04e6fb64bb
Phase 0 — measurement gate: - tests/test_contracts.py: BOARDS↔NORMALIZERS alignment, funnel-shape guards, overlap() None contract, and the frontend↔backend EXACT-STRING vocab check (scout.ts options must resolve in coerce.py, else silent keyword-fold). - tests/run_regression.py + fixtures/regression_set.json (+ _seed_regression.py): a frozen 120-pair set (tech + non-tech) Opus-judged for recruiter-fit targets. Two metrics: score MAE and SIFT TOP-K MEMBERSHIP RECALL (do the best jobs reach Opus?). Baseline: MAE 24.7, recall 0.68 — i.e. ~32% of the genuinely-best jobs are cut before the curator ever sees them (worse for non-tech). Phase 1 — coverage (India non-tech): - Enable Indeed (misceres) + enrich indeed_to_scoutjob with details.description + location_mode (jobType is employment type, not skills → required_skills now []). - Add WorkIndia (shahidirfan) — India blue/grey-collar non-tech: build_workindia_input + workindia_to_scoutjob (real skills + description; per-job apply URL from job_id since source_url is generic and would dedup-collapse the deck). - Drop Wellfound from the stack (US-startup-heavy + 400s); kept registered-but-off. - BOARDS_ENABLED = naukri,foundit,linkedin,indeed,workindia. Pool ~90 → ~140 jobs/search. Regression scores unchanged (engine scoring untouched). 31 tests pass.
541 lines
23 KiB
Python
541 lines
23 KiB
Python
"""Per-board normalizers: raw Apify item → the frontend `ScoutJob` shape.
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Slice-1a: no engine, so matchScore/note/qx are placeholders (matchScore is assigned
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in search.py after merge). Each board's output differs — one mapper per board.
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"""
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from __future__ import annotations
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import html as _html
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import json
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import re
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_PALETTE = ["#4F46E5", "#EA580C", "#0F6E56", "#DB2777", "#0EA5E9", "#7C3AED", "#D97706", "#059669"]
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||
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def _plain(s, limit: int = 1600) -> str:
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"""HTML/Markdown → readable plain text for the detail modal (keeps paragraph breaks).
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We surface ONLY what the board/employer provided — and strip the scraper's own
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aggregator disclaimer boilerplate (we add nothing of our own).
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"""
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if not s:
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return ""
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# HTML → text
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s = re.sub(r"<\s*(br|/p|/li|/div|/h\d|/tr)\s*[^>]*>", "\n", str(s), flags=re.I) # breaks → newlines
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s = re.sub(r"<li[^>]*>", "• ", s, flags=re.I)
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s = re.sub(r"<[^>]+>", "", s) # drop remaining tags
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s = _html.unescape(s)
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# Markdown leftovers (when the source is markdown, not HTML)
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s = re.sub(r"(?m)^\s{0,3}#{1,6}\s*", "", s) # ATX headers → plain line
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s = re.sub(r"(?m)^[ \t]*[-*+][ \t]+", "• ", s) # list bullets → •
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s = re.sub(r"\*\*([^*]+)\*\*", r"\1", s) # **bold** → text
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s = re.sub(r"`([^`]+)`", r"\1", s) # `code` → text
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# Broken-tag remnants (e.g. a leaked "> from malformed HTML)
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s = re.sub(r'"\s*>', " ", s)
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s = s.lstrip(' "\'>')
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# Drop the scraper's appended disclaimer boilerplate (NOT employer content)
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s = re.split(r"\n?\s*Disclaimer\s*[:\-]", s, maxsplit=1, flags=re.I)[0]
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# whitespace
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s = re.sub(r"[ \t]+", " ", s)
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s = re.sub(r"\n{3,}", "\n\n", s).strip()
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return (s[:limit].rstrip() + "…") if len(s) > limit else s
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def _compact(d: dict) -> dict:
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"""Drop empty values so the details payload stays lean."""
