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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".

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  • SKILL.md5.0 KB
  • scripts/run_oracle.py1.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Consult Oracle

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

Configuration

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

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.

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:

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.

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