gpt-pro-relay

effort: low name: gpt-pro-relay description: | Send a prompt to ChatGPT Pro Extended via gpt-pro-relay on macmini — over SSH from any other machine, or directly when invoked on macmini itself. Use for "ask gpt-pro", "send to gpt-pro", "use gpt-pro", "Pro Extended take", "ask the deep model", or "second opinion from chatgpt pro". The wrapper (not the caller) polls through flaky networks; the agent fires one backgrounded call and waits for the notification. allowed-tools: Bash(gpt-pro:), Bash(~/.claude/skills/gpt-pro-relay/scripts/gpt-pro:), Bash(ssh:*), Read, Write user-invocable: true

gpt-pro-relay

One prompt in, one response out. The gpt-pro wrapper composes the whole call: it generates the run-id, picks local-vs-SSH transport, submits, polls with backoff over flaky links, demuxes exit codes, and prints the response to stdout. The browser automation runs on macmini against a dedicated logged-in profile; a detached worker does the work so transport drops (or parent death) don't kill it — reconnect with gpt-pro --run-id <id>.

Quick start

The happy path, in execution order — the sections below are the authoritative detail; this is the shape:

1. Runtime — Pro Extended runs take 5–20 min. Launch immediately; the invocation (direct, or a calling skill adding a gpt-pro lens) is the go-ahead. Never pause to confirm — mention the runtime only as passing context if useful.
2. Compose a self-contained prompt in a file — GPT-Pro can't see anything local to you (codebase, shell, this conversation), so paste in every file, excerpt, and prior decision it needs. It can search the public web, so when the answer turns on external facts, include the grounding directive — see "Grounding external facts".
3. Run it as a Bash-tool call — mandatory on every call (a bare shell command is a trap: it blocks the turn and drops the envelope). Issue exactly:

   command:           gpt-pro < prompt.md
   run_in_background: true
   timeout:           7260000        # 121 min — see Background and timeout
   

   Response → stdout; run_id=<id> and a copy-paste recover_with=… line → stderr.
4. Wait for the completion notification — do NOT poll. A backgrounded call returns a task id immediately (expected); the harness notifies you when it exits. Don't re-run gpt-pro or re-read the task output in a loop meanwhile — the wrapper is already polling through any drops.
5. On failure, reattach (also backgrounded — recovery, not polling). If the call dies or exits 124/255, the macmini worker survives. Take the literal run-id from the task output (run_id=<id> / recover_with=… on the first stderr lines) and reattach in the same envelope: gpt-pro --run-id <id>. Then map the exit code — see "If it fails".

Invocation

Compose the prompt in a file (so $, backticks, and heredoc markers don't get mangled), then pipe it on stdin:

gpt-pro < prompt.md

That's the whole call. gpt-pro is on PATH (added in ~/.zshenv). The response is on stdout; the run-id, diagnostics, and the engine's JSONL are on stderr. Always issue it inside the Bash-tool envelope (run_in_background: true, timeout: 7260000) — see Background and timeout.

gpt-pro [--max-wait <sec>] [--dry-run] < prompt.md   # new run
gpt-pro --run-id <id> [--max-wait <sec>]             # reattach / recover

Want a durable copy? Keep the streams separate: gpt-pro < prompt.md > answer.md 2> gpt-pro.log — never 2>&1 (it contaminates the answer and buries the run_id). Optional: the backgrounded task output already captures both streams, so a redirect is only for a persistent on-disk artifact.

If gpt-pro: command not found (a sandboxed or reset-env shell that didn't inherit the .zshenv PATH), run the identical command from this skill's own scripts/ directory — the gpt-pro script sits beside this SKILL.md (<this-skill-dir>/scripts/gpt-pro < prompt.md).

Prompts must be self-contained

GPT-Pro runs in a ChatGPT web tab. It cannot see anything local to you — your codebase, your shell, the files on your machine, or this conversation. Every byte of local context it needs must be inside the prompt you pipe in: if you reference a file path, paste its contents; if a decision earlier in this session shapes the answer, paste that excerpt. It can, however, reach the public web through its own browser/search tool — so don't paste public docs it can fetch itself; tell it what to look up (see "Grounding external facts"). The rule: private-local context is pasted in; public-external facts are searched for.

