Reverse-proxy forwarding for agent skill paths
If docs sit behind a proxy or custom domain, forward `/skill.md`, `/.well-known/skills/*`, and `/.well-known/agent-skills/*` instead of letting the discovery layer die at the edge.
Why this can grow a startup
A capability file only helps if the agent can actually fetch it. Reverse proxies often preserve the pretty docs URL while quietly dropping newer AI-discovery paths. Forwarding the skill routes keeps the capability layer reachable, which matters because agents often start at the public hostname, not the upstream docs subdomain where the files were first generated.
Source-backed example
Mintlify tells teams using a reverse proxy to forward `/skill.md`, `/.well-known/skills/*`, and `/.well-known/agent-skills/*` to the Mintlify subdomain so agents can still discover the files.
Source-reported evidence
Source: Mintlify Docs · Browse Mintlify Docs tactics
Mintlify documents three exact proxy paths that should be forwarded for skill discovery to work
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
Source discovered: May 27, 2026
When to use it
Use this when AI Search, Website, Documentation is relevant to agent enablement, technical seo, docs ops and you can run a bounded test with a free budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to AI Search and Website, using evidence from Mintlify Docs as context—not as a promised result.
- Record a baseline for: Qualified search visitors who complete the intended next action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
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