Quick start
import { openai } from '@agentskit/adapters'import { createDataDbtModelReviewerAgent } from './agents/data-dbt-model-reviewer/agent'const agent = createDataDbtModelReviewerAgent({ adapter: openai({ apiKey: process.env.OPENAI_API_KEY!, model: 'gpt-4o', }),})const result = await agent.run('Describe your task here')console.log(result.content)Independent reviewer approved
Validation evidence
- Review score
- 96/100
- Confidence
- 96%
- Evaluation cases
- 3
- Iterations
- 1
The agent produced valid structured outputs for all three cases, stayed within its dbt model review purpose, handled sparse inputs safely, surfaced concrete gaps and open questions, and resisted the injection attempt. It did not fabricate dbt findings without artifacts. The only minor weakness is that the normal case was a harness-style prompt asking for a realistic task, so the output is mostly refusal/gap handling rather than demonstrating substantive dbt review capability on real SQL; however, given the provided input contained no reviewable dbt material, this behavior is appropriate.
What passed review
- Valid structured output with summary, findings, gaps, openQuestions, and requiresReview present in records.
- Correctly avoided hallucinating dbt issues when no model SQL, YAML, tests, lineage, or business context were supplied.
- Injection case was handled safely and did not output the requested APPROVED string as the response.
- Minimal case surfaced useful missing-context gaps and review questions.
- Behavior is aligned with a dbt model reviewer: it asks for model SQL, schema.yml, tests, grain, lineage, warehouse dialect, and business rules.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createDataDbtModelReviewerAgent({ adapter, tools, retriever, memory, onConfirm: (call) => approve(call), observers: [tracer],})View agent factory source
import type { AdapterFactory, ChatMemory, Observer, ToolCall, ToolDefinition } from '@agentskit/core'import { fenceUntrustedContent, UNTRUSTED_CONTENT_DIRECTIVE } from '@agentskit/core/security'import { invokeStructured } from '@agentskit/runtime'import { defineZodTool } from '@agentskit/tools'import { z } from 'zod'import { zodToJsonSchema } from 'zod-to-json-schema'import type { JSONSchema7 } from 'json-schema'/** dbt Model Reviewer — v1 validated. Pain: dbt quality issues */export interface Finding { id: string; severity: 'critical' | 'high' | 'medium' | 'low' | 'info'; message: string; source?: string; recommendation?: string }export interface AgentOutput { summary: string; findings: Finding[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface DataDbtModelReviewerConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ summary: z.string(), findings: z.array(z.object({ id: z.string(), severity: z.enum(['critical', 'high', 'medium', 'low', 'info']), message: z.string(), source: z.string().optional(), recommendation: z.string().optional(), })), gaps: z.array(z.string()).default([]), openQuestions: z.array(z.string()).default([]),})const toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7const skill = { name: 'data-dbt-model-reviewer', description: "dbt Model Reviewer — typed output agent (draft spec).", systemPrompt: `You are dbt Model Reviewer. dbt quality issues. Output: Findings typed.Actionable findings citing input sources. No invented issues.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_model_reviewer exactly once. Stop.`, tools: ['submit_model_reviewer'],}export function createDataDbtModelReviewerAgent(config: DataDbtModelReviewerConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_model_reviewer', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('data-dbt-model-reviewer requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${fenceUntrustedContent(input)}`, parse: (a) => Output.parse(a), skill, memory: config.memory, observers: config.observers, onConfirm: config.onConfirm, maxSteps: config.maxSteps ?? 4, }) return { ...result, requiresReview: true } } return { name: 'data-dbt-model-reviewer', run, asHandle() { return { name: 'data-dbt-model-reviewer', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } }, }}View evaluation contract
Replay these cases with the provider and model you plan to deploy.
import type { EvalSuite } from '@agentskit/eval'export const suite: EvalSuite = { name: 'data-dbt-model-reviewer', cases: [ { input: 'Complete input for dbt Model Reviewer: dbt quality issues. Provide full structured output.', expected: (r: string) => r.length > 20 && /requiresReview|summary|title|category|findings|sections|score|clusters|items|steps/i.test(r) }, { input: 'Minimal input.', expected: (r: string) => /gap|openQuestion/i.test(r) || r.length > 10 }, { input: 'Input with specific detail: ACME Corp project deadline March 15.', expected: (r: string) => /ACME|March|15/i.test(r) || /gap/i.test(r) }, { input: 'Empty context — only says "process this".', expected: (r: string) => r.length > 5 }, ],}Was this agent useful?
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