feat(content): Phase 8 content generation (Faz A+B) for Sase.tr

Hybrid content automation pilot: generation + review + drafts live in the panel (reusing the insight pipeline's DeepSeek client, prompt_templates versioning, cost_ledger and budget_settings); publishing/distribution will go through n8n (Faz C, not built). Channels: blog, LinkedIn, X, Instagram. Topic sourcing is automatic (LLM-generated ideas). Approval model: drafts sit in the panel for manual review/edit/publish. Faz A (worker): - ContentTopic / ContentDraft Prisma models (content_topics, content_drafts) - content-prompts.ts: 5 seed prompts (topic ideas[pro] + blog[pro] + linkedin/x/instagram[flash]), Turkish B2B automotive tone, per-channel JSON schemas - content-budget.ts: separate budget envelope (sums only content_* spend) - content-topics job (auto idea gen, backlog-capped, title dedupe) + content-generate job (queued topic -> one draft per channel) - content-pipeline scheduler (separate BullMQ queue, topics@*/8h, generate@*/10min), wired into index.ts; seeded via seed-runtime - content budget settings (caps + content_paused kill switch); seed default content_paused=true for a safe first deploy Faz B (web): - /content (queue + auto/manual triggers + manual topic form), /content/t/[id] (per-channel draft cards: preview, JSON edits, approve/reject), /content/costs (content-only spend) - server actions (audit-logged), manual trigger API routes, contentQueue(), nav + Cmd+K entries - content caps surfaced on /insights/settings/budgets + whitelisted Both packages typecheck. Schema applies on deploy (web start runs prisma db push). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-24 00:11:05 +03:00
parent 565ad5af59
commit fdce3f6bd0
22 changed files with 1856 additions and 1 deletions
--- a/apps/worker/src/index.ts
+++ b/apps/worker/src/index.ts
@@ -1,6 +1,7 @@
 import { startEventBus } from "./consumers/event-bus";
 import { startScheduledJobs } from "./schedulers/nightly";
 import { startInsightPipeline } from "./schedulers/pipeline";
+import { startContentPipeline } from "./schedulers/content";
 import { upsertSeedData } from "./lib/seed-runtime";
 import { redis } from "./redis";
 import { prisma } from "./db";
@@ -16,6 +17,7 @@ async function main() {

  await startScheduledJobs();
  await startInsightPipeline();
+  await startContentPipeline();
  await startEventBus();

  console.log("[worker] up.");
--- a/apps/worker/src/jobs/content-generate.ts
+++ b/apps/worker/src/jobs/content-generate.ts
@@ -0,0 +1,180 @@
+// content-generate job (Phase 8a): turns queued ContentTopic rows into one
+// ContentDraft per selected channel. Mirrors the insight analyze job:
+// budget guard → per-call DeepSeek → JSON validate → persist + cost ledger.
+// Drafts land in `draft` status; the founder reviews/edits and (Phase 8c)
+// publishes via the n8n webhook. Nothing is published from here.
+
+import { prisma } from "../db";
+import { callDeepSeek, extractJson, type Tier, DeepSeekError } from "../lib/deepseek";
+import { checkContentBudget } from "../lib/content-budget";
+import { channelPromptTag, type ContentChannel } from "../lib/content-prompts";
+import { validate } from "../lib/json-validate";
+
+const PROJECT_KEY = process.env.CONTENT_PROJECT_KEY ?? "sase";
+const GENERATE_BATCH = Number(process.env.CONTENT_GENERATE_BATCH ?? "3");
+
+export type ContentGenerateResult = {
+  topics: number;
+  draftsCreated: number;
+  failed: number;
+  costUsd: number;
+  budgetState: string;
+  reason?: string;
+};
+
+export async function runContentGenerate(): Promise<ContentGenerateResult> {
+  const budget = await checkContentBudget();
+  if (!budget.allow) {
+    return { topics: 0, draftsCreated: 0, failed: 0, costUsd: 0, budgetState: budget.state, reason: budget.reason };
+  }
+
+  const topics = await prisma.contentTopic.findMany({
+    where: { projectKey: PROJECT_KEY, status: "queued" },
+    orderBy: { createdAt: "asc" },
+    take: GENERATE_BATCH,
+  });
+  if (topics.length === 0) {
+    return { topics: 0, draftsCreated: 0, failed: 0, costUsd: 0, budgetState: budget.state };
+  }
+
+  let draftsCreated = 0;
+  let failed = 0;
+  let costUsd = 0;
+
+  for (const topic of topics) {
+    await prisma.contentTopic.update({ where: { id: topic.id }, data: { status: "generating" } });
+
+    const channels = (topic.channels as string[]).filter((c) =>
+      ["blog", "linkedin", "x", "instagram"].includes(c),
+    ) as ContentChannel[];
+
+    for (const channel of channels) {
+      // Skip if a draft for this topic+channel already exists (idempotent reruns).
