use Bazhuayu Skill on TypingMind?

Because you install it once and use it with any model. Bazhuayu Skill 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 Bazhuayu Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/infometa/workbuddyskills/tree/main/connectors/bazhuayu/skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bazhuayu Skill?

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 Bazhuayu Skill?

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

Is the Bazhuayu Skill AI skill free?

It is published on GitHub by infometa. Check the repository for licensing terms. 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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