Open Websearch Maintainer

CommunityPopular

Aas-ee

open-websearch-maintainer

Maintain and extend the open-websearch MCP server. Trigger when changing search engines, proxy or TLS behavior, tool registration, live parsing behavior, or compatibility and regression behavior inside this repository.

Overview

Publisher	Aas-ee
Repository	`open-webSearch`
Skill name	`open-websearch-maintainer`
Stars	1.8K
Forks	186
Bundled files	2
License	Apache-2.0
Links	Skill source Repository Homepage

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

Installation

Install the Open Websearch Maintainer 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.
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.

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/Aas-ee/open-webSearch.git /tmp/open-webSearch
mkdir -p .claude/skills
cp -r /tmp/open-webSearch/skills/open-websearch-maintainer .claude/skills/open-websearch-maintainer

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

Use it in TypingMind

Enable Open Websearch Maintainer 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 Open Websearch Maintainer 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 Open Websearch Maintainer 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.

Open WebSearch Maintainer

Use this skill when modifying the open-websearch codebase itself.

If this skill and the current repository behavior disagree, trust the repository code, tests, and intended change scope, then update the skill or docs before finishing.

First classify the change

Engine change: new engine, engine parsing, engine pagination, anti-bot handling.
Network change: proxy, TLS, request builder, Playwright interaction, browser fallback.
Tool surface change: MCP tool registration, schema, engine normalization, config exposure.
Validation-only change: tests, docs, or release hygiene when no code-path behavior changes are intended.

Use this classification as the execution entrypoint. Start by choosing one category, load only the checklist that matches it, and expand scope only when the changed code path actually crosses that boundary.

Scope discipline

Keep fixes as local as the real failure surface allows.
Do not turn a parsing or engine-specific issue into a shared networking refactor without evidence.
Do not widen insecure or compatibility-relaxing behavior just to make one test pass.
Do not change tool contracts or global behavior unless the change is intentional, verified, and documented.

Core rules

Keep MCP tool contracts stable unless the change explicitly requires a breaking change.
Route Axios-based networking through the shared HTTP request builder instead of ad hoc proxy handling.
Treat proxy, TLS, and Playwright behavior as cross-cutting concerns. Verify them explicitly after changes.
Update tests and README files when adding engines or changing behavior.

Do not

Do not bypass the shared HTTP request builder for a one-off engine fix.
Do not silently fork proxy behavior in a single engine unless the repository explicitly adopts that policy.
Do not widen insecure TLS behavior beyond the narrow code path that requires it.
Do not change tool contracts without checking tests, config handling, and README files.
Do not touch shared networking code if the change is only about result parsing or docs.

Change checklist

When adding or changing a search engine:

update src/config.ts
update src/tools/setupTools.ts
add or update engine tests
update README.md and README-zh.md

When changing network behavior:

prefer src/utils/httpRequest.ts
keep proxy: false behavior explicit for Axios
keep insecure TLS opt-in and scoped only where necessary

Validation

Run TypeScript checks first.
Run targeted tests for the touched area.
Run live tests when the change depends on real network behavior.

Review focus

pagination and empty-result handling
anti-bot / verification page detection
proxy interactions with USE_PROXY and PROXY_URL
request-mode versus Playwright-mode behavior
documentation drift

Read references/workflow.md when implementing changes and references/validation.md before finalizing a patch.

Bundled files

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

references/validation.md1 KB
references/workflow.md1 KB

Frequently asked questions

What does the Open Websearch Maintainer AI skill do?

Why use Open Websearch Maintainer on TypingMind?

Because you install it once and use it with any model. Open Websearch Maintainer 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 Open Websearch Maintainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aas-ee/open-webSearch/tree/main/skills/open-websearch-maintainer. 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 Open Websearch Maintainer?

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 Open Websearch Maintainer?

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

Is the Open Websearch Maintainer AI skill free?

Yes. It is published on GitHub by Aas-ee under the Apache-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

Open WebSearch Maintainer

First classify the change

Scope discipline

Core rules

Do not

Change checklist

Validation

Review focus

Bundled files

Frequently asked questions

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