refactor(solve-issue): flatten role folders to single files
Also clean up leftover knowledge-extraction folder (renamed to extract-knowledge in dc1e96d). Refs uncaged/nerve#284
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import type { AgentFn, WorkflowDefinition } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { createLlmAdapter } from "@uncaged/nerve-workflow-utils";
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import { moderator } from "./moderator.js";
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import type { WorkflowMeta } from "./moderator.js";
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import { createAnswererRole } from "./roles/answerer.js";
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import { createExplorerRole } from "./roles/explorer.js";
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import { createQuestionerRole } from "./roles/questioner.js";
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export type CreateKnowledgeExtractionDeps = {
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defaultAdapter: AgentFn;
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adapters?: Partial<Record<keyof WorkflowMeta, AgentFn>>;
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extract: LlmExtractorConfig;
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};
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export function createKnowledgeExtractionWorkflow({
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defaultAdapter,
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adapters,
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extract,
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}: CreateKnowledgeExtractionDeps): WorkflowDefinition<WorkflowMeta> {
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const a = (role: keyof WorkflowMeta) => adapters?.[role] ?? defaultAdapter;
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const llmAdapter = createLlmAdapter(extract.provider);
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return {
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name: "knowledge-extraction",
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roles: {
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questioner: createQuestionerRole(adapters?.questioner ?? llmAdapter, { extract }),
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answerer: createAnswererRole(adapters?.answerer ?? llmAdapter, { extract }),
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explorer: createExplorerRole(a("explorer"), { extract }),
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},
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moderator,
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};
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}
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import type { AgentFn, Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { createRole } from "@uncaged/nerve-workflow-utils";
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import { z } from "zod";
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import { resolveWorkdir } from "../lib/workdir.js";
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import type { AnswererMeta } from "./answerer.js";
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import type { QuestionerMeta } from "./questioner.js";
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export const explorerMetaSchema = z.object({
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patches: z.array(
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z.object({
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card: z.string(),
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section: z.string(),
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}),
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),
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new_cards: z.array(z.string()),
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});
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export type ExplorerMeta = z.infer<typeof explorerMetaSchema>;
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export type CreateExplorerRoleDeps = {
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extract: LlmExtractorConfig;
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};
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function lastMeta<M>(messages: WorkflowMessage[], role: string): M | undefined {
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for (let i = messages.length - 1; i >= 0; i--) {
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if (messages[i].role === role) {
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return messages[i].meta as M;
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}
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}
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return undefined;
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}
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export function explorerPrompt(ctx: ThreadContext): string {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const threadId = ctx.start.meta.threadId;
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const qm = lastMeta<QuestionerMeta>(messages, "questioner");
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const am = lastMeta<AnswererMeta>(messages, "answerer");
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const cwd = resolveWorkdir(ctx.start);
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const unanswered =
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am?.results.filter((r) => !r.found).map((r) => r.id) ?? [];
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return `You are the **explorer** in a knowledge-extraction workflow.
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## Context
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- Thread: \`nerve thread ${threadId}\`
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- Working directory (repo root for paths): ${cwd}
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- Current knowledge card (questioner): ${qm?.card ?? "(unknown)"}
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## Unanswered question ids
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${JSON.stringify(unanswered)}
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Use the prior answerer results in the thread to map ids to full question text when you read messages above.
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## Task
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For each unanswered question, **read the codebase** as needed, then either:
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- Add a new markdown file under \`.knowledge/\`, or
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- Patch an existing card (prefer updating the card listed above when appropriate).
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After any write or patch to \`.knowledge\`, run:
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\`\`\`bash
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nerve knowledge sync
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\`\`\`
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from this repo root (${cwd}), and fix failures until sync succeeds.
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## Output meta
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Report \`patches\` as { card, section } entries for cards you edited (section is a short heading or path hint).
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Report \`new_cards\` as repo-relative paths for brand-new files you created (e.g. \`.knowledge/new-topic.md\`).