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return {k: v for k, v in d.items() if v not in (None, "", [], {})}
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def _logo(org: str, url: str | None = None) -> dict:
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org = (org or "").strip()
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label = org[0].upper() if org else "?"
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bg = _PALETTE[sum(map(ord, org)) % len(_PALETTE)] if org else "#9CA3AF"
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d = {"label": label, "bg": bg}
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# Real company logo from the actor (used by the UI; the label/bg stay as the fallback).
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if url and isinstance(url, str) and url.startswith("http"):
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d["url"] = url
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return d
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# actor field names for the company logo, across boards + nesting (Naukri=logoPath,
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# LinkedIn=company.logo, Indeed=companyLogo, …). First http(s) value wins; None → letter badge.
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_LOGO_KEYS = ("logoPath", "logoPathV3", "logo", "companyLogo", "company_logo", "logoUrl", "image", "companyImage")
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def _logo_url(it: dict) -> str | None:
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scopes = [it, it.get("company") or {}, it.get("jobDetails") or {},
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(it.get("jobDetails") or {}).get("companyDetail") or {}]
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for sc in scopes:
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if not isinstance(sc, dict):
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continue
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for k in _LOGO_KEYS:
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v = sc.get(k)
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if isinstance(v, str) and v.startswith("http"):
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return v
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return None
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# posting-date field across boards (Naukri createdDate · LinkedIn postedDate · Foundit date_posted).
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# Feeds the dashboard "Apply window" (freshness urgency) + honest "posted Nd ago" age signals.
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_POSTED_KEYS = ("postedDate", "createdDate", "date_posted", "postedAt", "listedAt", "datePosted", "published")
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def _posted_date(it: dict) -> str | None:
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scopes = [it, it.get("jobDetails") or {}]
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for sc in scopes:
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if not isinstance(sc, dict):
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continue
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for k in _POSTED_KEYS:
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v = sc.get(k)
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if isinstance(v, str) and v[:4].isdigit(): # ISO-ish "2026-06-18T…"
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return v
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return None
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def _digits(s: str) -> int | None:
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nums = re.sub(r"[^\d]", "", s or "")
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return int(nums) if nums else None
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def _salary_from_amount(amount: int | None, per_year: bool) -> tuple[float | None, str]:
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"""amount in ₹ → (lpa, payLabel)."""
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if not amount:
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return None, "Not disclosed"
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annual = amount if per_year else amount * 12
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lpa = round(annual / 100_000, 1)
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return lpa, f"₹{lpa:g}L/yr"
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def _seniority_from_years(min_yrs: int | None):
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if min_yrs is None:
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return None
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return ("junior" if min_yrs <= 1 else "junior" if min_yrs <= 4 else
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"mid" if min_yrs <= 8 else "senior" if min_yrs <= 12 else "lead")
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# free-text seniority (LinkedIn experienceLevel / ATS seniority) → our Seniority enum
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_SENIORITY_TEXT = {
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"internship": "junior", "entry level": "junior", "entry": "junior", "junior": "junior",
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"associate": "mid", "mid": "mid", "mid-senior level": "senior", "senior": "senior",
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"director": "lead", "executive": "lead", "lead": "lead",
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}
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def _seniority_from_text(s: str | None):
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if not s:
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return None
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return _SENIORITY_TEXT.get(s.strip().lower())
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_WORKMODE = {"remote": "remote", "hybrid": "hybrid", "on_site": "onsite", "onsite": "onsite"}
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def naukri_to_scoutjob(it: dict, board: str = "Naukri") -> dict | None:
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# The naukri-job-scraper (fetchDetails=True) nests the real fields under `jobDetails`.
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jd = it.get("jobDetails") or {}
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title = (jd.get("title") or "").strip()
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org = ((jd.get("companyDetail") or {}).get("name") or "").strip()
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if not title or not org:
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return None
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locs = jd.get("locations") or []
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city = (locs[0].get("label") if locs and isinstance(locs[0], dict) else None) or None
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# keySkills is {other:[{label}], preferred:[{label}], ...} — flatten the labels.
