Job Posting Optimization

import os, requests, time, base64

GH_KEY = os.environ["GREENHOUSE_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
GH_BASE = "https://harvest.greenhouse.io/v1"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

gh_auth = base64.b64encode(f"{GH_KEY}:".encode()).decode()
GH_H = {"Authorization": f"Basic {gh_auth}"}

# 1. Pull open jobs
jobs = requests.get(f"{GH_BASE}/jobs", headers=GH_H,
    params={"status": "open", "per_page": 50}).json()

# 2. Create target candidate personas
CANDIDATE_ARCHETYPES = [
    {"name": "Passive Senior Engineer", "desc": "Employed, not actively looking. 8+ years exp. Cares about impact, not perks."},
    {"name": "Active Mid-Level IC", "desc": "3-5 years exp, actively interviewing. Compares 5+ postings. Values clarity and growth."},
    {"name": "Career Changer", "desc": "Switching from adjacent field. Needs to understand if they qualify. Values inclusive language."},
]

persona_ids = []
for arch in CANDIDATE_ARCHETYPES:
    p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
        "name": f"GH Candidate: {arch['name']}",
        "description": arch["desc"],
        "psychographic": {"job_seeking_status": arch["name"].split()[0].lower()},
    }).json()
    persona_ids.append(p["id"])
    time.sleep(0.2)

# 3. Rate each posting via Focus Group
results = []
for job in jobs[:10]:
    title = job.get("name", "Untitled")
    dept = (job.get("departments", [{}])[0] or {}).get("name", "N/A")
    office = (job.get("offices", [{}])[0] or {}).get("name", "Remote")

    content_parts = []
    for q in job.get("questions", []):
        content_parts.append(q.get("label", ""))
    job_content = job.get("notes", "") or "\n".join(content_parts)

    posting_detail = requests.get(f"{GH_BASE}/jobs/{job['id']}", headers=GH_H).json()
    description = ""
    for sec in posting_detail.get("content", {}).get("sections", []):
        description += f"\n{sec.get('title','')}\n{sec.get('body','')}"
    if not description:
        description = posting_detail.get("notes", "") or title

    fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
        "name": f"Job Posting Review: {title}",
        "persona_ids": persona_ids,
        "questions": [
            {"type": "likert", "text": "Rate the CLARITY of this job posting (1=confusing, 5=crystal clear)", "scale": 5},
            {"type": "likert", "text": "Rate the APPEAL of this posting (1=would skip, 5=would apply immediately)", "scale": 5},
            {"type": "likert", "text": "Rate the AUTHENTICITY (1=corporate fluff, 5=genuine and believable)", "scale": 5},
            {"type": "open_ended", "text": "What about this posting would make you NOT apply?"},
            {"type": "open_ended", "text": "Rewrite the first sentence to make it more compelling."},
        ],
        "context": f"JOB POSTING: {title}\nDepartment: {dept} | Location: {office}\n\n{description[:2000]}",
        "responses_per_persona": 2,
    }).json()

    for _ in range(15):
        time.sleep(5)
        fg_data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
        if fg_data.get("status") == "completed":
            break

    scores = {"clarity": [], "appeal": [], "authenticity": []}
    for resp in fg_data.get("responses", []):
        q = resp.get("question", "").lower()
        val = resp.get("rating") or resp.get("score")
        if val:
            if "clarity" in q: scores["clarity"].append(val)
            elif "appeal" in q: scores["appeal"].append(val)
            elif "authenticity" in q: scores["authenticity"].append(val)

    avg = lambda lst: sum(lst)/len(lst) if lst else 0
    result = {
        "job": title, "dept": dept, "fg_id": fg["id"],
        "clarity": round(avg(scores["clarity"]), 1),
        "appeal": round(avg(scores["appeal"]), 1),
        "authenticity": round(avg(scores["authenticity"]), 1),
    }
    results.append(result)
    print(f"{title}: clarity={result['clarity']} appeal={result['appeal']} auth={result['authenticity']}")
    time.sleep(1)

