Researching On The Internet logo

Researching On The Internet

Organization
ed3dai
researching-on-the-internet

Use when planning features and need current API docs, library patterns, or external knowledge; when testing hypotheses about technology choices or claims; when verifying assumptions before design decisions - gathers well-sourced, current information from the internet to inform technical decisions

Overview

Publishered3dai
Repositoryed3d-plugins
Skill nameresearching-on-the-internet
Stars
249
Forks
33
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 ed3dai on GitHub. Read the source before you install it.

Installation

Install the Researching On The Internet 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.
  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/ed3dai/ed3d-plugins.git /tmp/ed3d-plugins
mkdir -p .claude/skills
cp -r /tmp/ed3d-plugins/plugins/ed3d-research-agents/skills/researching-on-the-internet .claude/skills/researching-on-the-internet
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Researching On The Internet 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 Researching On The Internet 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 Researching On The Internet 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.

Researching on the Internet

Overview

Gather accurate, current, well-sourced information from the internet to inform planning and design decisions. Test hypotheses, verify claims, and find authoritative sources for APIs, libraries, and best practices.

When to Use

Use for:

  • Finding current API documentation before integration design
  • Testing hypotheses ("Is library X faster than Y?", "Does approach Z work with version N?")
  • Verifying technical claims or assumptions
  • Researching library comparison and alternatives
  • Finding best practices and current community consensus

Don't use for:

  • Information already in codebase (use codebase search)
  • General knowledge within Claude's training (just answer directly)
  • Project-specific conventions (check CLAUDE.md)

Core Research Workflow

Do not use nested subagents. If you are running as a research subagent, perform the research directly with web/search/fetch tools already available to you. Do not dispatch or invoke additional subagents.

  1. Define question clearly - specific beats vague
  2. Search official sources first - docs, release notes, changelogs
  3. Cross-reference - verify claims across multiple sources
  4. Evaluate quality - tier sources (official → verified → community)
  5. Report concisely - lead with answer, provide links and evidence

Hypothesis Testing

When given a hypothesis to test:

  1. Identify falsifiable claims - break hypothesis into testable parts
  2. Search for supporting evidence - what confirms this?
  3. Search for disproving evidence - what contradicts this?
  4. Evaluate source quality - weight evidence by tier
  5. Report findings - supported/contradicted/inconclusive with evidence
  6. Note confidence level - strong consensus vs single source vs conflicting info

Example:

Hypothesis: "Library X is faster than Y for large datasets"

Search for:
✓ Benchmarks comparing X and Y
✓ Performance documentation for both
✓ GitHub issues mentioning performance
✓ Real-world case studies

Report:
- Supported: [evidence with links]
- Contradicted: [evidence with links]
- Conclusion: [supported/contradicted/mixed] with [confidence level]

Quick Reference

TaskStrategy
API docsOfficial docs → GitHub README → Recent tutorials
Library comparisonOfficial sites → npm/PyPI stats → GitHub activity
Best practicesOfficial guides → Recent posts → Stack Overflow
TroubleshootingError search → GitHub issues → Stack Overflow
Current stateRelease notes → Changelog → Recent announcements
Hypothesis testingDefine claims → Search both sides → Weight evidence

Source Evaluation Tiers

TierSourcesUsage
1 - Most reliableOfficial docs, release notes, changelogsPrimary evidence
2 - Generally reliableVerified tutorials, maintained examples, reputable blogsSupporting evidence
3 - Use with cautionStack Overflow, forums, old tutorialsCheck dates, cross-verify

Always note source tier in findings.

Search Strategies

Multiple approaches:

  • WebSearch for overview and current information
  • WebFetch for specific documentation pages
  • Check MCP servers (Context7, search tools) if available
  • Follow links to authoritative sources
  • Search official documentation before community resources

Cross-reference:

  • Verify claims across multiple sources
  • Check publication dates - prefer recent
  • Flag breaking changes or deprecations
  • Note when information might be outdated
  • Distinguish stable APIs from experimental features

Reporting Findings

Lead with answer:

  • Direct answer to question first
  • Supporting details with source links second
  • Code examples when relevant (with attribution)

Include metadata:

  • Version numbers and compatibility requirements
  • Publication dates for time-sensitive topics
  • Security considerations or best practices
  • Common gotchas or migration issues
  • Confidence level based on source consensus

Handle uncertainty clearly:

  • "No official documentation found for [topic]" is valid
  • Explain what you searched and where you looked
  • Distinguish "doesn't exist" from "couldn't find reliable information"
  • Present what you found with appropriate caveats
  • Suggest alternative search terms or approaches

Common Mistakes

MistakeFix
Searching only one sourceCross-reference minimum 2-3 sources
Ignoring publication datesCheck dates, flag outdated information
Treating all sources equallyUse tier system, weight accordingly
Reporting before verificationVerify claims across sources first
Vague hypothesis testingBreak into specific falsifiable claims
Skipping official docsAlways start with tier 1 sources
Over-confident with single sourceNote source tier and look for consensus

Frequently asked questions

What does the Researching On The Internet AI skill do?

Use when planning features and need current API docs, library patterns, or external knowledge; when testing hypotheses about technology choices or claims; when verifying assumptions before design decisions - gathers well-sourced, current information from the internet to inform technical decisions

Why use Researching On The Internet on TypingMind?

Because you install it once and use it with any model. Researching On The Internet 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 Researching On The Internet in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ed3dai/ed3d-plugins/tree/main/plugins/ed3d-research-agents/skills/researching-on-the-internet. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Researching On The Internet?

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 Researching On The Internet?

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

Is the Researching On The Internet AI skill free?

It is published on GitHub by ed3dai. 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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