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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.

rare tactic
Best-fit channelAI Search · Documentation · Integrations
Stage + contextagent enablement · integration hygiene · brand trust · crawlability
BudgetFreebudget tier
Suggested operator signalQualified search visitors who complete the intended next action

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

AI SearchDocumentationIntegrationsagent enablementintegration hygienebrand trustcrawlability
GrowthDex operator note

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

  1. Choose one target segment and define the exact next action this tactic should prompt.
  2. Adapt the tactic to AI Search and Documentation, using evidence from Mintlify Docs as context—not as a promised result.
  3. Record a baseline for: Qualified search visitors who complete the intended next action. Set a guardrail that would make you pause the test.
  4. 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.
  5. 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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