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Deep Research

Community
TheGoat395
deep-research

Research with scoped depth and counterevidence.

Overview

PublisherTheGoat395
RepositoryCodex-Skills
Skill namedeep-research
Stars
136
Forks
48
Bundled files
6
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TheGoat395 on GitHub. Read the source before you install it.

Installation

Install the Deep 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/TheGoat395/Codex-Skills/tree/main/skills/deep-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/TheGoat395/Codex-Skills.git /tmp/Codex-Skills
mkdir -p .claude/skills
cp -r /tmp/Codex-Skills/skills/deep-research .claude/skills/deep-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep 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 Deep 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 Deep 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.

Deep Research

Produce a decision-ready result whose material claims can be traced to inspected evidence. Derive the necessary research universe from the objective, pursue it broadly and deeply in proportion to consequence, challenge the leading conclusion, and finish the natural in-scope work without waiting for follow-up prompts.

Read for the chosen depth

  • Focused: apply the contract, hierarchy, exact claim support and stopping rule below; use the relevant sections of evidence standard when evidence ambiguity or an auditable ledger matters.
  • Deep or exhaustive: read coverage and research passes and evidence standard before substantive research. Map, screen, deepen, challenge and verify; source count is not coverage.
  • Exhaustive coverage, exceptional consecutive maximum-reasoning passes, or joint $godmode work: also read challenger and handoff. Deep Research establishes the decision. If a separately installed $godmode skill is explicitly invoked, use it for authorized execution; otherwise complete authorized implementation and verification directly. Its absence is not a blocker.
  • Material access/search failure or long, multi-surface, compaction-prone work: read the applicable recovery or checkpoint sections of access and continuity.

Inspect exact artifacts; snippets are discovery aids. Preserve protected-source exclusions on alternate routes. Challenge the strongest countercase and complete material in-scope omissions. Separate screened and deeply inspected coverage. For deep/exhaustive or consequential delivery, read research delivery; otherwise lead with the answer, exact supporting citations and material uncertainty. Instruction authority controls work, not empirical truth: test factual claims even in governing sources. Do not expand scope or force durable ledgers on small work.

Establish the Research Contract

  1. Identify the decision, deliverable, or truth claim the research must support.
  2. Derive scope, exclusions, time horizon, freshness, jurisdiction, acceptable uncertainty, and failure conditions from the request and governing sources.
  3. Define observable acceptance criteria and what evidence could change the answer.
  4. Inspect the exact named sources and the governing material applicable to the decision. Read complete operative sections and enough surrounding context to preserve meaning; expand to full documents or collections when requested or needed. Report partial coverage honestly rather than requiring an unrelated repository or account tour.
  5. Make reasonable reversible assumptions when they preserve the objective; surface only assumptions that materially affect the result.

Keep research read-only unless the request separately authorizes implementation, communication, purchasing, publication, or account mutation.

Calibrate Depth

Classify the task before gathering evidence:

  • Focused: resolve a bounded question with the smallest sufficient evidence.
  • Deep: map the credible field, verify decisive claims, investigate contradictions, and recommend a direction.
  • Exhaustive: derive the full relevant research graph, screen the broad field, deeply inspect the strongest bounded subset, run an adversarial completion pass, and create a decision-to-execution handoff in one invocation.

Use deep mode for explicit deep research. Reserve exhaustive bounded coverage for explicit exhaustive, maximum or complete research intent; keep focused, deep and exhaustive modes distinct. Do not make the user discover and request natural subquestions one at a time. Do not make a focused task exhaustive merely because more sources exist.

Apply the Evidence Hierarchy

Use current explicit instructions and governing sources to establish the requested scope and approved operating decisions. They do not make an empirical claim true. For factual support, use the strongest applicable evidence:

  1. Verified live state and direct records.
  2. Original authoritative evidence, including relevant governing records, first-party data, official documentation, filings, standards, source code, and primary materials.
  3. Independent analysis with visible methods and incentives.
  4. Credible specialist reporting.
  5. Community experience and anecdotal evidence as field signal.
  6. Metadata, summaries, snippets, and remembered context only as discovery aids.

Verify unstable facts live. Test incentivized claims independently when the decision warrants it. Do not inflate confidence by counting multiple retellings of one origin as independent evidence.

Stop at Decision Sufficiency

Stop when:

  • acceptance criteria are met;
  • all material coverage dimensions are addressed or explicitly unresolved;
  • decisive claims use the strongest practical evidence;
  • contradictions, inaccessible evidence, and uncertainty are visible;
  • the recommendation survives the challenger pass;
  • additional work is unlikely to change the decision enough to justify its cost.

Do not stop at the first plausible answer. Do not continue collecting repetitive evidence after the decision is stable.

Bundled files

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

Frequently asked questions

What does the Deep Research AI skill do?

Research with scoped depth and counterevidence.

Why use Deep Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheGoat395/Codex-Skills/tree/main/skills/deep-research. 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 Deep 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 Deep Research?

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

Is the Deep Research AI skill free?

Yes. It is published on GitHub by TheGoat395 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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