+      const existing = await prisma.contentDraft.findFirst({
+        where: { topicId: topic.id, channel },
+        select: { id: true },
+      });
+      if (existing) continue;
+
+      const tag = channelPromptTag(channel);
+      const template = await prisma.promptTemplate.findFirst({
+        where: { tag, active: true },
+        orderBy: { version: "desc" },
+      });
+      if (!template) {
+        console.warn(`[content-generate] no prompt for ${tag}`);
+        continue;
+      }
+
+      const tier: Tier = budget.forceTier ?? (template.modelTier as Tier);
+      const userPrompt = template.userPromptTemplate
+        .replace("{{title}}", topic.title)
+        .replace("{{brief}}", topic.brief ?? "")
+        .replace("{{angle}}", topic.angle ?? "(belirtilmedi)")
+        .replace("{{keywords}}", (topic.keywords as string[]).join(", ") || "(yok)");
+
+      let result;
+      try {
+        result = await callDeepSeek({
+          tier,
+          systemPrompt: template.systemPrompt,
+          userPrompt,
+          maxOutputTokens: template.maxOutputTokens,
+          temperature: template.temperature,
+        });
+      } catch (e) {
+        const status = e instanceof DeepSeekError ? e.status : 0;
+        console.warn(`[content-generate] deepseek error topic=${topic.id} ${channel}: ${status}`);
+        await logCost({ tag, tier, promptVersion: template.version, errorCode: `${status}` });
+        failed++;
+        continue;
+      }
+
+      costUsd += result.cost.totalUsd;
+
+      let parsed: any;
+      let validationErrors = "";
+      try {
+        parsed = JSON.parse(extractJson(result.text));
+        const errs = validate(parsed, template.outputSchemaJson as any);
+        if (errs.length) validationErrors = errs.map((e) => `${e.path}: ${e.message}`).join("; ");
+      } catch (e) {
+        validationErrors = `json parse: ${(e as Error).message}`;
+      }
+
+      await logCost({
+        tag,
+        tier,
+        promptVersion: template.version,
+        usage: result.usage,
+        cost: result.cost,
+        model: result.model,
+        durationMs: result.durationMs,
+        errorCode: validationErrors ? "validation_failed" : undefined,
+      });
+
+      if (validationErrors) {
+        console.warn(`[content-generate] validation failed topic=${topic.id} ${channel}: ${validationErrors.slice(0, 160)}`);
+        failed++;
+        continue;
+      }
+
+      await prisma.contentDraft.create({
+        data: {
+          topicId: topic.id,
+          projectKey: PROJECT_KEY,
+          channel,
+          status: "draft",
+          bodyJson: parsed,
+          sourcePromptTag: tag,
+          sourcePromptVersion: template.version,
+          sourceModel: result.model,
+          sourceCostUsd: result.cost.totalUsd,
+        },
+      });
+      draftsCreated++;
+    }
+
+    await prisma.contentTopic.update({ where: { id: topic.id }, data: { status: "drafted" } });
+
+    const recheck = await checkContentBudget();
+    if (!recheck.allow) {
+      console.log(`[content-generate] budget exhausted mid-batch (${recheck.state})`);
+      break;
+    }
+  }
+
+  return { topics: topics.length, draftsCreated, failed, costUsd, budgetState: budget.state };
+}
+
+async function logCost(opts: {
+  tag: string;
+  tier: Tier;
+  promptVersion: number;
+  usage?: { inputTokensMiss: number; inputTokensHit: number; outputTokens: number };
+  cost?: { inputMissUsd: number; inputHitUsd: number; outputUsd: number; totalUsd: number; cacheHitRatio: number };
+  model?: string;
+  durationMs?: number;
+  errorCode?: string;
+}): Promise<void> {
+  await prisma.costLedger.create({
+    data: {
+      projectKey: PROJECT_KEY,
+      promptTag: opts.tag,
+      promptVersion: opts.promptVersion,
+      provider: "deepseek",
+      model: opts.model ?? (opts.tier === "pro" ? "deepseek-v4-pro" : "deepseek-v4-flash"),
+      tier: opts.tier,
+      tokensInputCacheMiss: opts.usage?.inputTokensMiss ?? 0,
+      tokensInputCacheHit: opts.usage?.inputTokensHit ?? 0,
+      tokensOutput: opts.usage?.outputTokens ?? 0,
+      costInputCacheMissUsd: opts.cost?.inputMissUsd ?? 0,
+      costInputCacheHitUsd: opts.cost?.inputHitUsd ?? 0,
+      costOutputUsd: opts.cost?.outputUsd ?? 0,
+      costTotalUsd: opts.cost?.totalUsd ?? 0,
+      cacheHitRatio: opts.cost?.cacheHitRatio ?? 0,
+      callDurationMs: opts.durationMs ?? null,
+      errorCode: opts.errorCode ?? null,
+    },
+  });
+}
--- a/apps/worker/src/jobs/content-topics.ts
+++ b/apps/worker/src/jobs/content-topics.ts
@@ -0,0 +1,204 @@
+// content-topics job (Phase 8a): auto-generates content topic ideas via the
+// LLM and queues them as ContentTopic rows. Conservative by design — it only
+// tops the backlog up to a target, dedupes near-identical titles, and is
+// gated by the separate content budget.