Err toward more context, not less — the 1 MB submission cap is generous, and a run that fails for missing context still burns 5–20 min. Compose the prompt in a file. Prefer the Write tool — it's in this skill's allowed-tools (bare mktemp/cat/heredocs may not be), and it sidesteps $/backtick/heredoc mangling entirely. In a plain shell you can instead build it inline:

PROMPT_FILE=$(mktemp)
{
  cat <<'TASK'
<one-sentence outcome>

Success criteria:
- ...
TASK
  echo
  echo '=== src/foo.py ==='
  cat src/foo.py
} > "$PROMPT_FILE"

gpt-pro < "$PROMPT_FILE"

For recurring tasks, factor the composer into a small compose-prompt.sh in the project — keep the file list and section headers there rather than rewriting them on every call.

Grounding external facts

Local context is pasted in (above); public, external facts are searched for. When the answer turns on information not in the pasted context, GPT-Pro must search the web and ground its answer in cited sources. The directive below is conditional — a no-op on pure-reasoning tasks — so include it in prompt.md whenever the task could depend on external facts. Paste it verbatim:

## Grounding external facts

Some questions turn on facts not in the pasted context — current versions, releases, dates,
prices, API/library behavior, specs, benchmarks, CVEs, recent events, current best practices:
anything that changes over time or that you'd otherwise answer from memory. For any such fact
you MUST use web search and ground the answer in what you find — do not answer external or
time-sensitive questions from memory.

When you search:
- Corroborate each load-bearing fact across at least two independent sources (prefer
  primary/official). If sources conflict or only one exists, say so and mark it low-confidence.
- Cite the specific page URL you actually opened, inline next to the claim — never a homepage,
  a search-results page, or a URL reconstructed from memory. Didn't open it? Don't cite it.
- End with a `## Sources` list: every URL you opened, each with one line on what you took from it.

Do NOT search when the task is self-contained — pure reasoning, math, or analysis of the pasted
code/text — or to confirm stable facts you already know. If you didn't need external facts, say
so in one line ("No external sources needed — reasoning over the pasted material.") rather than
just omitting citations: that line tells the caller a deliberate skip from a missed one.

On return, verify grounding cheaply: a fact-dependent answer should carry inline URLs and a ## Sources list — spot-check one or two with the Read/WebFetch tools (does the page resolve, does it actually say that) before relying on it. A fact-heavy answer with no sources and no "no external sources needed" line is ungrounded — re-prompt or discount it.

Calibration block

For judgment tasks — analysis, recommendations, design, second opinions — have gpt-pro end with a reasons-based calibration block. Its slowness and cost make a self-graded score more tempting and no better calibrated, so ask for assumptions and failure modes, not a number. Skip it for deterministic lookups or mechanical transforms. Paste this into prompt.md (alongside the grounding directive when both apply):

## Calibration

End your response with this block exactly:
- Key assumptions: 1-3 the answer depends on ("none material" only if true)
- Most likely wrong because: the strongest failure mode, missing info, or counterargument
- Would change my conclusion: the specific fact, test, or counterexample that would flip it
- Best next check: the single source, test, or lookup that would most reduce uncertainty
- Verify before acting: specific current/high-stakes claims to check ("none" for pure reasoning)

No numeric %, probability, star rating, or High/Medium/Low label — this block is for routing and
verification, not a calibrated probability.

On return, treat it as the action plan: run the Best next check and verify the listed claims before relying on the answer. Because a re-query costs another 5–20 min, a targeted check is almost always cheaper than re-running. Discount any answer that self-asserts confidence in place of naming its assumptions.

Runtime note

Pro Extended runs take 5–20 minutes per prompt. Invoking gpt-pro is itself the go-ahead — whether the user named it directly or a calling skill (prism, goal-loop) added a gpt-pro lens. Just launch; do not pause to confirm or wait for a "continue". Mention the ~5–20 min runtime in passing only if it's useful context, never as a gate.

