Agent-skills manifest with sha256 integrity
Publish `/.well-known/agent-skills/index.json` with a digest for each skill so agents can verify they fetched the right instructions before they act on them.
Why this can grow a startup
Capability discovery gets more useful when the client can trust the file it just loaded. The manifest digest is a small detail with a big effect: it helps agents verify content integrity, reduces confusion around stale mirrors, and makes the discovery layer feel like infrastructure instead of an undocumented convenience page.
Source-backed example
Mintlify's recommended agent-skills endpoint uses the 0.2.0 discovery schema and includes a `sha256` digest for every listed skill file.
Source-reported evidence
Source: Mintlify Docs · Browse Mintlify Docs tactics
The agent-skills 0.2.0 manifest includes a sha256 digest for each skill file
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, Documentation, Integrations is relevant to agent enablement, integration hygiene, brand trust 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 Documentation, 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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