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Do not claim work you did not perform.`;
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}
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export function createExplorerRole(
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adapter: AgentFn,
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{ extract }: CreateExplorerRoleDeps,
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): Role<ExplorerMeta> {
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return createRole(
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adapter,
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async (ctx: ThreadContext) => explorerPrompt(ctx),
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explorerMetaSchema,
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extract,
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);
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}
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@ -1,108 +0,0 @@
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import { readFile } from "node:fs/promises";
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import { join } from "node:path";
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import type { AgentFn, Role, ThreadContext, WorkflowMessage } from "@uncaged/nerve-core";
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import type { LlmExtractorConfig } from "@uncaged/nerve-workflow-utils";
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import { createRole } from "@uncaged/nerve-workflow-utils";
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import { z } from "zod";
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import { resolveQueueForQuestioner } from "../lib/knowledge-queue.js";
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import { resolveWorkdir } from "../lib/workdir.js";
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const questionerExtractSchema = z.object({
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questions: z
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.array(
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z.object({
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id: z.string(),
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question: z.string(),
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domain: z.string(),
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}),
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)
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.length(5),
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});
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export type QuestionerMeta = {
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/** Empty when no .knowledge cards and no work to do. */
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card: string;
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questions: { id: string; question: string; domain: string }[];
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remaining_queue: string[];
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};
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export type CreateQuestionerRoleDeps = {
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extract: LlmExtractorConfig;
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};
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function questionerSystem(): string {
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return `You are the **questioner** in a knowledge-extraction workflow.
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Read the given markdown knowledge card. Propose exactly **five** technical questions that are **not** already answered or covered by that card.
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Rules:
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- Questions must be concrete and technical.
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- Each question needs a stable string id (e.g. q1, q2, q3, q4, q5), a short domain label (e.g. routing, storage), and the question text.
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- Do not assume access to other files or tools — reason only from the card content shown.`;
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}
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function questionerUser(card: string, cardBody: string, remainingHint: string[]): string {
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return `Current card path: ${card}
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Remaining queue after this card (paths, may be empty): ${JSON.stringify(remainingHint)}
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--- Card content ---
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${cardBody}`;
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}
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export async function questionerPrompt(ctx: ThreadContext): Promise<string> {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const cwd = resolveWorkdir(ctx.start);
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const queue = await resolveQueueForQuestioner(ctx.start, messages, cwd);
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if (queue.length === 0) {
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throw new Error(
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"questioner: prompt invoked with empty queue — wrapped role should short-circuit before LLM",
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);
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}
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const card = queue[0]!;
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const remaining_queue = queue.slice(1);
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let cardBody: string;
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try {
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cardBody = await readFile(join(cwd, card), "utf8");
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} catch (e) {
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const msg = e instanceof Error ? e.message : String(e);
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throw new Error(`questioner: failed to read ${card}: ${msg}`);
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}
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return `${questionerSystem()}\n\n${questionerUser(card, cardBody, remaining_queue)}`;
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}
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export function createQuestionerRole(adapter: AgentFn, { extract }: CreateQuestionerRoleDeps): Role<QuestionerMeta> {
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const inner = createRole(adapter, questionerPrompt, questionerExtractSchema, extract);
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return async (ctx: ThreadContext) => {
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const messages = ctx.steps as unknown as WorkflowMessage[];
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const cwd = resolveWorkdir(ctx.start);
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const queue = await resolveQueueForQuestioner(ctx.start, messages, cwd);
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if (queue.length === 0) {
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return {
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content:
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"questioner: no `.knowledge` markdown files found and no seed path in the trigger prompt; queue is empty.",
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meta: {
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card: "",
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questions: [],
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remaining_queue: [],
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},
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};
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}
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const card = queue[0]!;
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const remaining_queue = queue.slice(1);
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const r = await inner(ctx);
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return {
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content: r.content,
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meta: {
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card,
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questions: r.meta.questions,
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remaining_queue,
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},
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};
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};
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}
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