Add interactive 1:1 sessions with optional AI questions

CHANGELOG.md

@@ -14,6 +14,7 @@ Versions follow [SemVer](https://semver.org/) (`0.1.0-alpha.x` while the public
 - Default **PE Engineering Career Framework** (Graduate → CTO) seeded on empty and demo workspaces
 - **Cursor Cloud Agents** AI provider — use dashboard `crsr_…` keys via the Cloud Agents API (no-repo agents for digests/drafts)
 - Guided **/setup** onboarding: team → assign-first roles → optional AI → integrations with credential guides → evidence backfill with progress
+- **1:1 sessions** on the person dossier — manual Q&A agenda plus optional AI-suggested questions grounded in evidence
 
 ### Changed
 

apps/api/src/ai.ts

@@ -19,7 +19,8 @@ export type AiFeature =
   | "peer_synthesize"
   | "framework_extract"
   | "meeting_summary"
-  | "skip_level_talking_points";
+  | "skip_level_talking_points"
+  | "one_on_one_questions";
 
 export type ChatMessage = { role: "system" | "user" | "assistant"; content: string };
 

apps/api/src/featureRoutes.ts

@@ -120,6 +120,16 @@ export function registerFeatureRoutes(app: Hono<any>, helpers: Helpers) {
             content: `WORKSPACE EVIDENCE (${ctx.scopeLabel}):\n${ctx.lines.join("\n")}\n\nUser question: (preview — actual question sent on confirm)`,
           },
         ];
+      } else if (body.feature === "one_on_one_questions" && body.personId) {
+        const ctx = helpers.buildEvidenceContext(body.personId, body.cycleId);
+        dataClassesSent = ctx.dataClassesSent;
+        messages = [
+          { role: "system", content: buildSystemPrompt() },
+          {
+            role: "user",
+            content: `Suggest 1:1 questions.\n\nEVIDENCE:\n${ctx.lines.join("\n") || "(thin evidence)"}`,
+          },
+        ];
       } else if (body.personId && body.cycleId) {
         const ctx = helpers.buildEvidenceContext(body.personId, body.cycleId);
         dataClassesSent = ctx.dataClassesSent;

apps/api/src/index.ts

@@ -110,6 +110,7 @@ import { extractCitationIds, filterCitationsAgainstAllowlist } from "./citations
 import { runBiasToneLint } from "./bias.js";
 import { mergeLintFindings, runFaithfulnessLint } from "./faithfulness.js";
 import { registerP1Routes } from "./p1Routes.js";
+import { registerOneOnOneRoutes } from "./oneOnOnes.js";
 import { indexReviewFts } from "./reviewSearch.js";
 import { renderPromotionPacketHtml, renderSubjectPacketHtml } from "./packetHtml.js";
 import {
@@ -4692,6 +4693,8 @@ registerP1Routes(app, {
   frameworkInsufficient,
 });
 
+registerOneOnOneRoutes(app, { buildEvidenceContext });
+
 registerFeatureRoutes(app, {
   buildEvidenceContext,
   buildWorkspaceContext,

apps/api/src/oneOnOnes.test.ts

@@ -0,0 +1,27 @@
+import assert from "node:assert/strict";
+import { describe, it } from "node:test";
+import { parseSuggestedQuestions } from "../src/oneOnOnes.js";
+
+describe("parseSuggestedQuestions", () => {
+  it("parses a JSON array of strings", () => {
+    const qs = parseSuggestedQuestions(`Here you go:\n["How is capacity?", "What blocked [ach_1]?"]\n`);
+    assert.deepEqual(qs, ["How is capacity?", "What blocked [ach_1]?"]);
+  });
+
+  it("parses objects with question fields", () => {
+    const qs = parseSuggestedQuestions(
+      JSON.stringify([{ question: "What went well last sprint?" }, { question: "Any risks?" }]),
+    );
+    assert.deepEqual(qs, ["What went well last sprint?", "Any risks?"]);
+  });
+
+  it("falls back to numbered lines with question marks", () => {
+    const qs = parseSuggestedQuestions(`1. How are you feeling about goals?\n2. What support do you need?\nNote: thin evidence`);
+    assert.equal(qs.length, 2);
+    assert.match(qs[0], /feeling about goals/);
+  });
+
+  it("returns empty for blank input", () => {
+    assert.deepEqual(parseSuggestedQuestions("   "), []);
+  });
+});