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 AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrappergit clone --depth 1 https://github.com/AIDevGTM/gtm-cofounderWrote 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/aidevgtm/gtm-cofounder/05-beyond-the-wrapper)<a href="https://agentmods.dev/skills/aidevgtm/gtm-cofounder/05-beyond-the-wrapper"><img src="https://agentmods.dev/badge/skills/aidevgtm/gtm-cofounder/05-beyond-the-wrapper/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/aidevgtm/gtm-cofounder/05-beyond-the-wrapper"><img src="https://agentmods.dev/badge/skills/aidevgtm/gtm-cofounder/05-beyond-the-wrapper.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 | $0.00095 | $0.01518 |
| Opus 5 | $0.00048 | $0.00759 |
| Sonnet 5 | $0.00019 | $0.00304 |
| Haiku 4.5 | $0.00010 | $0.00152 |
Grade A, and why
beyond-the-wrapper 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 12d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beyond the wrapper (positioning an AI product)
"AI-powered" is the new "powerful." When the model is a commodity anyone can call, your positioning cannot be the model. It has to be the problem you solve, the trust you earn, and the last mile nobody else does.
Use this when: people call your product "just a GPT wrapper," you blend into fifty tools that demo the same thing, buyers worry about accuracy or where their data goes, or you cannot answer "why won't OpenAI or Anthropic just build this?"
Not building an AI product? Skip this one. It is the one skill here that is not for everyone, and nothing else in the pack depends on it. Move straight on to
value-prop-that-converts. Come back only if the "is this just a wrapper?" question ever lands on you.
The core idea
Half your competitors have the same model behind them, so the model cannot be your pitch. The "wrapper" objection is not a technology problem, it is a positioning problem: you are being described at the feature level (see positioning-and-story), and features that call the same API are interchangeable. The work is to move up a level, to the specific problem, the specific buyer, and the specific reasons a developer would trust you over a weekend prototype and their own API key.
And a hard truth from developer psychology: developers will test your claims and find the truth. If you overclaim what the AI does, they will find the case where it fails, and you lose them for good. So in AI, honesty about limits is not a weakness. It is the differentiation.
Where the moat actually is (name yours)
The model is rented, and everyone rents the same one. Your defensibility is one of these, so say which:
- Proprietary data or context the raw model does not have (your users' data, your domain corpus, live signals).
- Workflow depth a model plus a prompt cannot touch (the last mile: integrations, UX, the ten unglamorous steps around the generation).
- Domain expertise encoded as evals, guardrails, and judgment a general model gets wrong.
- Reliability and trust: it works in production, not just in a demo.
- Distribution: you are already in their stack.
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.
- 12d ago First seen · 69 lines · 95 tokens per session scan A 88e24c32dbfa
beyond-the-wrapper is a skill published in the GitHub repository AIDevGTM/gtm-cofounder (274 stars, last pushed 4d ago), licensed MIT. It adds 95 tokens to every session and 1,518 once invoked, about $0.0005 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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