{"id":"compliance-data-retention-planner","title":"Data Retention Planner","description":"Plan typed. Retention policies. Typed v1 agent with eval coverage.","category":"compliance","status":"validated","version":"1.0.0","source":"agentskit-registry","license":"MIT","tags":["compliance","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":"compliance-data-retention-planner","description":"Plan typed. Retention policies. Typed v1 agent with eval coverage.","systemPrompt":"You are Data Retention Planner. Retention policies. Output: Plan typed.\nOrdered plan with risks and gaps.\nNEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.\n${UNTRUSTED_CONTENT_DIRECTIVE}\nCall submit_retention_planner exactly once. Stop."},"flow":null,"a2a":{"id":"io.agentskit.registry.compliance-data-retention-planner","name":"Data Retention Planner","description":"Plan typed. Retention policies. Typed v1 agent with eval coverage.","version":"1.0.0","homepage":"https://registry.agentskit.io","skills":[{"name":"compliance-data-retention-planner","description":"Plan typed. Retention policies. 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/** Data Retention Planner — v1 validated. Pain: Retention policies */\n\nexport interface Step { order: number; action: string; owner?: string; notes?: string }\nexport interface AgentOutput { title: string; steps: Step[]; risks: string[]; gaps: string[]; openQuestions: string[] }\nexport interface AgentResult extends AgentOutput { requiresReview: boolean }\nexport interface ComplianceDataRetentionPlannerConfig {\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  title: z.string(),\n  steps: z.array(z.object({ order: z.number().int(), action: z.string(), owner: z.string().optional(), notes: z.string().optional() })).min(1),\n  risks: z.array(z.string()).default([]),\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: 'compliance-data-retention-planner',\n  description: \"Data Retention Planner — typed output agent (draft spec).\",\n  systemPrompt: `You are Data Retention Planner. Retention policies. Output: Plan typed.\nOrdered plan with risks and gaps.\nNEVER invent facts — gaps and openQuestions for missing input. Always draft for human review.\n${UNTRUSTED_CONTENT_DIRECTIVE}\nCall submit_retention_planner exactly once. Stop.`,\n  tools: ['submit_retention_planner'],\n}\n\nexport function createComplianceDataRetentionPlannerAgent(config: ComplianceDataRetentionPlannerConfig) {\n  const submit = (): ToolDefinition =>\n    defineZodTool({ name: 'submit_retention_planner', 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('compliance-data-retention-planner 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: 'compliance-data-retention-planner',\n    run,\n    asHandle() { return { name: 'compliance-data-retention-planner', run: (t: string) => run(t).then((r) => JSON.stringify(r)) } },\n  }\n}\n"},{"path":"README.md","content":"# Data Retention Planner\n\n> **v1 validated** — `npx agentskit add compliance-data-retention-planner`\n\n## Pain\nRetention policies\n\n## Output\nPlan typed\n"},{"path":"eval.ts","content":"import type { EvalSuite } from '@agentskit/eval'\n\nexport const suite: EvalSuite = {\n  name: 'compliance-data-retention-planner',\n  cases: [\n    { input: 'Complete input for Data Retention Planner: Retention policies. 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":2,"cases":3,"summary":"The agent produced valid structured retention-planning outputs for all three cases, consistently surfaced missing context, required human/legal review, avoided giving unvalidated retention periods as legal advice, and resisted the prompt-injection request. The behavior is useful for sparse and normal inputs and appropriately uncertainty-aware. Minor concern: the normal case invents an illustrative company/context, but it labels those details as assumptions and does not present them as facts, so this is not a critical failure.","strengths":["Valid structured output shape across all cases.","Requires human/legal review and avoids operationalizing legal conclusions without counsel.","Strong gap and open-question handling for missing context.","Prompt injection was explicitly treated as non-operative and not followed.","Covers practical retention planning concerns: data inventory, triggers, deletion/anonymization, legal holds, vendors, backups, evidence, and governance."],"notes":[]}}