beyond-the-wrapper — Claude Code skill for AI product positioning
Find a product’s edge beyond its model, from data and workflow to domain expertise and reliability. Useful when buyers call it a wrapper or question its accuracy.
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a claude-codeWhat beyond-the-wrapper does
The skill helps explain what sets an AI product apart when competitors rely on similar models. It points to proprietary context, workflow depth, encoded domain judgment, production reliability, or distribution as possible sources of an edge. It also works through the question of why major model providers would not build the same product, and advises leading with the job the product does rather than an AI label.
Its guidance includes rewriting a pitch without the word “AI,” describing failure cases, and explaining how customer data is handled. It recommends showing evaluations on real tasks and clarifying where human review remains in the loop. If you cannot name a real advantage, the issue may be the product itself, not its pitch.
What it helps with
- explain how a product differs from a basic model wrapper
- answer why major model providers would not build the same product
- describe AI failure cases without overstating accuracy
- rewrite a product pitch without leading with AI
- show what makes a tool reliable in production
Example prompts for the agent
- Help me explain how our product differs from a basic model wrapper.
- Draft a clear answer to: why wouldn’t OpenAI or Anthropic build this?
- Rewrite our product pitch without leading with AI, and identify its real edge.
How to install beyond-the-wrapper
1Skills CLI (Vercel)
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a claude-code2Manually: put the files in place
~/.claude/skills/05-beyond-the-wrapper/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/05-beyond-the-wrapper/
1Skills CLI (Vercel)
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a codex2Manually: put the files in place
.agents/skills/05-beyond-the-wrapper/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a cursor2Manually: put the files in place
.cursor/skills/05-beyond-the-wrapper/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install AIDevGTM/gtm-cofounder 05-beyond-the-wrapper2Manually: put the files in place
.github/skills/05-beyond-the-wrapper/SKILL.md1Skills CLI (Vercel)
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a gemini-cli2Platform CLI
gemini skills install https://github.com/AIDevGTM/gtm-cofounder --path skills/05-beyond-the-wrapperManually: put the files in place
.devin/skills/05-beyond-the-wrapper/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about beyond-the-wrapper
How do I install beyond-the-wrapper in Claude Code?
Run npx skills add AIDevGTM/gtm-cofounder --skill 05-beyond-the-wrapper -a claude-code. The skill file is at ~/.claude/skills/05-beyond-the-wrapper/SKILL.md.
What does beyond-the-wrapper do?
It helps position an AI product around an advantage beyond its underlying model, such as proprietary data, workflow depth, domain expertise, or reliability. It also covers accuracy concerns and questions about competition with major model providers.
How does the skill recommend handling AI accuracy concerns?
It advises stating where the product performs well and where it fails instead of overstating accuracy. It also suggests showing evaluations on real tasks and explaining when a person stays involved.
Source text
SKILL.md · MIT
--- name: beyond-the-wrapper description: "Position an AI product when everyone claims AI and skeptics call it \"just a wrapper.\" Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer \"won't the big labs just build this,\" and stop leading with \"AI-powered.\" Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity." --- # 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. Positioning line to work toward: "The model is a commodity. The [your real moat] is not." ## Trust is the product For an AI tool, reliability is the whole game, and every buyer has been burned by a confident wrong answer. So position on it, out loud: - **Do not overclaim accuracy.** Publish what it is good at and where it fails. Devs trust the tool that tells them its limits. - **Show your work:** evals, benchmarks on real tasks, what happens when the model is unsure, where a human stays in the loop. - **Close the demo-to-production gap.** Anyone can demo an AI thing. The wedge is being the one that survives edge cases, weird inputs, and real data. Position on production, not magic. - **Answer the data question before they ask:** where does their data go, is it used for training, can they self-host or keep it in region. For many buyers this decides the deal, not output quality. ## The two objections you will always get **"Isn't this just a wrapper?"** Do not get defensive. Agree the model is commodity, then name the part that is not: "The generation is the easy 20 percent. We do the [data / workflow / reliability / domain] that makes it usable in production." If you cannot name that part, that is the real problem, and it is a product problem, not a pitch problem. **"Won't OpenAI or Anthropic just build this?"** Answer in one sentence or you do not have a wedge yet. The honest answers are usually: they build horizontal capability, you win on a vertical, workflow, or data set they will never go deep on. Or: you ride their improvements (every model release makes you better) instead of competing with them. Pick the true one and say it plainly. ## Stop leading with "AI" "AI-powered" is puffery now, the same class of word as "powerful," "seamless," and "platform" (see `value-prop-that-converts`). Developers skim past it. Lead with the job done; the AI is the *how*, not the headline. "Turn X into Y in seconds" beats "AI-powered X platform" every time. Test it: if your pitch still makes sense with the word "AI" deleted, you are positioned on the problem. If it collapses, you are positioned on the technology. ## Mistakes that look reasonable - **Leading with the model or "AI-powered."** It signals commodity and invites the wrapper objection you are trying to avoid. - **Overclaiming accuracy or hiding failure modes.** Devs find the failure case, and the trust never comes back. - **Competing on model quality.** You do not control the model. Compete on the last mile. - **Ignoring the data question.** For enterprise and EU buyers, "where does our data go" often outranks output quality. Silence reads as a red flag. - **A great demo and no production story.** The demo gets the meeting. Reliability gets the renewal. Demo only, and you have a toy. - **No answer to "won't the big labs build this."** If you cannot say it in a sentence, you have not found your wedge. ## Your next 30 minutes - [ ] Write your pitch, then delete the word "AI." If it still says what you do and for whom, good. If it collapses, rewrite it around the problem. - [ ] Name your real moat in one line: "The model is a commodity. Our [data / workflow / reliability / domain] is not." - [ ] Write the one-sentence answer to "won't OpenAI or Anthropic just build this?" If you cannot, that is your most important strategic question this month. - [ ] List your top three failure modes and decide how you will show, not hide, them (evals, "here is where it struggles," human-in-the-loop). - [ ] Answer the data question on your site before a buyer has to ask: where it goes, training, self-host or region. --- Built from real dev-tool GTM experience, with frameworks from Adam Frankl (*The Developer-Facing Startup*) and Jakub Czakon (*markepear.dev*). If "just a wrapper" is a fight you're losing and you want help finding the real wedge, reach out to me on [LinkedIn](https://www.linkedin.com/in/devtoolgtm/).
© AIDevGTM · MIT · AIDevGTM/gtm-cofounder