Super Agent Skill vs custom GPTs
Custom GPTs made packaging instructions mainstream: give a system prompt a name and a storefront listing. For consumer chat, that's plenty. For teams wiring agents into real systems, the packaging is the easy part — portability, provenance, and testing are where custom GPTs stop and skills begin.
Walled garden vs open protocol
A custom GPT runs in one place: ChatGPT. Its instructions cannot follow you to another model, another runtime, or your own agent infrastructure. MCP skills install into any MCP-capable agent with a single URL or an npx command — the skill is an asset you own and move, not a configuration trapped in one vendor's UI.
Trust: store listing vs verifiable evidence
GPT store rankings run on usage and ratings — the same popularity signals that miss robustness entirely, and GPT instructions are famously extractable and modifiable by their owner at any time with no notice to users. There is no signature binding behavior to an author, and no published hostile-testing results.
Skills on Super Agent Skill are Ed25519-signed and tamper-evident, adversarially tested by attacker and judge LLMs with pass rates on the listing, and re-tested by SkillForge as models change. The Trust Score is a public, reproducible formula: adversarial test results count for 45%, real-world success telemetry for 20%, signing status for 10%, package age for 15%, and schema validity for 10%. Nothing is editorially assigned, and anyone can recompute a score offline with npm run trust:verify.
Production posture
Custom GPTs are a consumer distribution channel; they don't slot into CI, code review, or a security team's audit workflow. Skills are text artifacts your team can review in a PR, verify offline, and pin by signature — the properties infrastructure teams already expect from every other dependency they ship.
The verdict
Custom GPTs are great storefronts for chat experiences. Agent infrastructure deserves dependencies that are portable, signed, and adversarially tested.
Frequently asked questions
Can I convert a custom GPT into a skill?
The instructions port naturally: package them as a skill, sign it, and submit it for adversarial testing. You gain portability across MCP runtimes plus a published Trust Score.
Do skills work with OpenAI models?
Skills are model-agnostic instructions delivered over MCP; any MCP-capable agent runtime can install them, regardless of the underlying model vendor.
Why does tamper-evidence matter here?
A custom GPT's owner can silently change its instructions after you've adopted it. A signed skill cannot change without breaking Ed25519 verification, which you can check yourself with npm run trust:verify.
Every listing shows its Trust Score, adversarial pass rate, and Ed25519 signing status.
One URL installs the marketplace into any MCP-capable agent. No SDK, no migration.
Want to audit us? Run npm run trust:verify to recompute any Trust Score and verify signatures offline.