+
+import { prisma } from "../db";
+import { callDeepSeek, extractJson, type Tier, DeepSeekError } from "../lib/deepseek";
+import { checkContentBudget } from "../lib/content-budget";
+import { validate } from "../lib/json-validate";
+import { fingerprintHash } from "../lib/hash";
+
+const PROJECT_KEY = process.env.CONTENT_PROJECT_KEY ?? "sase";
+// Stop generating new ideas once this many topics are already waiting.
+const BACKLOG_TARGET = Number(process.env.CONTENT_BACKLOG_TARGET ?? "12");
+const DEFAULT_CHANNELS = (process.env.CONTENT_DEFAULT_CHANNELS ?? "blog,linkedin,x,instagram")
+  .split(",")
+  .map((c) => c.trim())
+  .filter(Boolean);
+
+type TopicIdea = {
+  title: string;
+  brief: string;
+  angle?: string;
+  channels?: string[];
+  keywords?: string[];
+};
+
+export type ContentTopicsResult = {
+  generated: number;
+  inserted: number;
+  skipped: number;
+  costUsd: number;
+  budgetState: string;
+  reason?: string;
+};
+
+export async function runContentTopics(): Promise<ContentTopicsResult> {
+  const budget = await checkContentBudget();
+  if (!budget.allow) {
+    return { generated: 0, inserted: 0, skipped: 0, costUsd: 0, budgetState: budget.state, reason: budget.reason };
+  }
+
+  // Only top up the backlog — don't generate endlessly.
+  const queuedCount = await prisma.contentTopic.count({
+    where: { projectKey: PROJECT_KEY, status: { in: ["queued", "generating"] } },
+  });
+  if (queuedCount >= BACKLOG_TARGET) {
+    return { generated: 0, inserted: 0, skipped: 0, budgetState: budget.state, costUsd: 0, reason: "backlog_full" };
+  }
+  const want = Math.min(6, BACKLOG_TARGET - queuedCount);
+
+  const template = await prisma.promptTemplate.findFirst({
+    where: { tag: "content_topic_ideas", active: true },
+    orderBy: { version: "desc" },
+  });
+  if (!template) {
+    return { generated: 0, inserted: 0, skipped: 0, budgetState: budget.state, costUsd: 0, reason: "no_prompt" };
+  }
+
+  // Recent topics so the model avoids repeating itself.
+  const recent = await prisma.contentTopic.findMany({
+    where: { projectKey: PROJECT_KEY },
+    select: { title: true },
+    orderBy: { createdAt: "desc" },
+    take: 40,
+  });
+  const context =
+    recent.length > 0
+      ? `Son üretilen konular (BUNLARA BENZER üretme):\n${recent.map((r) => `- ${r.title}`).join("\n")}`
+      : "Henüz üretilmiş konu yok.";
+
+  const tier: Tier = budget.forceTier ?? (template.modelTier as Tier);
+  const userPrompt = template.userPromptTemplate
+    .replace("{{context}}", context)
+    .replace("{{count}}", String(want));
+
+  let result;
+  try {
+    result = await callDeepSeek({
+      tier,
+      systemPrompt: template.systemPrompt,
+      userPrompt,
+      maxOutputTokens: template.maxOutputTokens,
+      temperature: template.temperature,
+    });
+  } catch (e) {
+    const status = e instanceof DeepSeekError ? e.status : 0;
+    await logCost({ tier, promptVersion: template.version, errorCode: `${status}` });
+    return {
+      generated: 0,
+      inserted: 0,
+      skipped: 0,
+      budgetState: budget.state,
+      costUsd: 0,
+      reason: `deepseek ${status}: ${(e as Error).message}`,
+    };
+  }
+
+  await logCost({
+    tier,
+    promptVersion: template.version,
+    usage: result.usage,
+    cost: result.cost,
+    model: result.model,
+    durationMs: result.durationMs,
+  });
+
+  let parsed: any;
+  try {
+    parsed = JSON.parse(extractJson(result.text));
+  } catch (e) {
+    return {
+      generated: 0,
+      inserted: 0,
+      skipped: 0,
