Account health without the gut feel

Nobody needs to read every Slack thread to know if an account is healthy — that's an inference problem, not a willpower problem. These blueprints read the scatter, Slack, usage, audit, and return the same structured health read every time.

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Parameters

Typed inputs, declared up front

Tools

BigQuery

Slack

Adoption risk

• Sessions
• Scoped context per call

Typed output

• Health overview
• Risk flags
• Adoption gaps

What breaks when you do this by hand

• Account health lives in someone's head, read fresh out of Slack threads every time.
• Adoption and reliability data sits in tables no one queries by hand — so health calls run on gut feel.
• Tenant reviews mean rebuilding the same deck from scratch, every quarter.

What you get instead

• One structured health read — usage, audit, and error data, reconciled automatically
• Scheduled reporting instead of a person scrambling before every QBR
• The same signal, read the same way, across every account

This is the whole thing

The opening of Customer Health Report — the system prompt, its typed parameters, and the tools it's allowed to reach, all declared up front. No canvas, no hidden nodes. 221 lines of source you can review in a pull request.

Read all 221 lines →

customer-analytics.fml

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system("You are a CS and DevOps analyst generating a customer health report.")

parameter("tenant_id", type=string, title="Tenant ID")
parameter("customer_name", type=string, title="Customer Name")

require mcp BigQuery

components {
    schema("ServerStats") {
        mcp_name: string
        status: string
        total_tool_calls: int
        total_errors: int
        error_rate: float
        active_users: int
        distinct_tools_called: int
        active_days: int
        first_call: string
        last_call: string
        days_since_last_call: int
        connected_but_never_called: bool
    }
}

3 blueprints you can run today

Every one is a typed FML plan. Open it, read the source, and run it — nothing here is a mockup.

Customer Health Report \ Health calls run on gut feel because the data sits in warehouse tables nobody queries by hand.\ \ Customer success\ \ Account teams\ \ Ops\ \ Read the blueprint Customer Slack Signals \ Account health lives in scattered Slack threads. No one has a repeatable way to read it — so nobody really does.\ \ Customer success\ \ Account management\ \ Support\ \ Read the blueprint Tenant Usage Report \ A dozen ad-hoc warehouse queries, rebuilt by hand for every tenant review.\ \ Customer success\ \ Platform ops\ \ Account teams\ \ Read the blueprint

What every blueprint here guarantees

• Zero prompt drift
• Scoped sessions
• Typed output
• Reusable like an API

Every blueprint is a versioned contract. Run 1 and run 10,000 behave identically. Each LLM call sees only the context it needs — no one giant prompt, no context rot. Blueprints return validated objects pinned to a schema, not text you have to parse. Parameterise once and call it from anywhere — versioned, auditable, shareable.