Pipeline Stage → Candidate Experience Focus Group

import os, requests, time, base64
from collections import defaultdict

LV_KEY = os.environ["LEVER_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
LV_BASE = "https://api.lever.co/v1"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

lv_auth = base64.b64encode(f"{LV_KEY}:".encode()).decode()
LV_H = {"Authorization": f"Basic {lv_auth}"}

def lv_get(path, params=None):
    r = requests.get(f"{LV_BASE}{path}", headers=LV_H, params=params or {})
    if r.status_code == 429:
        time.sleep(1)
        return lv_get(path, params)
    r.raise_for_status()
    return r.json()

# 1. Pull stage definitions
stages_resp = lv_get("/stages")
stage_map = {s["id"]: s["text"] for s in stages_resp.get("data", [])}

# 2. Pull opportunities with stage info
opportunities = []
offset = None
while len(opportunities) < 500:
    params = {"limit": 100, "expand": "stage"}
    if offset:
        params["offset"] = offset
    resp = lv_get("/opportunities", params)
    opportunities.extend(resp.get("data", []))
    offset = resp.get("next")
    if not offset:
        break
    time.sleep(0.15)

# 3. Group by current stage
stage_groups = defaultdict(list)
for opp in opportunities:
    stage_id = opp.get("stage")
    stage_name = stage_map.get(stage_id, "Unknown")
    stage_groups[stage_name].append({
        "name": opp.get("name", ""),
        "headline": opp.get("headline", ""),
        "origin": opp.get("origin", ""),
        "sources": opp.get("sources", []),
    })

# 4. Create personas per pipeline stage
persona_ids = []
for stage_name, opps in stage_groups.items():
    if len(opps) < 3:
        continue
    headlines = list({o["headline"] for o in opps if o["headline"]})[:5]
    origins = list({o["origin"] for o in opps if o["origin"]})[:3]

    p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
        "name": f"Lever: {stage_name} Candidate",
        "description": (
            f"Candidate currently in '{stage_name}' stage. N={len(opps)}. "
            f"Headlines: {', '.join(headlines[:3])}. Origins: {', '.join(origins)}."
        ),
        "demographic": {"job_titles": headlines},
        "psychographic": {
            "pipeline_stage": stage_name,
            "mindset": f"Candidate at {stage_name} — " + (
                "just applied, low investment" if "new" in stage_name.lower()
                else "deeply invested, high expectations" if "onsite" in stage_name.lower() or "offer" in stage_name.lower()
                else "moderate engagement"
            ),
        },
    }).json()
    persona_ids.append({"id": p["id"], "stage": stage_name, "n": len(opps)})
    print(f"Persona: {p['id']} — {stage_name} ({len(opps)} candidates)")
    time.sleep(0.3)

# 5. Run candidate experience Focus Group
PROCESS_DESCRIPTION = """Our interview process:
1. Application review (3-5 business days)
2. 30-min recruiter screen
3. 45-min hiring manager phone interview
4. 4-hour onsite (3 technical + 1 culture)
5. Offer within 48 hours of onsite decision"""

fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
    "name": "Candidate Experience by Pipeline Stage",
    "persona_ids": [p["id"] for p in persona_ids],
    "questions": [
        {"type": "likert", "text": "Rate your overall experience at your current stage (1-5)", "scale": 5},
        "What has been the most frustrating part of the process so far?",
        "What communication would improve your experience right now?",
        "Would you recommend this company to a friend based on your experience so far?",
        "What's the #1 thing that would make you drop out of this process?",
    ],
    "context": PROCESS_DESCRIPTION,
    "responses_per_persona": 3,
}).json()

# 6. Poll
for _ in range(20):
    time.sleep(5)
    data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
    if data.get("status") == "completed":
        break

print(f"\nFocus Group: {fg['id']}")
for resp in data.get("responses", []):
    stage = next((p["stage"] for p in persona_ids if p["id"] == resp.get("persona_id")), "?")
    print(f"\n[{stage}] {resp.get('question','')[:60]}")
    print(f"  → {resp.get('answer','')[:250]}")

const LV_KEY = process.env.LEVER_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const LV_BASE = "https://api.lever.co/v1";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const LV_H = { Authorization: `Basic ${btoa(`${LV_KEY}:`)}` };

async function lvGet(path, params = {}) {
  const qs = new URLSearchParams(params).toString();
  const res = await fetch(`${LV_BASE}${path}?${qs}`, { headers: LV_H });
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 1000)); return lvGet(path, params); }
  if (!res.ok) throw new Error(`Lever ${res.status}`);
  return res.json();
}

// 1. Stages
const stageMap = Object.fromEntries(
  ((await lvGet("/stages")).data || []).map((s) => [s.id, s.text])
);

// 2. Opportunities
const opportunities = [];
let offset = null;
while (opportunities.length < 500) {
  const params = { limit: 100, expand: "stage" };
  if (offset) params.offset = offset;
  const resp = await lvGet("/opportunities", params);
  opportunities.push(...(resp.data || []));
  offset = resp.next;
  if (!offset) break;
  await new Promise((r) => setTimeout(r, 150));
}

// 3. Group by stage
const stageGroups = {};
for (const opp of opportunities) {
  const stageName = stageMap[opp.stage] || "Unknown";
  (stageGroups[stageName] ??= []).push({
    headline: opp.headline || "",
    origin: opp.origin || "",
  });
}

// 4. Personas
const personaIds = [];
for (const [stageName, opps] of Object.entries(stageGroups)) {
  if (opps.length < 3) continue;
  const headlines = [...new Set(opps.map((o) => o.headline).filter(Boolean))].slice(0, 5);
  const origins = [...new Set(opps.map((o) => o.origin).filter(Boolean))].slice(0, 3);

  const p = await fetch(`${MV_BASE}/personas`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `Lever: ${stageName} Candidate`,
      description: `${stageName} stage. N=${opps.length}. Headlines: ${headlines.slice(0, 3).join(", ")}.`,
      demographic: { job_titles: headlines },
      psychographic: { pipeline_stage: stageName },
    }),
  }).then((r) => r.json());
  personaIds.push({ id: p.id, stage: stageName, n: opps.length });
  console.log(`Persona: ${p.id} — ${stageName} (${opps.length})`);
  await new Promise((r) => setTimeout(r, 300));
}

// 5. Focus Group
const fg = await fetch(`${MV_BASE}/focus-groups`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "Candidate Experience by Stage",
    persona_ids: personaIds.map((p) => p.id),
    questions: [
      { type: "likert", text: "Rate your overall experience at this stage (1-5)", scale: 5 },
      "What has been most frustrating so far?",
      "What communication would improve your experience?",
      "Would you recommend this company to a friend?",
      "What would make you drop out of this process?",
    ],
    context: "Our process: 1) App review (3-5 days) 2) 30-min recruiter screen 3) 45-min phone 4) 4hr onsite 5) Offer in 48hr",
    responses_per_persona: 3,
  }),
}).then((r) => r.json());

// 6. Poll
let data;
for (let i = 0; i < 20; i++) {
  await new Promise((r) => setTimeout(r, 5000));
  data = await fetch(`${MV_BASE}/focus-groups/${fg.id}`, { headers: MV_H }).then((r) => r.json());
  if (data.status === "completed") break;
}

for (const resp of data.responses || []) {
  const stage = personaIds.find((p) => p.id === resp.persona_id)?.stage || "?";
  console.log(`\n[${stage}] ${(resp.question || "").slice(0, 60)}`);
  console.log(`  → ${(resp.answer || "").slice(0, 250)}`);
}