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skills: list[str] = []
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ks = jd.get("keySkills") or {}
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if isinstance(ks, dict):
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for bucket in ks.values():
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for s in bucket or []:
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lbl = (s.get("label") if isinstance(s, dict) else str(s)).strip()
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if lbl and lbl not in skills:
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skills.append(lbl)
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# salaryDetail is already a dict here (not a JSON string).
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lpa, pay = None, "Not disclosed"
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sd = jd.get("salaryDetail") or {}
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if isinstance(sd, dict) and not sd.get("hideSalary") and sd.get("maximumSalary"):
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lpa, pay = _salary_from_amount(int(sd["maximumSalary"]), per_year=True)
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# Offsite employer/ATS apply link first; the Naukri listing (staticUrl) is the fallback.
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redirect = (
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jd.get("applyRedirectUrl") or jd.get("companyApplyUrl")
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or it.get("applyRedirectUrl") or it.get("companyApplyUrl")
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)
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return {
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"id": f"naukri-{jd.get('jobId')}",
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"title": title,
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"organization": org,
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"logo": _logo(org, _logo_url(it)),
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"posted_date": _posted_date(it),
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"location_city": city,
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"location_country": "India",
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"matchScore": 0, # placeholder — set after merge
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"salary_lpa": lpa,
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"payLabel": pay,
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"seniority_level": _seniority_from_years(_digits(str(jd.get("minimumExperience", "")))),
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"required_skills": skills[:6],
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"tags": skills[:3],
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"applicants": jd.get("applyCount"),
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"note": f"Live from {board}",
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"apply_url": redirect or jd.get("staticUrl"), # offsite employer link first, Naukri listing fallback
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"offsite_apply": bool(redirect),
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"qx": 10,
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}
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def indeed_to_scoutjob(it: dict, board: str = "Indeed") -> dict | None:
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if str(it.get("isExpired", "False")).lower() == "true":
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return None
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title = (it.get("positionName") or "").strip()
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org = (it.get("company") or "").strip()
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if not title or not org:
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return None
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# Indeed location is "Area, City, State" (or "City, State") — the city is the 2nd-to-last part.
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parts = [p.strip() for p in (it.get("location") or "").split(",") if p.strip()]
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city = (parts[-2] if len(parts) >= 2 else (parts[0] if parts else None))
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types = []
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try:
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types = [t.strip() for t in eval(it.get("jobType") or "[]")] if it.get("jobType") else []
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except Exception: # noqa: BLE001
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pass
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sal = it.get("salary") or ""
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lpa, pay = _salary_from_amount(_digits(sal), per_year="year" in sal.lower()) if sal else (None, "Not disclosed")
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ext = it.get("externalApplyLink")
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offsite = bool(ext and ext != "None") # externalApplyLink = real offsite employer link
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apply_url = ext if offsite else it.get("url") # offsite first, Indeed listing fallback
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mode = "remote" if any("remote" in t.lower() for t in types) else None
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# jobType is employment classification ("Full-time", "Remote") — NOT skills. The real signal for
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# embedding + the curator is the description, which the actor DOES return (verified Phase 0).
