gptpro-handoff

name

gptpro-handoff

description

Run the research/audit handoff loop between an agentic coding harness and a slow, non-agentic frontier reasoning model (GPT Pro on the ChatGPT web UI, or equivalent) with the codebase pre-loaded as split zip bundles. Author a high-signal prompt, then later take the returned report, verify every finding against the LIVE codebase, and implement what holds up. Use whenever the operator wants to send something to a deep external model, prep a prompt or report, scope an issue for deep research, bug-hunting, or optimization analysis, OR when a report comes back and needs checking against the code before anything is implemented. This is the WORKFLOW skill — to build or refresh the zip bundles themselves, use the separate `gptpro` export skill.

Deep-model handoff loop

A slow, non-agentic frontier reasoning model is a different tool from your coding agent, and it needs a different workflow. This skill is that workflow.

Written against GPT Pro on the ChatGPT web UI, with the codebase loaded into a Project as split zip bundles (produced by the gptpro export skill). It transfers to any deep model you reach through a chat UI rather than a harness.

The defining fact: the model is non-agentic — no shell, no live filesystem, bounded context. It reasons over a static snapshot.

That drives the entire discipline: it proposes, you verify, then you implement. Its report is the strongest kind of input, but it is read against a snapshot that may lag the live tree, and it cannot run a test. Nothing ships unchecked. In practice the large majority of findings hold up — the job is to catch the ones that don't before they cost you.

What this model is (and how it behaves)

Internalize the model before writing for it. Its constraints and its personality both shape the prompt.

• No harness, no live repo. It runs in the chat provider's own container: no shell, no tools, no filesystem. It sees only the bundled snapshot you upload. On the harness axis it is strictly disadvantaged; its reasoning depth is what makes up for that. You give up "can run a test" to buy "can out-think the problem."
• Slow, deep, one-shot. A single answer can take ~50–60 minutes. You do not iterate cheaply. The prompt must be complete and self-contained, because there is no follow-up channel mid-run.
• The prompt is the entire interface. The agent authors it; the operator copies it verbatim into the web UI. No tool call, no back-channel. What you write is exactly and only what the model gets.
• The operator sets the reasoning tier at send time. NEVER put reasoning-effort instructions in the prompt (no "use xhigh", no <REASONING> block) — that's a UI control, so any such text is dead weight crowding the real instructions.
• Obsessively literal. It follows instructions to the letter and hyper-focuses on exactly what you specify. A superpower for audits, and a trap for research: over-constrain a prompt meant to surface theories and it will tunnel on your seeds and never bring you the ideas you couldn't think of yourself.

Calibrate the leash to the intent

Because the model is so literal, prompt tightness is a dial you set deliberately:

• Audit / verification → tight leash. When the job is "check THIS against the code," constrain hard: VERIFIED/HYPOTHESIS labeling, confirm-or-refute each seeded hypothesis, fixed output format, DO-NOT roadmap. The rigid structure is what makes verification fast and the report triageable.
• Research / theory / exploration → loose leash. When the job is "tell us what we're missing," do the opposite: state the problem and the constraints, then explicitly grant freedom to reason independently, challenge your framing, and surface directions you didn't ask about. Seeds are a floor, not a fence — say so ("our hypotheses are a starting point; go beyond them, and tell us where we're wrong"). Leash a research prompt like an audit and you waste the one thing this model is best at.
• Blended (most real prompts) → label the halves. Mark which objectives are "verify tightly" and which are "run free."

Keep the invariants hard even on a loose leash. Free on ideas, fixed on constraints.

The loop

1. SCOPE      — pin one well-framed problem with the operator (not "review everything").
2. AUTHOR     — write the prompt together; save to gptpro/issue-NN-<slug>.md.
3. FRESHEN    — if code changed materially since the last export, re-run `gptpro`.
4. HAND OFF   — operator pastes the prompt into the web UI.
5. RECEIVE    — operator brings the report back; save to gptpro/issue-NN-<slug>.report.md.
6. VERIFY     — re-check every finding against the LIVE codebase (this is the gate).
7. IMPLEMENT  — apply what holds up; record what didn't and why.

