Research logo

Research

Community
cubetribe
research

Research task workflow — @researcher agent for technology evaluation, best practices lookup, documentation discovery, with a non-enforced time budget guideline and memory guidelines

Overview

Publishercubetribe
RepositoryClaudeCode_GodMode-On
Skill nameresearch
Stars
54
Forks
10
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 cubetribe on GitHub. Read the source before you install it.

Installation

Install the 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/cubetribe/ClaudeCode_GodMode-On/tree/main/skills/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/cubetribe/ClaudeCode_GodMode-On.git /tmp/ClaudeCode_GodMode-On
mkdir -p .claude/skills
cp -r /tmp/ClaudeCode_GodMode-On/skills/research .claude/skills/research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Research Workflow

Command

"Research: [topic]"

Workflow

@researcher → report with sources

When to Use @researcher

ScenarioUse @researcher?
New library/framework neededYES
Best practices for unfamiliar domainYES
Security advisory checkYES
API documentation lookupYES
Well-known patterns (REST, MVC)NO
Bug fix in existing codeNO
Refactoring known codeNO

@researcher Configuration

  • Model: haiku (fast & cost-effective)
  • Tools: WebSearch, WebFetch, Read, Glob, memory MCP
  • Target budget: ~30 seconds per task (guideline, not an enforced limit — no Timeout field on agents/researcher.md and no hook enforces it; see skills/cost-efficiency/ § Research Budget for the actual mechanism, which is scope discipline, not a clock)

Phase Budget (Guideline, Not Enforced)

These per-phase figures are a pacing target for the agent to self-report against, not a hard cutoff — nothing currently kills a task at these marks:

PhaseTargetIf running long
WebSearch~10sUse cached/partial results
WebFetch~8sSkip this source
Analysis~12sReport with available data
Total~30sProduce partial report

Report Format

markdown
# Research Report: [Topic]

## Summary
[1-2 sentence overview of findings]

## Findings
1. [Finding with source]
2. [Finding with source]
3. [Finding with source]

## Recommendation
[Clear recommendation with rationale]

## Sources
- [Source 1](URL) — [brief description]
- [Source 2](URL) — [brief description]

## Confidence
[HIGH/MEDIUM/LOW] — [reason]

Save to: reports/vX.Y.Z/sprint-NN/00-researcher-report.md

Graceful Degradation

If research fails or times out:

  1. Full Report — All sources found, all analysis complete
  2. Partial Report — Some sources found, partial analysis
  3. Failure Report — Research failed, structured error output

Partial report format includes completion_percentage and sources_status (FULL/PARTIAL/PENDING).

Memory Usage

After completing research:

  • STORE in memory: Key decisions, no-go technologies, verified sources
  • STORE when: High-confidence findings, deprecated tech discoveries
  • QUERY first: Check memory before starting new research (avoid duplicates)

Integration with Other Workflows

@researcher is OPTIONAL in Feature and API Change workflows:

Feature:    (@researcher) → @architect → @builder → gates → @scribe
API Change: (@researcher) → @architect → @api-guardian → @builder → gates → @scribe

Use when the task involves unfamiliar technology. Skip when working with known patterns.


CC_GodMode — © 2025–2026 Dennis Westermann (dennis-westermann.de). Proprietary — not open source. Free for private, non-commercial use; redistribution or re-hosting outside GitHub is prohibited; attribution required. Official source: github.com/cubetribe/ClaudeCode_GodMode-On. See LICENSE.

Frequently asked questions

What does the Research AI skill do?

Research task workflow — @researcher agent for technology evaluation, best practices lookup, documentation discovery, with a non-enforced time budget guideline and memory guidelines

Why use Research on TypingMind?

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

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

Which AI models can use 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 Research?

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

Is the Research AI skill free?

It is published on GitHub by cubetribe. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