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details = _compact({
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"description": _plain(it.get("description") or it.get("descriptionHTML")),
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"employment_type": ", ".join(types) or None,
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})
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return {
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"id": f"indeed-{it.get('id')}",
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"title": title,
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"organization": org,
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"logo": _logo(org, _logo_url(it)),
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"posted_date": _posted_date(it),
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"location_city": city,
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"location_country": "India",
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"location_mode": mode,
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"matchScore": 0,
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"salary_lpa": lpa,
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"payLabel": pay,
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"required_skills": [], # Indeed exposes no skill list; description carries the signal
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"tags": types[:3],
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"applicants": None,
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"note": f"Live from {board}",
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"apply_url": apply_url,
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"offsite_apply": offsite,
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"details": details,
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"qx": 10,
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}
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|
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|
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def linkedin_to_scoutjob(it: dict, board: str = "LinkedIn") -> dict | None:
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title = (it.get("title") or "").strip()
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org = ((it.get("company") or {}).get("name") or "").strip()
|
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if not title or not org:
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return None
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loc = it.get("location") or {}
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parsed = loc.get("parsed") or {} if isinstance(loc, dict) else {}
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city = parsed.get("city") or None
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country = parsed.get("country") or parsed.get("countryFull") or None
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mode = _WORKMODE.get((it.get("workplaceType") or "").lower())
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industries = [s for s in (it.get("industries") or []) if isinstance(s, str)]
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sal = it.get("salary") or {}
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pay = sal.get("text") if isinstance(sal, dict) and sal.get("text") else "Not disclosed"
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co = it.get("company") or {}
|
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details = _compact({
|
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"description": _plain(it.get("descriptionText") or it.get("descriptionHtml")),
|
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"experience": it.get("experienceLevel"),
|
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"employment_type": (it.get("employmentType") or "").replace("_", " ").title() or None,
|
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"industry": ", ".join(industries[:3]) or None,
|
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"skills": industries,
|
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"company_about": _plain(co.get("description"), 700) if isinstance(co, dict) else "",
|
||
})
|
||
# Prefer the OFFSITE employer/ATS apply link over the LinkedIn listing — the
|
||
# board URL can go stale, the employer's own apply page is the durable target.
|
||
_apply_method = it.get("applyMethod") or {}
|
||
offsite = (
|
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it.get("companyApplyUrl")
|
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or (_apply_method.get("companyApplyUrl") if isinstance(_apply_method, dict) else None)
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||
or it.get("applyRedirectUrl")
|
||
)
|
||
return {
|
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"id": f"linkedin-{it.get('id')}",
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||
"title": title,
|
||
"organization": org,
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"logo": _logo(org, _logo_url(it)),
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||
"posted_date": _posted_date(it),
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"location_city": city,
|
||
"location_country": country,
|
||
"location_mode": mode,
|
||
"matchScore": 0,
|
||
"salary_lpa": None,
|
||
"payLabel": pay,
|
||
"seniority_level": _seniority_from_text(it.get("experienceLevel")),
|
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"required_skills": industries[:6],
|
||
"tags": industries[:3],
|
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"applicants": it.get("applicants"),
|
||
"note": f"Live from {board}",
|
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"apply_url": offsite or it.get("linkedinUrl"), # offsite employer link first, board link fallback
|
||
"offsite_apply": bool(offsite),
|
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"details": details,
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
def ats_to_scoutjob(it: dict, board: str = "Direct") -> dict | None:
|
||
title = (it.get("title") or "").strip()
|
||
org = (it.get("company") or "").strip()
|
||
if not title or not org:
|
||
return None
|
||
org = org.replace("-", " ").replace("_", " ").title() # ats slugs ("stripe") → "Stripe"
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parts = [p.strip() for p in (it.get("location") or "").split(",") if p.strip()]
|
||
city = parts[0] if parts else None
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mode = _WORKMODE.get((it.get("remote_type") or "").lower()) or ("remote" if it.get("remote") else None)
|
||
dept = [d for d in [it.get("department"), it.get("team")] if d]
|
||
return {
|
||
"id": f"ats-{it.get('global_id') or it.get('job_id')}",
|
||
"title": title,
|
||
"organization": org,
|
||
"logo": _logo(org, _logo_url(it)),
|
||
"posted_date": _posted_date(it),
|
||
"location_city": city,
|
||
"location_country": None,
|
||
"location_mode": mode,
|
||
"matchScore": 0,
|
||
"salary_lpa": None,
|
||
"payLabel": "Not disclosed",
|
||
"seniority_level": _seniority_from_text(it.get("seniority")),
|
||
"required_skills": dept[:6],
|
||
"tags": dept[:3],
|
||
"applicants": None,
|
||
"note": f"Live from {board}", # direct employer posting → real offsite apply link
|
||
"apply_url": it.get("apply_url") or it.get("url"),
|
||
"offsite_apply": bool(it.get("apply_url") or it.get("url")), # ATS direct posting = employer's own apply page
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
def naukri_feed_to_scoutjob(it: dict, board: str = "Naukri") -> dict | None:
|
||
"""blackfalcondata/naukri-jobs-feed (fetchDetails=True) — flat shape with a real
|
||
offsite `applyRedirectUrl` when the employer uses an external ATS."""
