Hermy HQ: self-hostable mission-control template for your Hermes agent
A Next.js 16 + Prisma/Postgres dashboard that pairs with a local Hermes agent over a Postgres message bus: dispatch work, approve side-effecting actions, browse the agent's memory, and watch it run. Ships with an agent-onboarding prompt (ONBOARDING.md) so your Hermes can install it for you step by step. All secrets are env-configured; nothing sensitive is bundled. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
sharbelxyz
Claude Opus 4.8
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export const maxDuration = 120;
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import { NextRequest, NextResponse } from "next/server";
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import fs from "fs";
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async function callLLM(apiKey: string, apiBase: string, payload: object): Promise<string> {
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const res = await fetch(`${apiBase}/chat/completions`, {
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method: "POST",
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headers: { "Authorization": `Bearer ${apiKey}`, "Content-Type": "application/json" },
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body: JSON.stringify(payload),
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});
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if (!res.ok) {
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const err = await res.text();
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throw new Error(`LLM API error ${res.status}: ${err.slice(0, 200)}`);
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}
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return res.json().then(d => JSON.stringify(d));
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}
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async function braveSearch(query: string, braveKey: string, count = 5): Promise<{ title: string; url: string; description: string }[]> {
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try {
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const res = await fetch(
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`https://api.search.brave.com/res/v1/web/search?q=${encodeURIComponent(query)}&count=${count}`,
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{ headers: { "Accept": "application/json", "X-Subscription-Token": braveKey } }
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);
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if (!res.ok) return [];
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const data = await res.json();
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return (data.web?.results || []).map((r: any) => ({ title: r.title, url: r.url, description: r.description }));
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} catch {
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return [];
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}
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}
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export async function POST(req: NextRequest) {
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const { topic, hook: existingHook, type: contentType } = await req.json();
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if (!topic) return NextResponse.json({ error: "topic required" }, { status: 400 });
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// Use XAI (Grok) — already configured on Vercel. Falls back to OpenAI if set.
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const apiKey = process.env.XAI_API_KEY || process.env.OPENAI_API_KEY || "";
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const apiBase = process.env.XAI_API_KEY ? "https://api.x.ai/v1" : "https://api.openai.com/v1";
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const model = process.env.XAI_API_KEY ? "grok-4-1-fast-non-reasoning" : "gpt-4o-mini";
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const braveKey = process.env.BRAVE_API_KEY || "";
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if (!apiKey) return NextResponse.json({ error: "No API key" }, { status: 500 });
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// Step 1: Web search to gather real context
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let sourceContext = "";
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const sourceUrls: string[] = [];
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if (braveKey) {
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// Generate search queries
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const queryResult = await callLLM(apiKey, apiBase, {
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model,
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messages: [
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{ role: "system", content: "Extract 3 specific search queries to research this video topic thoroughly. Return JSON: {\"queries\":[\"query1\",\"query2\",\"query3\"]}" },
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{ role: "user", content: `Long-form video topic: ${topic}` }
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],
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temperature: 0.3,
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response_format: { type: "json_object" }
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});
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let queries: string[] = [];
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try {
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const qd = JSON.parse(queryResult);
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queries = JSON.parse(qd.choices[0].message.content).queries || [];
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} catch { /* ok */ }
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for (const query of queries.slice(0, 3)) {
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const results = await braveSearch(query, braveKey, 3);
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if (results.length > 0) {
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sourceContext += `\n\n## SEARCH: "${query}"\n${results.map(r => {
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sourceUrls.push(r.url);
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return `- ${r.title} (${r.url})\n ${r.description}`;
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}).join("\n")}`;
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}
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}
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}
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// Step 2: Load voice analysis
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let voiceGuide = "";
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try {
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voiceGuide = fs.readFileSync("./data/longform-voice-analysis.md", "utf-8");
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} catch { /* ok */ }
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// Step 2b: Load feedback history (what the user likes/hates)
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let feedbackContext = "";
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try {
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const drafts = JSON.parse(fs.readFileSync("./data/drafts.json", "utf-8"));
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const rejected = drafts.filter((d: any) => d.feedback?.rating === "down" && d.feedback?.reason);
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const approved = drafts.filter((d: any) => d.status === "approved" || d.feedback?.rating === "up");
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if (rejected.length > 0) {
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feedbackContext += "\n## THINGS THE USER HATES (from rejected drafts):\n";
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for (const r of rejected.slice(0, 15)) {
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feedbackContext += `- "${r.title}" REJECTED because: "${r.feedback.reason}"\n`;
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}
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}
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if (approved.length > 0) {
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feedbackContext += "\n## THINGS THE USER LIKES (approved drafts):\n";
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for (const a of approved.slice(0, 10)) {
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feedbackContext += `- "${a.title}" ✅\n`;
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}
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}
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} catch { /* ok */ }
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// Step 2c: Load YouTube performance data
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let performanceContext = "";
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try {
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const transcripts = JSON.parse(fs.readFileSync("./data/youtube-transcripts.json", "utf-8"));
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const sorted = transcripts.sort((a: any, b: any) => (b.views || 0) - (a.views || 0));
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performanceContext = "\n## TOP PERFORMING YOUTUBE VIDEOS:\n";
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for (const v of sorted.slice(0, 5)) {
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performanceContext += `- "${v.title}" — ${v.views} views\n`;
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}
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performanceContext += "\nThe AI trading/experiment format massively outperforms other content.\n";
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} catch { /* ok */ }
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// Step 2d: Load rejected longform scripts for learning
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let longformFeedback = "";
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try {
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const lfScripts = JSON.parse(fs.readFileSync("./data/longform-scripts.json", "utf-8"));
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const rejectedLf = lfScripts.filter((s: any) => s.status === "rejected" && s.rejectedReason);
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if (rejectedLf.length > 0) {
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longformFeedback = "\n## REJECTED LONGFORM SCRIPTS:\n";
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for (const r of rejectedLf) {
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longformFeedback += `- "${r.title}" REJECTED: "${r.rejectedReason}"\n`;
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}
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}
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} catch { /* ok */ }
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// Step 3: Generate long-form script
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const systemPrompt = `You are writing a long-form YouTube video script for the user, a founder and content creator.