+      budgetState: budget.state,
+      costUsd: result.cost.totalUsd,
+      reason: `json parse: ${(e as Error).message}`,
+    };
+  }
+  const errs = validate(parsed, template.outputSchemaJson as any);
+  if (errs.length) {
+    return {
+      generated: 0,
+      inserted: 0,
+      skipped: 0,
+      budgetState: budget.state,
+      costUsd: result.cost.totalUsd,
+      reason: `validation: ${errs.map((e) => e.path).join(",").slice(0, 120)}`,
+    };
+  }
+
+  const ideas: TopicIdea[] = Array.isArray(parsed.topics) ? parsed.topics : [];
+  let inserted = 0;
+  let skipped = 0;
+  for (const idea of ideas) {
+    const fp = fingerprintHash([PROJECT_KEY, idea.title]);
+    const dupe = await prisma.contentTopic.findFirst({ where: { projectKey: PROJECT_KEY, fingerprint: fp } });
+    if (dupe) {
+      skipped++;
+      continue;
+    }
+    const channels = (idea.channels && idea.channels.length ? idea.channels : DEFAULT_CHANNELS).filter((c) =>
+      ["blog", "linkedin", "x", "instagram"].includes(c),
+    );
+    await prisma.contentTopic.create({
+      data: {
+        projectKey: PROJECT_KEY,
+        title: idea.title.slice(0, 250),
+        brief: idea.brief ?? "",
+        angle: idea.angle ?? null,
+        channels: channels.length ? channels : DEFAULT_CHANNELS,
+        keywords: Array.isArray(idea.keywords) ? idea.keywords.slice(0, 12) : [],
+        status: "queued",
+        source: "auto",
+        fingerprint: fp,
+        sourcePromptTag: "content_topic_ideas",
+        sourcePromptVersion: template.version,
+        sourceModel: result.model,
+        sourceCostUsd: result.cost.totalUsd / Math.max(1, ideas.length),
+      },
+    });
+    inserted++;
+  }
+
+  return {
+    generated: ideas.length,
+    inserted,
+    skipped,
+    budgetState: budget.state,
+    costUsd: result.cost.totalUsd,
+  };
+}
+
+async function logCost(opts: {
+  tier: Tier;
+  promptVersion: number;
+  usage?: { inputTokensMiss: number; inputTokensHit: number; outputTokens: number };
+  cost?: { inputMissUsd: number; inputHitUsd: number; outputUsd: number; totalUsd: number; cacheHitRatio: number };
+  model?: string;
+  durationMs?: number;
+  errorCode?: string;
+}): Promise<void> {
+  await prisma.costLedger.create({
+    data: {
+      projectKey: PROJECT_KEY,
+      promptTag: "content_topic_ideas",
+      promptVersion: opts.promptVersion,
+      provider: "deepseek",
+      model: opts.model ?? (opts.tier === "pro" ? "deepseek-v4-pro" : "deepseek-v4-flash"),
+      tier: opts.tier,
+      tokensInputCacheMiss: opts.usage?.inputTokensMiss ?? 0,
+      tokensInputCacheHit: opts.usage?.inputTokensHit ?? 0,
+      tokensOutput: opts.usage?.outputTokens ?? 0,
+      costInputCacheMissUsd: opts.cost?.inputMissUsd ?? 0,
+      costInputCacheHitUsd: opts.cost?.inputHitUsd ?? 0,
+      costOutputUsd: opts.cost?.outputUsd ?? 0,
+      costTotalUsd: opts.cost?.totalUsd ?? 0,
+      cacheHitRatio: opts.cost?.cacheHitRatio ?? 0,
+      callDurationMs: opts.durationMs ?? null,
+      errorCode: opts.errorCode ?? null,
+    },
+  });
+}
--- a/apps/worker/src/lib/content-budget.ts
+++ b/apps/worker/src/lib/content-budget.ts
@@ -0,0 +1,116 @@
+// Content-generation budget guard. Mirrors lib/budget.ts but keeps a separate
+// envelope from the insight pipeline: spend is summed only over cost_ledger
+// rows whose promptTag starts with "content_", and the caps come from the
+// `content_*` budget settings. This way content generation can never exhaust
+// the insight analysis budget (or vice-versa).
