P1: AI estimates, retention, templates, Linear, and UI follow-ups

apps/api/src/ai.ts

@@ -12,7 +12,8 @@ export type AiFeature =
   | "bias_lint"
   | "peer_synthesize"
   | "framework_extract"
-  | "meeting_summary";
+  | "meeting_summary"
+  | "skip_level_talking_points";
 
 export type ChatMessage = { role: "system" | "user" | "assistant"; content: string };
 

apps/api/src/aiEstimate.test.ts

@@ -0,0 +1,25 @@
+import assert from "node:assert/strict";
+import { describe, it } from "node:test";
+import { estimateCostUsd, estimateTokensFromText, maxOutputTokensForFeature } from "../src/aiEstimate.js";
+
+describe("ai estimate helpers", () => {
+  it("estimates tokens from text length", () => {
+    assert.equal(estimateTokensFromText("abcd"), 1);
+    assert.ok(estimateTokensFromText("x".repeat(40)) >= 10);
+  });
+
+  it("returns null cost for cursor/ollama", () => {
+    assert.equal(estimateCostUsd({ provider: "cursor", inputTokens: 1000, outputTokens: 1000 }), null);
+    assert.equal(estimateCostUsd({ provider: "ollama", inputTokens: 1000, outputTokens: 1000 }), null);
+  });
+
+  it("estimates anthropic cost", () => {
+    const cost = estimateCostUsd({ provider: "anthropic", inputTokens: 1_000_000, outputTokens: 0 });
+    assert.equal(cost, 3);
+  });
+
+  it("caps output tokens by feature", () => {
+    assert.equal(maxOutputTokensForFeature("framework_extract"), 4096);
+    assert.equal(maxOutputTokensForFeature("evidence_digest"), 1600);
+  });
+});

apps/api/src/aiEstimate.ts

@@ -0,0 +1,74 @@
+import type { AiFeature, ChatMessage } from "./ai.js";
+import { getAiConfig } from "./ai.js";
+
+/** Rough tokenizer: ~4 chars per token for Latin prose. */
+export function estimateTokensFromText(text: string): number {
+  const t = text.trim();
+  if (!t) return 0;
+  return Math.max(1, Math.ceil(t.length / 4));
+}
+
+export function estimateTokensFromMessages(messages: ChatMessage[]): number {
+  return messages.reduce((sum, m) => sum + estimateTokensFromText(m.content) + 4, 0);
+}
+
+/** Static USD rates per 1M tokens (approx public list prices; Cursor/Ollama → null). */
+const RATES: Record<string, { input: number; output: number } | null> = {
+  anthropic: { input: 3, output: 15 },
+  openai: { input: 0.15, output: 0.6 },
+  cursor: null,
+  ollama: null,
+};
+
+export function maxOutputTokensForFeature(feature: string): number {
+  return feature === "framework_extract" ? 4096 : 1600;
+}
+
+export function estimateCostUsd(opts: {
+  provider: string;
+  inputTokens: number;
+  outputTokens: number;
+}): number | null {
+  const rates = RATES[opts.provider] ?? null;
+  if (!rates) return null;
+  return Number(
+    ((opts.inputTokens / 1_000_000) * rates.input + (opts.outputTokens / 1_000_000) * rates.output).toFixed(6),
+  );
+}
+
+export function buildAiEstimate(opts: {
+  feature: string;
+  messages: ChatMessage[];
+  dataClassesSent?: string[];
+}) {
+  const cfg = getAiConfig();
+  const provider =
+    cfg.localOnly || cfg.provider === "ollama" ? "ollama" : (cfg.provider as string);
+  const model =
+    opts.feature === "evidence_digest"
+      ? cfg.modelDigest
+      : cfg.modelDraft || (provider === "cursor" ? "composer-2.5" : "claude-sonnet-4-5");
+  const inputTokensApprox = estimateTokensFromMessages(opts.messages);
+  const outputTokensMax = maxOutputTokensForFeature(opts.feature);
+  const costUsdApprox = estimateCostUsd({
+    provider,
+    inputTokens: inputTokensApprox,
+    outputTokens: outputTokensMax,
+  });
+  return {
+    feature: opts.feature,
+    provider,
+    model: model || "unknown",
+    inputTokensApprox,
+    outputTokensMax,
+    costUsdApprox,
+    localOnly: Boolean(cfg.localOnly || provider === "ollama"),
+    dataClassesSent: opts.dataClassesSent ?? [],
+    note:
+      costUsdApprox == null
+        ? "Token estimate only — Cursor/Ollama pricing is not modeled."
+        : "Approximate list-price estimate; actual bill depends on provider discounts and true tokenizer.",
+  };
+}
+
+export type { AiFeature };

apps/api/src/bias.test.ts

@@ -0,0 +1,25 @@
+import assert from "node:assert/strict";
+import { describe, it } from "node:test";
+import { runBiasToneLint } from "../src/bias.js";
+
+describe("bias tone lint", () => {
+  it("flags absolutist language", () => {
+    const findings = runBiasToneLint("They always ship and never miss.");
+    assert.ok(findings.some((f) => f.id === "absolutes"));
+  });
+
+  it("flags dense superlatives", () => {
+    const findings = runBiasToneLint("A brilliant genius and amazing rockstar ninja.");
+    assert.ok(findings.some((f) => f.id === "praise_density"));
+  });
+
+  it("flags stereotype-risk phrasing", () => {
+    const findings = runBiasToneLint("Can be abrasive and a cultural fit concern in meetings.");
+    assert.ok(findings.some((f) => f.id === "stereotype_risk"));
+  });
+
+  it("notes missing citations", () => {
+    const findings = runBiasToneLint("Solid contributor across the board this cycle.");
+    assert.ok(findings.some((f) => f.id === "no_citations"));
+  });
+});