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## THE USER'S LONG-FORM VOICE (30-Transcript Analysis)
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**Narrative Strategy and Performance Optimization:**
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1. **Content Architecture (Statistically Most Engaging):**
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- Personal experiment/challenge format
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- Clear, quantifiable goal
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- Day-by-day progression
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- Unexpected insights
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- Data-driven conclusions
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- Vulnerability in sharing results
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2. **Voice Characteristics:**
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- "Curious explorer" — NOT a lecturer
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- Learning WITH the audience, not AT them
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- Comfortable admitting initial ignorance
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- Technical depth without academic language
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3. **Opening Hook Hierarchy (By Performance):**
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- "I was doing X when I noticed Y"
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- "Everyone says Z. So I decided to test it."
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- "I kept seeing something and realized I didn't understand it."
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4. **Linguistic Performance Markers:**
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- Primary transition: "So,"
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- Secondary transitions:
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* "And here's the thing"
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* "Which made me realize something"
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* "See,"
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- Conversational fillers: "like," "I mean," "honestly"
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5. **Humor Guidelines:**
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- Dry, self-deprecating
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- Observational comedy
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- Highlight absurdity without forcing jokes
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- Meta-commentary on process
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6. **Content Structure:**
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1. Personal discovery moment
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2. Clear investigation goal
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3. Systematic exploration
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4. Unexpected insights
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5. Broader implications
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6. Soft, organic conclusion
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7. **Language Restrictions:**
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- No em dashes
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- Avoid academic vocabulary
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- No exaggerated YouTuber language
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- Minimal CTAs
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- Prioritize genuine curiosity
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8. **High-Performance Topic Types:**
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- Technology disruption
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- Personal experiments
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- Demystifying complex topics
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- Unexpected perspectives on trends
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9. **Sentence Mechanics:**
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- Mix technical explanation + conversational reaction
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- Short, punchy sentences
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- Rarely academic
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- Strategic emphatic words: "literally," "impressive"
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10. **Fundamental Principle:**
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Sound like a smart, curious friend figuring something out — NOT an expert lecturing.
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## Performance Correlation Insights
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- Experiment/challenge videos generate 3-5x more engagement
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- Personal vulnerability increases viewer retention
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- Unexpected topic approaches drive curiosity
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- Technical topics made accessible through storytelling
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**Absolute Rule:** Authenticity trumps polish. Always.
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${voiceGuide ? `## FULL VOICE ANALYSIS REFERENCE\n${voiceGuide.slice(0, 3000)}` : ""}
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${feedbackContext}
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${performanceContext}
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${longformFeedback}
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${existingHook ? `## EXISTING HOOK (keep or improve):\n"${existingHook}"\n` : ""}
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## SOURCE MATERIAL (verified):
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${sourceContext || "No sources — be cautious with claims, hedge everything."}
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## SCRIPT FORMAT
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Write a FULL script in markdown with:
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- Clear section headers (## HOOK, ## PART 1: ..., etc.)
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- Stage directions in *[brackets]* for B-roll/screen recordings
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- ALL dialogue written as SPOKEN WORD, exactly how the user would say it on camera
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- Target: 8-15 minutes when read aloud (~1500-2500 words)
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- Use their actual patterns: "So, I decided to look into it just to understand it." / "And here's where it gets interesting."
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## OUTPUT FORMAT — valid JSON:
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{
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"title": "Compelling YouTube title",
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"hook": "The first 2-3 sentences that stop the scroll — personal, narrative, curiosity-driven",
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"outline": "Section1 → Section2 → Section3 → ...",
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"targetLength": "X-Y min",
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"fullScript": "Full markdown script with all sections, stage directions, and dialogue",
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"factCheck": {
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"status": "✅",
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"verified": ["list of verified claims"],
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"issues": ["any issues found"]
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}
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}`;
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const genResult = await callLLM(apiKey, apiBase, {
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model,
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messages: [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: `Write a full long-form video script about: ${topic}\n\nMake it feel real and honest. Use facts from the source material. Write the COMPLETE script, not just an outline.` }
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],
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temperature: 0.85,
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response_format: { type: "json_object" }
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});
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const genData = JSON.parse(genResult);
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const script = JSON.parse(genData.choices[0].message.content);
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return NextResponse.json({
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id: `lf-${Date.now()}`,
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title: script.title,
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hook: script.hook,
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outline: script.outline,
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targetLength: script.targetLength || "8-12 min",
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fullScript: script.fullScript,
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platforms: ["youtube", "twitter"],
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factCheck: script.factCheck || { status: "✅", verified: [], issues: [] },
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status: "draft",
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type: contentType || "article",
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createdAt: new Date().toISOString().split("T")[0],
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notes: "",
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});
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}
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