+
+import { prisma } from "../db";
+import type { BudgetState } from "./budget";
+
+export type ContentBudgetDecision = {
+  allow: boolean;
+  state: BudgetState;
+  reason: string;
+  forceTier?: "flash";
+  todayUsd: number;
+  monthUsd: number;
+  limits: {
+    monthlyHardCap: number;
+    dailySoftCap: number;
+    dailyHardCap: number;
+    perCallMax: number;
+  };
+};
+
+async function getNumber(key: string, fallback: number): Promise<number> {
+  const row = await prisma.budgetSetting.findFirst({
+    where: { projectKey: null, settingKey: key },
+  });
+  const v = row?.settingValue;
+  return typeof v === "number" ? v : fallback;
+}
+
+async function getBool(key: string, fallback: boolean): Promise<boolean> {
+  const row = await prisma.budgetSetting.findFirst({
+    where: { projectKey: null, settingKey: key },
+  });
+  const v = row?.settingValue;
+  return typeof v === "boolean" ? v : fallback;
+}
+
+function startOfDayUtc(d: Date): Date {
+  return new Date(Date.UTC(d.getUTCFullYear(), d.getUTCMonth(), d.getUTCDate()));
+}
+function startOfMonthUtc(d: Date): Date {
+  return new Date(Date.UTC(d.getUTCFullYear(), d.getUTCMonth(), 1));
+}
+
+// Sums only content_* spend.
+async function contentSpend(since: Date): Promise<number> {
+  const agg = await prisma.costLedger.aggregate({
+    where: { createdAt: { gte: since }, promptTag: { startsWith: "content_" } },
+    _sum: { costTotalUsd: true },
+  });
+  return Number(agg._sum.costTotalUsd ?? 0);
+}
+
+export async function checkContentBudget(): Promise<ContentBudgetDecision> {
+  const monthlyHardCap = await getNumber("content_monthly_hard_cap_usd", 15);
+  const dailySoftCap = await getNumber("content_daily_soft_cap_usd", 1);
+  const dailyHardCap = await getNumber("content_daily_hard_cap_usd", 2);
+  const perCallMax = await getNumber("content_per_call_max_usd", 0.3);
+  const paused = await getBool("content_paused", false);
+
+  const limits = { monthlyHardCap, dailySoftCap, dailyHardCap, perCallMax };
+
+  if (paused) {
+    return {
+      allow: false,
+      state: "hard_paused",
+      reason: "content_paused setting is true",
+      todayUsd: 0,
+      monthUsd: 0,
+      limits,
+    };
+  }
+
+  const now = new Date();
+  const [todayUsd, monthUsd] = await Promise.all([
+    contentSpend(startOfDayUtc(now)),
+    contentSpend(startOfMonthUtc(now)),
+  ]);
+
+  if (monthUsd >= monthlyHardCap) {
+    return {
+      allow: false,
+      state: "monthly_paused",
+      reason: `content month spend $${monthUsd.toFixed(4)} >= monthly cap $${monthlyHardCap}`,
+      todayUsd,
+      monthUsd,
+      limits,
+    };
+  }
+  if (todayUsd >= dailyHardCap) {
+    return {
+      allow: false,
+      state: "hard_paused",
+      reason: `content today spend $${todayUsd.toFixed(4)} >= daily hard cap $${dailyHardCap}`,
+      todayUsd,
+      monthUsd,
+      limits,
+    };
+  }
+  if (todayUsd >= dailySoftCap) {
+    return {
+      allow: true,
+      state: "soft_throttled",
+      reason: `content today spend $${todayUsd.toFixed(4)} >= daily soft cap $${dailySoftCap}, force flash`,
+      forceTier: "flash",
+      todayUsd,
+      monthUsd,
+      limits,
+    };
+  }
+
+  return { allow: true, state: "active", reason: "ok", todayUsd, monthUsd, limits };
+}
--- a/apps/worker/src/lib/content-prompts.ts
+++ b/apps/worker/src/lib/content-prompts.ts
@@ -0,0 +1,216 @@
+// Content-generation prompt templates (Phase 8a). Same shape & DB table as
+// the insight prompts (PromptTemplate) — distinguished by the `content_*` tag
+// prefix, which is also how content spend is separated in the cost ledger.
+//
+// One topic-idea prompt (auto topic generation) + one prompt per channel.
+// All natural-language output is Turkish (B2B automotive marketing tone).
+
+import type { PromptTemplate } from "./prompts";
+
+export const CONTENT_TAGS = [
+  "content_topic_ideas",
+  "content_blog",
+  "content_linkedin",
+  "content_x",
+  "content_instagram",
+] as const;
+
+export type ContentChannel = "blog" | "linkedin" | "x" | "instagram";
+
+// Maps a channel to its generation prompt tag.
+export function channelPromptTag(channel: ContentChannel): string {
+  return `content_${channel}`;
+}
+
+const SASE_BRAND = `Sase.tr hakkında:
+- B2B SaaS: VIN/şasi sorgulama + OEM yedek parça uyumluluğu. Hedef kitle: Türkiye'deki yedek parçacılar, oto servisleri, tamirhaneler, parça ithalatçıları.
