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Independent Research

Community
NTCoding
independent-research

Use when about to ask the user a factual question, propose a solution, diagnose an error, or choose between approaches. Triggers on: 'Do you have X installed?', 'What version?', 'Is X configured?', 'We should...', 'The fix is...', 'Options: 1...', 'Based on my understanding...', 'I believe X supports...'. Before deciding anything, spin up parallel subagents to WebSearch for current docs, community solutions, framework best practices, and GitHub issues. Your memory is stale — verify everything.

Overview

PublisherNTCoding
Repositoryclaude-skillz
Skill nameindependent-research
Stars
356
Forks
47
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 NTCoding on GitHub. Read the source before you install it.

Installation

Install the Independent Research 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/NTCoding/claude-skillz/tree/main/independent-research
  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/NTCoding/claude-skillz.git /tmp/claude-skillz
mkdir -p .claude/skills
cp -r /tmp/claude-skillz/independent-research .claude/skills/independent-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Independent Research 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 Independent Research 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 Independent Research 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.

Independent Research

Research-driven investigation. Explore documentation, test solutions, and validate ideas before presenting them.

Critical Rules

🚨 NEVER ASK FACTUAL QUESTIONS YOU CAN ANSWER YOURSELF. If a command, config file, or web search can answer it, use that. Only ask users about preferences and priorities.

🚨 VALIDATE BEFORE PRESENTING. Test commands, verify syntax, check documentation. Never present untested recommendations.

Lazy Question Detection

If you catch yourself about to ask any of these, STOP. Answer it yourself.

Lazy QuestionWhat To Do Instead
"Do you have X installed?"Run which X or X --version
"What version of X?"Run X --version
"Is X configured?"Check the config file
"What OS are you using?"Run uname -a
"Which tool do you use for X?"Check which/where, look at project config
"Was this working before?"Check git log, recent changes
"Do you want me to..."Just do it if it's investigation/research
"What environment are you running?"Check env vars, config files, runtime versions
"Have you tried X?"Try it yourself first

Anti-pattern

❌ Asking Instead of Investigating

⏺ NX project graph failed — Node.js v24 compatibility issue.

⏺ Do you have nvm/fnm/volta installed? Was this working
  on a different Node version?

The user doesn't need to answer this. You have bash.

bash
which nvm; which fnm; which volta
node --version
cat .nvmrc 2>/dev/null
cat package.json | grep -A2 '"engines"'
git log --oneline -20

Premature Decision Detection

If you catch yourself about to recommend, diagnose, or decide without having researched, STOP.

Premature Decision SignalWhat To Do Instead
"We should downgrade/upgrade X"Spin up parallel subagents: one to WebSearch the error + framework version, one to fetch the framework's migration/compatibility docs, one to search GitHub issues for the exact error
"The fix is to..."Search for the exact error message first. Find the GitHub issue. Read the fix. Don't guess from memory
"This is a known issue with X"Prove it. Fetch the GitHub issue URL, the changelog entry, the docs page. If you can't link to it, you're guessing
"Options: 1. X, 2. Y"Before presenting options, WebSearch for what the framework/community actually recommends. Check migration guides, official docs, recent blog posts
"I believe X supports Y" / "Based on my understanding"That's stale memory. WebFetch the actual docs page. Check the release notes. Verify the version compatibility matrix
Choosing between tools/versions/approachesLaunch parallel subagents: one per approach, each researching current docs, community consensus, and known issues
Error you haven't seen beforeWebSearch the exact error message in quotes. Check GitHub issues. Check Stack Overflow. Check the framework's Discord/discussions
Recommending a config changeFetch the framework's current configuration docs. Don't rely on what you remember the API being

🚨 Be paranoid that you're missing a better solution. Your training data is stale. Libraries release weekly. Frameworks ship breaking changes. The answer you "know" may be outdated. Launch subagents to verify in parallel — it costs seconds and saves hours of wrong-direction work.

🚨 Concrete actions, not vague research:

  • WebSearch the exact error message, the framework + version, the specific compatibility question
  • WebFetch the official docs page, the changelog, the migration guide
  • Launch parallel subagents when multiple angles need investigating simultaneously
  • Check GitHub issues for the exact error — someone has probably hit this before
  • Read the actual release notes — don't guess what version supports what

When to Ask vs Research

Research yourself (facts):

  • What's installed, what version, what's configured
  • Whether something is compatible
  • What the error means
  • What solutions exist

Ask the user (preferences):

  • Which approach they prefer
  • What their priorities are
  • Design decisions with multiple valid options
  • Business context you can't infer

Mandatory Checklist

Before asking the user a question:

  1. Verify this is a preference/priority question, NOT a factual question
  2. Verify you cannot answer it with a command, config file read, or web search
  3. Verify you have already tried to answer it yourself

Do not ask until all checks pass.

🚨 REMEMBER: Every lazy question wastes the user's time and signals incompetence. If you can look it up, look it up.

Frequently asked questions

What does the Independent Research AI skill do?

Use when about to ask the user a factual question, propose a solution, diagnose an error, or choose between approaches. Triggers on: 'Do you have X installed?', 'What version?', 'Is X configured?', 'We should...', 'The fix is...', 'Options: 1...', 'Based on my understanding...', 'I believe X supports...'. Before deciding anything, spin up parallel subagents to WebSearch for current docs, community solutions, framework best practices, and GitHub issues. Your memory is stale — verify everything.

Why use Independent Research on TypingMind?

Because you install it once and use it with any model. Independent Research 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 Independent Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NTCoding/claude-skillz/tree/main/independent-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Independent Research?

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 Independent Research?

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

Is the Independent Research AI skill free?

It is published on GitHub by NTCoding. 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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