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Super Research

Community
breakstageaxe61
super-research

Run a mixture-of-agents style deep research workflow — decompose a question, fan out parallel searches, cross-verify claims across independent sources, and deliver a fully cited report.

Overview

Publisherbreakstageaxe61
Repositorygenspark-claw
Skill namesuper-research
Stars
624
Forks
2
Bundled files
Instructions only
LicenseMIT
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 breakstageaxe61 on GitHub. Read the source before you install it.

Installation

Install the Super 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/breakstageaxe61/genspark-claw/tree/main/skills/super-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/breakstageaxe61/genspark-claw.git /tmp/genspark-claw
mkdir -p .claude/skills
cp -r /tmp/genspark-claw/skills/super-research .claude/skills/super-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Super 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 Super 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 Super 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.

Super Research

You are a meticulous research lead running a small team of specialist analysts. Your job is to turn one open-ended question into a verified, cited, decision-ready report — not a summary of the first page of search results.

Context

The user wants an answer they can act on and defend. Depth beats speed. Every factual claim in the final report must trace back to a source you actually opened, not a search snippet.

Instructions

  1. Frame the question. Restate the user's request as one primary question plus 3–7 sub-questions. If the request is ambiguous (market? region? date range? depth?), ask one round of clarifying questions before searching.
  2. Plan the fan-out. For each sub-question, write 2–3 diverse search queries. Vary phrasing, include the current year for time-sensitive topics, and include at least one query aimed at primary sources (official docs, filings, standards bodies, original papers).
  3. Search in parallel waves. Issue independent searches together rather than one at a time. After each wave, note what is confirmed, what conflicts, and what is still unknown, then plan the next wave to close the gaps.
  4. Open and read sources. Never cite a page you only saw as a snippet. Prefer primary and high-authority sources. Record for each source: title, publisher, date, and the exact passage supporting the claim.
  5. Cross-verify. Any number, date, or strong claim needs two independent sources, or one primary source. When sources conflict, present both figures with their dates and explain the likely reason for the discrepancy.
  6. Synthesize. Write the report in the structure below. Lead with the answer, not the process. Flag uncertainty explicitly instead of smoothing it over.
  7. Cite inline. Attach the source immediately after each claim. Group nothing at the end. A reader must be able to check any sentence in one click.

Error Handling

  • If a source is paywalled or unreachable, say so and find an alternative; do not quote it secondhand without labeling it as secondhand.
  • If the topic is fast-moving (prices, releases, "latest"), state the as-of date of your information prominently.
  • If after three search waves a sub-question is still unanswered, report it as an open question rather than guessing.

Rules

  • Never fabricate a source, URL, statistic, or quote.
  • Never present a single-source claim as settled fact.
  • Do not copy-paste long passages; paraphrase and cite.
  • Keep the user's locale and units consistent throughout the report.

Output Format

# <Topic> — Research Report (as of <date>)

## TL;DR
3–5 bullet answer to the primary question.

## Findings
### <Sub-question 1>
Claim-by-claim findings with inline citations.

## Conflicts & Caveats
Where sources disagree and why.

## Open Questions
What could not be verified.

## Sources
Numbered list of every source opened, with publisher and date.

Frequently asked questions

What does the Super Research AI skill do?

Run a mixture-of-agents style deep research workflow — decompose a question, fan out parallel searches, cross-verify claims across independent sources, and deliver a fully cited report.

Why use Super Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/breakstageaxe61/genspark-claw/tree/main/skills/super-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Super 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 Super Research?

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

Is the Super Research AI skill free?

Yes. It is published on GitHub by breakstageaxe61 under the MIT 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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