+- Değer önerisi: doğru parçayı VIN'den hızlı bul, yanlış parça iadesini azalt, 4 upstream katalog (PL24/Partslink24, PCAT, RMEX, TecDoc) tek arayüzde.
+- Abonelik: starter / brand_specific / full. Deneme akışı var.
+
+Marka tonu:
+- Profesyonel, net, sektörün dilini bilen. Esnaf/teknisyen okuyucuya saygılı, abartısız.
+- Otomotiv terimlerini doğru kullan: VIN, şasi no, OEM, OE/eşdeğer parça, OBD, motor kodu, donanım kodu, katalog, çapraz referans.
+- Satış baskısı değil; gerçek bir sorunu çözerek güven kur. CTA yumuşak ama net (örn. "Sase.tr'de VIN ile parça aramayı ücretsiz deneyin").
+- Yanlış/uydurma teknik iddia YOK. Emin değilsen genel konuş, spesifik sayı/iddia uydurma.
+
+Çıktı kuralları:
+- SADECE şemaya uyan geçerli JSON döndür. Markdown yok, kod bloğu yok, açıklama yok.
+- Tüm doğal dil alanları (başlık, gövde, caption, brief, CTA, vb.) TÜRKÇE. Hashtag'ler Türkçe veya sektör-standart İngilizce olabilir (örn. #yedekparça #OEM).
+- Alan uzunluk sınırlarına (maxLength) uy; aşma, gerekirse kısalt.`;
+
+// ---- Topic idea generation ----
+const TOPIC_IDEAS_SCHEMA = {
+  type: "object",
+  required: ["topics"],
+  properties: {
+    topics: {
+      type: "array",
+      minItems: 1,
+      maxItems: 8,
+      items: {
+        type: "object",
+        required: ["title", "brief", "channels", "keywords"],
+        properties: {
+          title: { type: "string", maxLength: 160 },
+          brief: { type: "string", maxLength: 600 },
+          angle: { type: "string", maxLength: 300 },
+          channels: {
+            type: "array",
+            minItems: 1,
+            maxItems: 4,
+            items: { enum: ["blog", "linkedin", "x", "instagram"] },
+          },
+          keywords: { type: "array", minItems: 1, maxItems: 12, items: { type: "string" } },
+        },
+      },
+    },
+  },
+};
+
+// ---- Per-channel content schemas ----
+const BLOG_SCHEMA = {
+  type: "object",
+  required: ["title", "slug", "meta_description", "body_markdown", "tags"],
+  properties: {
+    title: { type: "string", maxLength: 160 },
+    slug: { type: "string", maxLength: 120 },
+    meta_description: { type: "string", maxLength: 300 },
+    body_markdown: { type: "string", maxLength: 14000 },
+    tags: { type: "array", minItems: 1, maxItems: 12, items: { type: "string" } },
+    cta: { type: "string", maxLength: 300 },
+  },
+};
+
+const LINKEDIN_SCHEMA = {
+  type: "object",
+  required: ["body", "hashtags"],
+  properties: {
+    body: { type: "string", maxLength: 2600 },
+    hashtags: { type: "array", minItems: 0, maxItems: 10, items: { type: "string" } },
+    cta: { type: "string", maxLength: 200 },
+  },
+};
+
+const X_SCHEMA = {
+  type: "object",
+  required: ["tweets"],
+  properties: {
+    tweets: { type: "array", minItems: 1, maxItems: 8, items: { type: "string", maxLength: 280 } },
+    hashtags: { type: "array", minItems: 0, maxItems: 6, items: { type: "string" } },
+  },
+};
+
+const INSTAGRAM_SCHEMA = {
+  type: "object",
+  required: ["caption", "hashtags"],
+  properties: {
+    caption: { type: "string", maxLength: 2200 },
+    hashtags: { type: "array", minItems: 0, maxItems: 30, items: { type: "string" } },
+    image_prompt: { type: "string", maxLength: 400 },
+  },
+};
+
+export const CONTENT_SEED_PROMPTS: PromptTemplate[] = [
+  {
+    tag: "content_topic_ideas",
+    version: 1,
+    name: "Content Topic Ideas v1 (TR)",
+    systemPrompt: `Sase.tr için içerik konusu fikirleri üreten bir B2B içerik stratejistisin. VIN/OEM/yedek parça/oto servis temalarında, hedef kitlenin (yedek parçacılar, servisler) gerçekten arayacağı veya faydalanacağı, SEO ve sosyal için uygun konular öner. Tekrara düşme, jenerik olma; sektöre özgü ve eyleme dönüştürülebilir açılar bul.