|
||
title = (it.get("title") or "").strip()
|
||
org = (it.get("companyName") or it.get("staticCompanyName") or "").strip()
|
||
if not title or not org:
|
||
return None
|
||
loc = it.get("location") or ""
|
||
city = (loc.split("/")[0].split(",")[0].strip() or None) if isinstance(loc, str) else None
|
||
# keySkills = {preferred:[{label}], other:[{label}]} — flatten labels.
|
||
skills: list[str] = []
|
||
ks = it.get("keySkills") or {}
|
||
if isinstance(ks, dict):
|
||
for bucket in ks.values():
|
||
for s in bucket or []:
|
||
lbl = (s.get("label") if isinstance(s, dict) else str(s)).strip()
|
||
if lbl and lbl not in skills:
|
||
skills.append(lbl)
|
||
elif isinstance(ks, list):
|
||
skills = [str(s) for s in ks if s]
|
||
lpa, pay = None, "Not disclosed"
|
||
if not it.get("salaryHidden") and it.get("salaryMax"):
|
||
lpa, pay = _salary_from_amount(int(it["salaryMax"]), per_year=True)
|
||
redirect = it.get("applyRedirectUrl")
|
||
education = "; ".join(it.get("educationUG") or []) or it.get("degreeCombination") or None
|
||
details = _compact({
|
||
"description": _plain(it.get("descriptionMarkdown") or it.get("description") or it.get("shortDescription")),
|
||
"experience": it.get("experienceText"),
|
||
"employment_type": it.get("employmentType"),
|
||
"education": education,
|
||
"industry": it.get("industry"),
|
||
"role_category": it.get("roleCategory"),
|
||
"skills": skills,
|
||
"company_about": _plain(it.get("companyDescription"), 700),
|
||
"vacancies": it.get("vacancy"),
|
||
})
|
||
return {
|
||
"id": f"naukri-{it.get('jobId')}",
|
||
"title": title.title() if title.isupper() else title,
|
||
"organization": org,
|
||
"logo": _logo(org, _logo_url(it)),
|
||
"posted_date": _posted_date(it),
|
||
"location_city": city,
|
||
"location_country": "India",
|
||
"location_mode": _WORKMODE.get((it.get("wfhType") or "").lower()),
|
||
"matchScore": 0,
|
||
"salary_lpa": lpa,
|
||
"payLabel": pay,
|
||
"seniority_level": _seniority_from_years(it.get("minimumExperience")),
|
||
"required_skills": skills[:6],
|
||
"tags": skills[:3],
|
||
"applicants": it.get("applyCount"),
|
||
"note": f"Live from {board}",
|
||
"apply_url": redirect or it.get("staticUrl") or it.get("portalUrl"),
|
||
"offsite_apply": bool(redirect), # True = real 3rd-party employer/ATS link (auto-applyable)
|
||
"details": details,
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
def foundit_to_scoutjob(it: dict, board: str = "Foundit") -> dict | None:
|
||
title = (it.get("title") or "").strip()
|
||
org = (it.get("company") or "").strip()
|
||
if not title or not org:
|
||
return None
|
||
loc = it.get("location") or ""
|
||
city = (loc.split("/")[0].split(",")[0].strip() or None) if isinstance(loc, str) else None
|
||
skills = it.get("skills") or []
|
||
if isinstance(skills, str):
|
||
skills = [s.strip() for s in skills.split(",") if s.strip()]
|
||
skills = [str(s) for s in skills if s]
|
||
seniority = None
|
||
m = re.search(r"\d+", str(it.get("experience") or ""))
|
||
if m:
|
||
seniority = _seniority_from_years(int(m.group()))
|
||
redirect = it.get("apply_url")
|
||
