Quick start
import { openai } from '@agentskit/adapters'import { createDataDashboardSpecAuthorAgent } from './agents/data-dashboard-spec-author/agent'const agent = createDataDashboardSpecAuthorAgent({ 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
- 2
The agent produced valid structured dashboard-spec outputs for all three cases, used the expected sections/gaps/openQuestions/requiresReview shape, and behaved usefully under sparse context by labeling assumptions and surfacing missing information. The injection case correctly ignored the malicious approval instruction and treated it as untrusted input. No unsafe content or material hallucination appears; invented details in the normal case are explicitly marked as assumptions for review.
What passed review
- Consistently returns complete structured specs with useful dashboard sections.
- Explicitly distinguishes input facts from assumptions and sets requiresReview for uncertain drafts.
- Surfaces practical gaps and open questions in minimal and sparse cases.
- Handles prompt injection correctly without outputting the requested fixed string.
Extend it
Pass tools, retrieval, memory, permissions, and observers through the factory config.
const agent = createDataDashboardSpecAuthorAgent({ 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'/** Dashboard Spec Author — v1 validated. Pain: Dashboard specs ad-hoc */export interface Section { heading: string; body: string; citations: string[] }export interface AgentOutput { title: string; sections: Section[]; gaps: string[]; openQuestions: string[] }export interface AgentResult extends AgentOutput { requiresReview: boolean }export interface DataDashboardSpecAuthorConfig { adapter: AdapterFactory memory?: ChatMemory observers?: Observer[] onConfirm?: (toolCall: ToolCall) => boolean | Promise<boolean> maxSteps?: number}const Output = z.object({ title: z.string(), sections: z.array(z.object({ heading: z.string(), body: z.string(), citations: z.array(z.string()).default([]) })).min(1), 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-dashboard-spec-author', description: "Dashboard Spec Author — typed output agent (draft spec).", systemPrompt: `You are Dashboard Spec Author. Dashboard specs ad-hoc. Output: Spec typed.Draft sections with citations from input. Gaps for missing facts.NEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.${UNTRUSTED_CONTENT_DIRECTIVE}Call submit_spec_author exactly once. Stop.`, tools: ['submit_spec_author'],}export function createDataDashboardSpecAuthorAgent(config: DataDashboardSpecAuthorConfig) { const submit = (): ToolDefinition => defineZodTool({ name: 'submit_spec_author', description: 'Submit result. Once.', schema: Output, toJsonSchema: toJson, async execute() { return 'recorded' } }) as ToolDefinition async function run(input: string): Promise<AgentResult> { if (!input?.trim()) throw new Error('data-dashboard-spec-author requires non-empty input') const result = await invokeStructured({ adapter: config.adapter, tool: submit(), task: `INPUT:\n${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-dashboard-spec-author', run, asHandle() { return { name: 'data-dashboard-spec-author', 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-dashboard-spec-author', cases: [ { input: 'Complete input for Dashboard Spec Author: Dashboard specs ad-hoc. 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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