Background and timeout

On a remote machine gpt-pro runs its own poll loop for up to --max-wait (default 120 min); on macmini it blocks on the engine directly (the engine's cap and the Bash-tool timeout bound it there). The default is deliberately generous — a queued or long Pro run killed by a tight timeout wastes the tokens it already spent, so favor completion. Either way, always wrap the Bash invocation in:

• run_in_background: true
• timeout: 7260000 — Bash-tool timeout in milliseconds = 121 min, 60 s of slack over the default --max-wait 7200 (= 120 min) so the tool doesn't preempt the wrapper's final diagnostic. If you change --max-wait, set this to at least (--max-wait + 60) × 1000.

Three clocks, inner → outer: the engine's 60-min per-run cap → the wrapper's --max-wait poll deadline (default 120 min, SSH path) → the Bash-tool timeout (121 min). A reason: timeout (exit 3) is the engine cap firing, not --max-wait.

Wait for the completion notification. Do NOT poll from the agent side — the wrapper is already polling (the affirmative wait primitive is in Quick start step 4).

Recovery

gpt-pro prints run_id=<id> and a ready-to-run recover_with=gpt-pro --run-id <id> line to stderr at the start — both land in the background task's output file. Capture the literal id from there; a $RUN_ID shell variable does not survive into a later Bash-tool call. If the caller dies, or the wrapper exits 124 (timed out, still pending) or 255 (SSH transport unknown), the worker on macmini keeps running. Reattach with the same id, inside the same envelope (run_in_background: true, timeout: 7260000) — this is recovery, not the forbidden polling:

gpt-pro --run-id ask-20260611T002710Z-…    # the literal id, not "$RUN_ID"

This skips submit and waits for the existing run to complete (blocking fetch on macmini, poll loop over SSH). If the run already finished but you lost the output, read it off macmini directly — quote the remote command so ~ expands on macmini, not locally:

ssh macmini "cat ~/.gpt-pro/runs/<run_id>/response.md"

Concurrency

Up to GPT_PRO_MAX_PARALLEL (default 3) gpt-pro calls run in parallel — each worker gets its own tab in a single shared Chrome process. Beyond the cap, additional workers queue on a file-lock semaphore in ~/.gpt-pro/slots/ and wait for a slot to free up. Pro Extended runs are 5–20 min each, so a queued run can be in that same magnitude — don't set --max-wait (or the Bash-tool timeout) shorter than that.

Chrome stays alive between runs (no per-call launch cost after the first) — an invoking agent never needs to tear it down. ssh macmini gpt-pro-relay close-chrome is operator maintenance only (it refuses by default if any worker is in flight; --force kills anyway) — don't run it as part of normal use or recovery. Raising GPT_PRO_MAX_PARALLEL above the default is a knob, not a free upgrade: parallel bursts on one ChatGPT Pro session are an account-side anti-abuse signal. If network.json starts showing 429s, captcha redirects, or unexplained needs_reauth after parallel use, drop it back to 1.

If it fails

The wrapper's exit code is the agent's decision key — every code maps to one action. Empty prompts, oversized prompts (>1 MB), and malformed run-ids are caught before any submission (exit 2 — no quota burned).

On a non-zero exit the engine's terminal stderr JSON carries a reason; for exit 1 it disambiguates the cause:

Run artifacts

run_dir lives on macmini at ~/.gpt-pro/runs/<run_id>/:

• prompt.md, response.md, meta.json, result.json
• pre-send.png, streaming-NNN.png, final.png, error-*.png
• final.html, network.json
• worker.stdout — detached worker's stdout (usually empty)
• worker.stderr — structured JSONL stage trace: one line per stage (start, slot_queued/slot_acquired, chrome_cdp_ready/chrome_connected/chrome_activated, logged_in, model_verified, prompt_typed, sent, extracted, finished, plus error / orphan_kill_* / *_skipped). When something fails mid-run, this is the fastest path to the failure point — the last stage before the error line tells you where it died.

Reach for them via ssh macmini cat <run_dir>/<file> or ssh macmini ls <run_dir> when diagnosing.

When gpt-pro-relay fits

Multi-turn

gpt-pro is one-shot per invocation — every call is a fresh ChatGPT conversation. To continue a thread, paste the prior response into the next prompt yourself. The dedicated profile retains login but does not persist conversation context across calls.