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Consult Oracle

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tobihagemann
consult-oracle

Consult ChatGPT Pro via ChatGPT browser automation for problems that resist standard approaches. Use when stuck on a very hard problem, when standard approaches have failed, when multiple debugging attempts haven't worked, or when the user says "ask the oracle", "consult oracle", "consult chatgpt", "I'm completely stuck", "I've tried everything", or "nothing is working".

Overview

Publishertobihagemann
Repositoryturbo
Skill nameconsult-oracle
Stars
417
Forks
32
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Consult Oracle 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/tobihagemann/turbo/tree/main/claude/skills/consult-oracle
  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/tobihagemann/turbo.git /tmp/turbo
mkdir -p .claude/skills
cp -r /tmp/turbo/claude/skills/consult-oracle .claude/skills/consult-oracle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Consult Oracle 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 Consult Oracle 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 Consult Oracle 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.

Consult Oracle

Consult ChatGPT Pro via ChatGPT browser automation for problems that resist standard approaches.

Configuration

The oracle reads from ~/.turbo/config.json:

json
{
  "oracle": {
    "chatgptUrl": "https://chatgpt.com/"
  }
}
KeyPurposeDefault
chatgptUrlChatGPT URL (e.g., a custom GPT project URL)https://chatgpt.com/

Step 1: Identify Key Files

Find the 2-5 files most relevant to the problem.

Step 2: Formulate the Question

Write a clear, specific problem description. Include what has already been tried and why it failed. Open with a short project briefing (stack, services, build steps). The more context, the better the response.

Step 3: Run the Oracle

Run via the Bash tool (timeout: 600000, do not set run_in_background). The script loads chatgptUrl from ~/.turbo/config.json automatically and consults through the oracle's own signed-in ChatGPT profile. Generate a random tag and persist the response:

bash
ORACLE_TAG=$(head -c 4 /dev/urandom | xxd -p) && mkdir -p "$PWD/.turbo/oracle" && echo "$PWD/.turbo/oracle/$ORACLE_TAG.txt"

Substitute the printed value for <printed-path> in the command below and on every follow-up turn. Shell variables do not survive between Bash tool calls, and an earlier cd in a compound command leaves the session in a different directory, so a relative path resolves against that directory instead.

bash
python3 scripts/run_oracle.py --prompt "<problem description>" --file <relevant files...> --write-output "<printed-path>"

Keep backticks, $, and straight double quotes out of --prompt even in text you wrote: the first two stay live inside the quotes, and a double quote ends them. Text you did not author — a diff, file contents, an error trace, command output — goes in a file passed with --file, written with the Write tool, as does any title or passage the prompt would otherwise quote. This holds on follow-up turns too.

If the run fails, retry the command once — same prompt, attachments, and timeout — when the failure looks transient, such as a browser challenge or automation error while the signed-in session is otherwise healthy. Report an authentication, browser-challenge, or permission blocker only after the retry reproduces it, and cite the failing output. Do not broaden permissions when the current context already has the access the run needs.

When the failure says the requested model is missing from the model picker and lists the available options, the CLI's default target is out of date. Instead of the identical retry, re-run once with --model gpt-6-pro, and name that model in the Step 5 summary. A Pro entry in that list does not work as a --model value.

A consult that outlives the timeout is normally moved to the background: the result carries a task ID, and the run continues to completion. Wait for the <task-notification> that reports completion, then read <printed-path>. Do not re-run the consult while it runs in the background, and never take the task ID, or <printed-path> read before that notification, as the answer. When the notification reports a failure, read the error from the task's output file and apply the retry rules above.

Step 4: Follow Up

Resume the same ChatGPT conversation with --followup and the session slug. The prior turn's attached files and context persist, so re-attaching the full diff each turn is unnecessary. Run via the Bash tool with the same settings as Step 3 (timeout: 600000, do not set run_in_background), and recover a backgrounded run the same way:

bash
python3 scripts/run_oracle.py --followup "<session-slug>" --prompt "<follow-up>" --write-output "<printed-path>"

Find the slug with python3 scripts/run_oracle.py status (the Slug column; follow-ups nest under their parent session) or from the directory names under ~/.oracle/sessions/.

Reuse the chat for a multi-turn review of the same code; start a fresh session (Step 3) for an unrelated question. Cap at 5 turns to prevent runaway conversations.

When the reviewed code changed since the prior turn, re-attach the changed files (--file <paths>) or a fresh diff, or state what changed. Otherwise the model reasons from the earlier attachments and flags already-fixed issues as live contradictions.

Step 5: Synthesize

Read the response from <printed-path>. Summarize the key insights from the consultation. Cross-reference suggestions with official docs and peer open-source implementations before applying. Oracle suggestions are starting points, not guaranteed solutions.

Then use the TaskList tool and proceed to any remaining task.

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 Consult Oracle AI skill do?

Consult ChatGPT Pro via ChatGPT browser automation for problems that resist standard approaches. Use when stuck on a very hard problem, when standard approaches have failed, when multiple debugging attempts haven't worked, or when the user says "ask the oracle", "consult oracle", "consult chatgpt", "I'm completely stuck", "I've tried everything", or "nothing is working".

Why use Consult Oracle on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tobihagemann/turbo/tree/main/claude/skills/consult-oracle. 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 Consult Oracle?

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 Consult Oracle?

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

Is the Consult Oracle AI skill free?

Yes. It is published on GitHub by tobihagemann 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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