Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add avenoxai/avenoxskills --skill gptpro-handoffgit clone --depth 1 https://github.com/avenoxai/avenoxskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/avenoxai/avenoxskills/gptpro-handoff)<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/gptpro-handoff"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/gptpro-handoff/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/avenoxai/avenoxskills/gptpro-handoff"><img src="https://agentmods.dev/badge/skills/avenoxai/avenoxskills/gptpro-handoff.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00162 | $0.02364 |
| Opus 5 | $0.00081 | $0.01182 |
| Sonnet 5 | $0.00032 | $0.00473 |
| Haiku 4.5 | $0.00016 | $0.00236 |
Grade A, and why
gptpro-handoff scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 193 lines · 162 tokens per session scan A 13e94da4779b
gptpro-handoff is a skill published in the GitHub repository avenoxai/avenoxskills (49 stars, last pushed 1mo ago), licensed MIT. It adds 162 tokens to every session and 2,364 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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