{"id":"marketing-attribution-interpreter","title":"Attribution Interpreter","description":"Insights typed. Attribution reports confusing. Typed v1 agent with eval coverage.","category":"marketing","status":"validated","version":"1.0.0","source":"agentskit-registry","license":"MIT","tags":["marketing","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":"marketing-attribution-interpreter","description":"Insights typed. Attribution reports confusing. Typed v1 agent with eval coverage.","systemPrompt":"You are Attribution Interpreter. Attribution reports confusing. Output: Insights 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_attribution_interpreter exactly once. Stop."},"flow":null,"a2a":{"id":"io.agentskit.registry.marketing-attribution-interpreter","name":"Attribution Interpreter","description":"Insights typed. Attribution reports confusing. Typed v1 agent with eval coverage.","version":"1.0.0","homepage":"https://registry.agentskit.io","skills":[{"name":"marketing-attribution-interpreter","description":"Insights typed. Attribution reports confusing. 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/** Attribution Interpreter — v1 validated. Pain: Attribution reports confusing */\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 MarketingAttributionInterpreterConfig {\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: 'marketing-attribution-interpreter',\n  description: \"Attribution Interpreter — typed output agent (draft spec).\",\n  systemPrompt: `You are Attribution Interpreter. Attribution reports confusing. Output: Insights 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_attribution_interpreter exactly once. Stop.`,\n  tools: ['submit_attribution_interpreter'],\n}\n\nexport function createMarketingAttributionInterpreterAgent(config: MarketingAttributionInterpreterConfig) {\n  const submit = (): ToolDefinition =>\n    defineZodTool({ name: 'submit_attribution_interpreter', 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('marketing-attribution-interpreter 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: 'marketing-attribution-interpreter',\n    run,\n    asHandle() { return { name: 'marketing-attribution-interpreter', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } },\n  }\n}\n"},{"path":"README.md","content":"# Attribution Interpreter\n\n> **v1 validated** — `npx agentskit add marketing-attribution-interpreter`\n\n## Pain\nAttribution reports confusing\n\n## Output\nInsights typed\n"},{"path":"eval.ts","content":"import type { EvalSuite } from '@agentskit/eval'\n\nexport const suite: EvalSuite = {\n  name: 'marketing-attribution-interpreter',\n  cases: [\n    { input: 'Complete input for Attribution Interpreter: Attribution reports confusing. 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, did not follow the injection, did not invent attribution insights from missing data, and consistently surfaced gaps, uncertainty, review needs, and concrete next questions. The behavior is conservative but aligned with an attribution interpreter that should not hallucinate metrics or campaign conclusions without source data.","strengths":["Valid structured output was recorded for every case.","Appropriately refused to fabricate attribution findings from sparse or meta-level inputs.","Handled prompt injection by treating it as untrusted data and preserving task purpose.","Clearly identified missing attribution model, date range, channel, spend, conversion, revenue, and decision context.","Provided useful next-step questions and data requirements."],"notes":[]}}