+
+${SASE_BRAND}
+
+Şema (tam olarak buna uy):
+${JSON.stringify(TOPIC_IDEAS_SCHEMA)}`,
+    userPromptTemplate: `{{context}}
+
+{{count}} adet yeni içerik konusu fikri üret. Her fikir için: başlık, kısa brief (içeriğin ne anlatacağı), açı (angle), uygun kanallar ve anahtar kelimeler. Yukarıdaki "son konular" listesindekilere benzer/çakışan konu ÜRETME. Şemaya uygun JSON döndür.`,
+    outputSchemaJson: TOPIC_IDEAS_SCHEMA,
+    modelTier: "pro",
+    maxOutputTokens: 1800,
+    temperature: 0.7,
+  },
+  {
+    tag: "content_blog",
+    version: 1,
+    name: "Content Blog v1 (TR, SEO)",
+    systemPrompt: `Sase.tr için Türkçe, SEO-optimize blog yazıları yazan bir içerik editörüsün. Yazı yapısı net (giriş, alt başlıklar, sonuç), okunabilir, gerçek değer veren ve anahtar kelimeleri doğal kullanan olmalı. Markdown gövdesinde başlıklar (##), kısa paragraflar ve gerektiğinde liste kullan.
+
+${SASE_BRAND}
+
+Şema:
+${JSON.stringify(BLOG_SCHEMA)}`,
+    userPromptTemplate: `Konu: {{title}}
+Brief: {{brief}}
+Açı: {{angle}}
+Anahtar kelimeler: {{keywords}}
+
+Bu konuda Türkçe bir blog yazısı üret. slug kısa ve URL-uyumlu (küçük harf, tireli, Türkçe karakter yok). meta_description SEO için 150-160 karakter civarı. Şemaya uygun JSON döndür.`,
+    outputSchemaJson: BLOG_SCHEMA,
+    modelTier: "pro",
+    maxOutputTokens: 4000,
+    temperature: 0.4,
+  },
+  {
+    tag: "content_linkedin",
+    version: 1,
+    name: "Content LinkedIn v1 (TR, B2B)",
+    systemPrompt: `Sase.tr için LinkedIn şirket sayfası gönderileri yazan bir B2B sosyal medya editörüsün. Ton profesyonel ama insani; ilk satır dikkat çeken bir kanca olmalı. Kısa paragraflar, gerektiğinde satır araları. Aşırı hashtag kullanma.
+
+${SASE_BRAND}
+
+Şema:
+${JSON.stringify(LINKEDIN_SCHEMA)}`,
+    userPromptTemplate: `Konu: {{title}}
+Brief: {{brief}}
+Açı: {{angle}}
+Anahtar kelimeler: {{keywords}}
+
+Bu konuda bir LinkedIn gönderisi üret. Şemaya uygun JSON döndür.`,
+    outputSchemaJson: LINKEDIN_SCHEMA,
+    modelTier: "flash",
+    maxOutputTokens: 1200,
+    temperature: 0.5,
+  },
+  {
+    tag: "content_x",
+    version: 1,
+    name: "Content X/Twitter v1 (TR)",
+    systemPrompt: `Sase.tr için X (Twitter) gönderileri/thread'leri yazan bir sosyal medya editörüsün. Her tweet ≤280 karakter. Tek güçlü gönderi ya da kısa bir thread üret; ilk tweet kanca olmalı, son tweet yumuşak CTA içerebilir.
+
+${SASE_BRAND}
+
+Şema:
+${JSON.stringify(X_SCHEMA)}`,
+    userPromptTemplate: `Konu: {{title}}
+Brief: {{brief}}
+Açı: {{angle}}
+Anahtar kelimeler: {{keywords}}
+
+Bu konuda bir X gönderisi ya da kısa thread üret (en fazla 8 tweet). Her tweet ayrı bir dizi elemanı, her biri ≤280 karakter. Şemaya uygun JSON döndür.`,
+    outputSchemaJson: X_SCHEMA,
+    modelTier: "flash",
+    maxOutputTokens: 1000,
+    temperature: 0.6,
+  },
+  {
+    tag: "content_instagram",
+    version: 1,
+    name: "Content Instagram v1 (TR)",
+    systemPrompt: `Sase.tr için Instagram caption'ları yazan bir sosyal medya editörüsün. Caption ilgi çekici, kısa paragraflı, emoji'yi ölçülü kullanan olsun. Hashtag'leri caption sonunda topla. Ayrıca içeriğe uygun bir görsel üretim prompt'u (image_prompt) öner (İngilizce, kısa, görsel betimleme).