Steps 1–3 are one session's work. Steps 5–7 usually happen in a later session, when the report lands.

1–2. Scope and author

A good payload is one hard problem, deeply framed — not a grab-bag. Pin three things with the operator first:

• The decision — what the model should produce (a critique? a chosen design? a prioritized fix list? a teardown of an approach you doubt?).
• The frame — the minimal context that makes it answerable: constraints, what's already decided and won't be relitigated, invariants it must respect.
• Focus + anti-focus — the surfaces and files to attend to, and what's explicitly out of scope so it doesn't wander.

Then write in the house style — see references/prompt-style.md for the XML structure, the VERIFIED-vs-HYPOTHESIS rule, and a ready skeleton. Non-negotiables:

• Ground it in your code. Give the real architecture and file anchors. Seed your hypotheses, but tag each "confirm, refute, or extend against the actual code" so it verifies rather than parrots.
• Set the leash to the intent (above).
• State invariants as hard constraints — always. The model is helpful to a fault; if you don't say a consistency boundary must hold, it may cheerfully propose moving it.
• Demand a prioritized, incremental roadmap with impact × effort ranking and a DO-NOT column — that roadmap is the direct input to step 7. (A pure-theory prompt may want an open synthesis instead.)
• Never put a reasoning level in the prompt.

Where prompts and reports live

Everything goes in a gitignored gptpro/ folder at the repo root — these are local handoff artifacts, never committed:

• gptpro/issue-NN-<slug>.md — the prompt you author.
• gptpro/issue-NN-<slug>.report.md — the returned report, pasted back.

Number issues sequentially so a session can carry several handoffs and you can refer to them unambiguously.

3. Keep the bundles fresh

The model reasons over the last-exported zips. If you've shipped changes since the last export and the new prompt depends on that code, the snapshot is stale — re-run the gptpro export and have the operator re-upload the changed bundles. When in doubt, name the export date in the prompt so the model knows the snapshot's age and can flag drift itself.

Standing project instructions

If your chat provider supports a persistent Project or custom-instructions blurb, put the "what this system is + answer like a senior engineer, ground in the attached code, label uncertainty" context there once. Individual prompts then focus on the specific issue instead of re-explaining the project every time. If the operator is setting up a fresh Project, offer that blurb first.

5–6. Receive and verify (the gate)

This is where the value is captured or lost. For each finding, before treating it as actionable:

• Re-anchor it. Open the file:line it cited in the LIVE tree. Bundles lag — the line may have moved, the code may already be fixed, or the symbol may be gone. Confirm the finding still describes reality.
• Reproduce the claim. It couldn't run anything. If it claims a bug, trace the code path or write the failing test. If it claims a cost, sanity-check the mechanism. A VERIFIED tag means "I found it in the snapshot," not "I proved it at runtime."
• Check it against your invariants. Reject anything that weakens a governance gate, crosses a consistency boundary, or contradicts a decision already made — even if the model is confident. Confident-and-wrong is the failure mode that matters.
• Triage into: implement now, needs-a-decision (back to the operator), and discard — with a one-line reason so it isn't re-litigated later.

Spawn parallel verification sub-agents when a report has many findings, one per finding or cluster — but the conclusion is yours to make, grounded in the live code, not the sub-agent's say-so.

7. Implement

Implement what survived verification, following the repo's normal discipline (tests with every change; typecheck and build green before claiming done). Work the prioritized roadmap top-down. When a finding is implemented or rejected, note it in the .report.md so the next session knows what's been actioned. The operator can then take a follow-up question back for the next round.

Anti-patterns

• Don't rubber-stamp. "The model said so" is not a reason to merge. The high hit rate is earned by verifying, not a license to skip it.
• Don't hand it a vague brief. "Review the codebase" wastes a 60-minute run. One framed problem per prompt.
• Don't let bundles rot silently. A confident report against month-old code is worse than no report. Freshen, or flag the snapshot age.
• Don't fragment a coherent problem into many thin prompts when one deep, well-scoped prompt would let the model see the whole shape. Genuinely separate concerns do get separate, sequential prompts.