{"id":"data-dbt-model-reviewer","title":"dbt Model Reviewer","description":"Findings typed. dbt quality issues. Typed v1 agent with eval coverage.","category":"data","status":"validated","version":"1.0.0","source":"agentskit-registry","license":"MIT","tags":["data","structured-output","v1"],"packages":["@agentskit/core","@agentskit/runtime","@agentskit/tools"],"files":["agent.ts","README.md","eval.ts"],"requires":{"zod":"^3","zod-to-json-schema":"^3"},"skill":{"name":"data-dbt-model-reviewer","description":"Findings typed. dbt quality issues. Typed v1 agent with eval coverage.","systemPrompt":"You are dbt Model Reviewer. dbt quality issues. Output: Findings typed.\nActionable findings citing input sources. No invented issues.\nNEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.\n${UNTRUSTED_CONTENT_DIRECTIVE}\nCall submit_model_reviewer exactly once. Stop."},"flow":null,"a2a":{"id":"io.agentskit.registry.data-dbt-model-reviewer","name":"dbt Model Reviewer","description":"Findings typed. dbt quality issues. Typed v1 agent with eval coverage.","version":"1.0.0","homepage":"https://registry.agentskit.io","skills":[{"name":"data-dbt-model-reviewer","description":"Findings typed. dbt quality issues. Typed v1 agent with eval coverage.","capabilities":{"streaming":true,"cancellation":true,"requiresApproval":false}}]},"sources":[{"path":"agent.ts","content":"import type { AdapterFactory, ChatMemory, Observer, ToolCall, ToolDefinition } from '@agentskit/core'\nimport { fenceUntrustedContent, UNTRUSTED_CONTENT_DIRECTIVE } from '@agentskit/core/security'\nimport { invokeStructured } from '@agentskit/runtime'\nimport { defineZodTool } from '@agentskit/tools'\nimport { z } from 'zod'\nimport { zodToJsonSchema } from 'zod-to-json-schema'\nimport type { JSONSchema7 } from 'json-schema'\n\n/** dbt Model Reviewer — v1 validated. Pain: dbt quality issues */\n\nexport interface Finding { id: string; severity: 'critical' | 'high' | 'medium' | 'low' | 'info'; message: string; source?: string; recommendation?: string }\nexport interface AgentOutput { summary: string; findings: Finding[]; gaps: string[]; openQuestions: string[] }\nexport interface AgentResult extends AgentOutput { requiresReview: boolean }\nexport interface DataDbtModelReviewerConfig {\n  adapter: AdapterFactory\n  memory?: ChatMemory\n  observers?: Observer[]\n  onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean>\n  maxSteps?: number\n}\n\nconst Output = z.object({\n  summary: z.string(),\n  findings: z.array(z.object({\n    id: z.string(), severity: z.enum(['critical', 'high', 'medium', 'low', 'info']),\n    message: z.string(), source: z.string().optional(), recommendation: z.string().optional(),\n  })),\n  gaps: z.array(z.string()).default([]),\n  openQuestions: z.array(z.string()).default([]),\n})\nconst toJson = (s: z.ZodTypeAny): JSONSchema7 => zodToJsonSchema(s) as JSONSchema7\n\nconst skill = {\n  name: 'data-dbt-model-reviewer',\n  description: \"dbt Model Reviewer — typed output agent (draft spec).\",\n  systemPrompt: `You are dbt Model Reviewer. dbt quality issues. Output: Findings typed.\nActionable findings citing input sources. No invented issues.\nNEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.\n${UNTRUSTED_CONTENT_DIRECTIVE}\nCall submit_model_reviewer exactly once. Stop.`,\n  tools: ['submit_model_reviewer'],\n}\n\nexport function createDataDbtModelReviewerAgent(config: DataDbtModelReviewerConfig) {\n  const submit = (): ToolDefinition =>\n    defineZodTool({ name: 'submit_model_reviewer', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition\n\n  async function run(input: string): Promise<AgentResult> {\n    if (!input?.trim()) throw new Error('data-dbt-model-reviewer requires non-empty input')\n    const result = await invokeStructured({\n      adapter: config.adapter,\n      tool: submit(),\n      task: `INPUT:\\n${fenceUntrustedContent(input)}`,\n      parse: (a) => Output.parse(a),\n      skill,\n      memory: config.memory,\n      observers: config.observers,\n      onConfirm: config.onConfirm,\n      maxSteps: config.maxSteps ?? 4,\n    })\n    return { ...result, requiresReview: true }\n  }\n  return {\n    name: 'data-dbt-model-reviewer',\n    run,\n    asHandle() { return { name: 'data-dbt-model-reviewer', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } },\n  }\n}\n"},{"path":"README.md","content":"# dbt Model Reviewer\n\n> **v1 validated** — `npx agentskit add data-dbt-model-reviewer`\n\n## Pain\ndbt quality issues\n\n## Output\nFindings typed\n"},{"path":"eval.ts","content":"import type { EvalSuite } from '@agentskit/eval'\n\nexport const suite: EvalSuite = {\n  name: 'data-dbt-model-reviewer',\n  cases: [\n    { 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) },\n    { input: 'Minimal input.', expected: (r: string) => /gap|openQuestion/i.test(r) || r.length > 10 },\n    { input: 'Input with specific detail: ACME Corp project deadline March 15.', expected: (r: string) => /ACME|March|15/i.test(r) || /gap/i.test(r) },\n    { input: 'Empty context — only says \"process this\".', expected: (r: string) => r.length > 5 },\n  ],\n}\n"}],"installable":true,"validation":{"status":"approved","score":96,"confidence":0.96,"method":"codex-executor-independent-reviewer","iterations":1,"cases":3,"summary":"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.","strengths":["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."],"notes":[]}}