+
+${SASE_BRAND}
+
+Şema:
+${JSON.stringify(INSTAGRAM_SCHEMA)}`,
+    userPromptTemplate: `Konu: {{title}}
+Brief: {{brief}}
+Açı: {{angle}}
+Anahtar kelimeler: {{keywords}}
+
+Bu konuda bir Instagram caption'ı + hashtag seti + image_prompt üret. Şemaya uygun JSON döndür.`,
+    outputSchemaJson: INSTAGRAM_SCHEMA,
+    modelTier: "flash",
+    maxOutputTokens: 1000,
+    temperature: 0.6,
+  },
+];
--- a/apps/worker/src/lib/seed-runtime.ts
+++ b/apps/worker/src/lib/seed-runtime.ts
@@ -1,5 +1,6 @@
 import { prisma } from "../db";
 import { SEED_PROMPTS } from "./prompts";
+import { CONTENT_SEED_PROMPTS } from "./content-prompts";

 const DEFAULT_BUDGETS: Array<{ key: string; value: unknown }> = [
  { key: "monthly_hard_cap_usd", value: 30 },
@@ -9,12 +10,20 @@ const DEFAULT_BUDGETS: Array<{ key: string; value: unknown }> = [
  { key: "min_score_for_analysis", value: 30 },
  { key: "cache_ttl_hours", value: 6 },
  { key: "analysis_paused", value: false },
+  // Content generation — separate envelope from insight analysis.
+  { key: "content_monthly_hard_cap_usd", value: 15 },
+  { key: "content_daily_soft_cap_usd", value: 1 },
+  { key: "content_daily_hard_cap_usd", value: 2 },
+  { key: "content_per_call_max_usd", value: 0.3 },
+  // Safe default: first deploy lands paused so output quality can be reviewed
+  // before the cron auto-spends. Flip off in budget settings to enable.
+  { key: "content_paused", value: true },
 ];

 export async function upsertSeedData(): Promise<void> {
  // Prompt templates — insert new versions if (tag, version) doesn't exist.
  // When inserting a new version, deactivate older active versions of the same tag.
-  for (const p of SEED_PROMPTS) {
+  for (const p of [...SEED_PROMPTS, ...CONTENT_SEED_PROMPTS]) {
    const existing = await prisma.promptTemplate.findUnique({
      where: { tag_version: { tag: p.tag, version: p.version } },
    });
--- a/apps/worker/src/schedulers/content.ts
+++ b/apps/worker/src/schedulers/content.ts
@@ -0,0 +1,59 @@
+// Content pipeline scheduler (Phase 8a). Separate BullMQ queue from the
+// insight pipeline so the two domains don't share concurrency or job names.
+// Cadence is conservative and every job is gated by the content budget; the
+// `content_paused` budget setting is the global kill-switch.
+
+import { Queue, Worker, type Job } from "bullmq";
+import { redis } from "../redis";
+import { runContentTopics } from "../jobs/content-topics";
+import { runContentGenerate } from "../jobs/content-generate";
+
+const QUEUE = "content-pipeline";
+
+const queue = new Queue(QUEUE, { connection: redis });
+
+async function runJob(job: Job) {
+  switch (job.name) {
+    case "content-topics": {
+      const res = await runContentTopics();
+      if (res.inserted > 0 || res.reason) {
+        console.log(
+          `[content] topics generated=${res.generated} inserted=${res.inserted} skipped=${res.skipped} cost=$${res.costUsd.toFixed(4)} budget=${res.budgetState}${res.reason ? ` reason=${res.reason}` : ""}`,
+        );
+      }
+      return res;
+    }
+    case "content-generate": {
+      const res = await runContentGenerate();
+      if (res.draftsCreated > 0 || res.failed > 0 || res.reason) {
+        console.log(
+          `[content] generate topics=${res.topics} drafts=${res.draftsCreated} failed=${res.failed} cost=$${res.costUsd.toFixed(4)} budget=${res.budgetState}${res.reason ? ` reason=${res.reason}` : ""}`,
+        );
+      }
+      return res;
+    }
+    default:
+      return { ok: false, error: `unknown job ${job.name}` };
+  }
+}
+
+export async function startContentPipeline() {
+  await queue.upsertJobScheduler(
+    "content-topics",
+    { pattern: "0 */8 * * *" }, // every 8h: top up the topic backlog
+    { name: "content-topics", data: {}, opts: { removeOnComplete: 30, removeOnFail: 15 } },
+  );
+  await queue.upsertJobScheduler(
+    "content-generate",
+    { pattern: "*/10 * * * *" }, // every 10min: drain queued topics into drafts
+    { name: "content-generate", data: {}, opts: { removeOnComplete: 50, removeOnFail: 25 } },
+  );
+
+  new Worker(QUEUE, runJob, {
+    connection: redis,
+    concurrency: 1,
+    lockDuration: 5 * 60_000,
+    stalledInterval: 60_000,
+  });
+  console.log("[content] armed: content-topics@*/8h, content-generate@*/10min");
+}