# 4. Flag underperformers
for r in sorted(results, key=lambda x: x["appeal"]):
    flag = "⚠ REWRITE" if r["appeal"] < 3.0 else "✓ OK"
    print(f"  [{flag}] {r['job']}: C={r['clarity']} A={r['appeal']} Au={r['authenticity']}")

const GH_KEY = process.env.GREENHOUSE_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const GH_BASE = "https://harvest.greenhouse.io/v1";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const GH_H = { Authorization: `Basic ${btoa(`${GH_KEY}:`)}` };

// 1. Open jobs
const jobs = await fetch(`${GH_BASE}/jobs?status=open&per_page=50`, { headers: GH_H })
  .then((r) => r.json());

// 2. Candidate archetypes
const ARCHETYPES = [
  { name: "Passive Senior Engineer", desc: "Employed, not looking. 8+ yrs. Cares about impact." },
  { name: "Active Mid-Level IC", desc: "3-5 yrs, actively interviewing. Compares 5+ postings." },
  { name: "Career Changer", desc: "Switching fields. Needs to know if they qualify." },
];

const personaIds = [];
for (const arch of ARCHETYPES) {
  const p = await fetch(`${MV_BASE}/personas`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `GH Candidate: ${arch.name}`, description: arch.desc,
      psychographic: { job_seeking_status: arch.name.split(" ")[0].toLowerCase() },
    }),
  }).then((r) => r.json());
  personaIds.push(p.id);
  await new Promise((r) => setTimeout(r, 200));
}

// 3. Rate each posting
const results = [];
for (const job of jobs.slice(0, 10)) {
  const title = job.name || "Untitled";
  const dept = (job.departments?.[0] || {}).name || "N/A";
  const office = (job.offices?.[0] || {}).name || "Remote";

  const detail = await fetch(`${GH_BASE}/jobs/${job.id}`, { headers: GH_H }).then((r) => r.json());
  const description = (detail.content?.sections || [])
    .map((s) => `${s.title}\n${s.body}`).join("\n") || detail.notes || title;

  const fg = await fetch(`${MV_BASE}/focus-groups`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `Job Posting Review: ${title}`,
      persona_ids: personaIds,
      questions: [
        { type: "likert", text: "Rate CLARITY (1=confusing, 5=crystal clear)", scale: 5 },
        { type: "likert", text: "Rate APPEAL (1=would skip, 5=would apply immediately)", scale: 5 },
        { type: "likert", text: "Rate AUTHENTICITY (1=corporate fluff, 5=genuine)", scale: 5 },
        { type: "open_ended", text: "What would make you NOT apply?" },
        { type: "open_ended", text: "Rewrite the first sentence to be more compelling." },
      ],
      context: `JOB: ${title}\nDept: ${dept} | Location: ${office}\n\n${description.slice(0, 2000)}`,
      responses_per_persona: 2,
    }),
  }).then((r) => r.json());

  let fgData;
  for (let i = 0; i < 15; i++) {
    await new Promise((r) => setTimeout(r, 5000));
    fgData = await fetch(`${MV_BASE}/focus-groups/${fg.id}`, { headers: MV_H }).then((r) => r.json());
    if (fgData.status === "completed") break;
  }

  const scores = { clarity: [], appeal: [], authenticity: [] };
  for (const resp of fgData.responses || []) {
    const q = (resp.question || "").toLowerCase();
    const val = resp.rating || resp.score;
    if (val) {
      if (q.includes("clarity")) scores.clarity.push(val);
      else if (q.includes("appeal")) scores.appeal.push(val);
      else if (q.includes("authenticity")) scores.authenticity.push(val);
    }
  }

  const avg = (a) => a.length ? a.reduce((s, v) => s + v, 0) / a.length : 0;
  const result = {
    job: title, dept, fg_id: fg.id,
    clarity: +avg(scores.clarity).toFixed(1),
    appeal: +avg(scores.appeal).toFixed(1),
    authenticity: +avg(scores.authenticity).toFixed(1),
  };
  results.push(result);
  console.log(`${title}: clarity=${result.clarity} appeal=${result.appeal} auth=${result.authenticity}`);
  await new Promise((r) => setTimeout(r, 1000));
}

results.sort((a, b) => a.appeal - b.appeal).forEach((r) => {
  const flag = r.appeal < 3.0 ? "REWRITE" : "OK";
  console.log(`  [${flag}] ${r.job}: C=${r.clarity} A=${r.appeal} Au=${r.authenticity}`);
});