details = _compact({
|
||
"description": _plain(it.get("description_text") or it.get("description_html")),
|
||
"experience": it.get("experience"),
|
||
"employment_type": it.get("employment_type"),
|
||
"industry": it.get("industry"),
|
||
"role_category": it.get("function"),
|
||
"skills": skills,
|
||
})
|
||
return {
|
||
"id": f"foundit-{it.get('job_id')}",
|
||
"title": title,
|
||
"organization": org,
|
||
"logo": _logo(org, _logo_url(it)),
|
||
"posted_date": _posted_date(it),
|
||
"location_city": city,
|
||
"location_country": "India",
|
||
"matchScore": 0,
|
||
"salary_lpa": None,
|
||
"payLabel": "Not disclosed",
|
||
"seniority_level": seniority,
|
||
"required_skills": skills[:6],
|
||
"tags": [it.get("function")][:1] if it.get("function") else skills[:3],
|
||
"applicants": None,
|
||
"note": f"Live from {board}",
|
||
"apply_url": redirect or it.get("url"),
|
||
"offsite_apply": bool(redirect),
|
||
"details": details,
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
def workindia_to_scoutjob(it: dict, board: str = "WorkIndia") -> dict | None:
|
||
"""shahidirfan/workindia-jobs-scraper (includeDetails=True) — India blue/grey-collar non-tech.
|
||
Verified live (Phase 0): returns real `skills` + `profile_job_description`. Apply is on-platform."""
|
||
title = (it.get("profile_job_title") or "").strip()
|
||
org = (it.get("branch_company_name") or "").strip()
|
||
if not title or not org:
|
||
return None
|
||
city = (str(it.get("branch_location_city_name") or "").strip() or None)
|
||
skills = it.get("skills") or []
|
||
if isinstance(skills, str):
|
||
skills = [s.strip() for s in skills.split(",") if s.strip()]
|
||
skills = [str(s) for s in skills if s]
|
||
seniority = None
|
||
m = re.search(r"\d+", str(it.get("job_experience") or it.get("experience") or ""))
|
||
if m:
|
||
seniority = _seniority_from_years(int(m.group()))
|
||
# Blue/grey-collar pay is usually monthly ₹ and noisy — show the structure text, don't risk the ₹L band.
|
||
details = _compact({
|
||
"description": _plain(it.get("profile_job_description") or it.get("profile_html_description")
|
||
or it.get("profile_short_description")),
|
||
"experience": it.get("job_experience") or it.get("experience"),
|
||
"employment_type": it.get("employment_type"),
|
||
"education": it.get("degree") or it.get("profile_qualification_required"),
|
||
"industry": it.get("profile_industry_display_name"),
|
||
"skills": skills,
|
||
})
|
||
return {
|
||
"id": f"workindia-{it.get('job_id') or it.get('id')}",
|
||
"title": title.title() if title.isupper() else title,
|
||
"organization": org,
|
||
"logo": _logo(org, _logo_url(it)),
|
||
"posted_date": it.get("job_published_on_platform") or it.get("created_at"),
|
||
"location_city": city,
|
||
"location_country": "India",
|
||
"matchScore": 0,
|
||
"salary_lpa": None,
|
||
"payLabel": it.get("profile_salary_structure") or "Not disclosed",
|
||
"seniority_level": seniority,
|
||
"required_skills": skills[:6],
|
||
"tags": skills[:3],
|
||
"applicants": None,
|
||
"note": f"Live from {board}",
|
||
# source_url is a GENERIC listing page (same for every job) → would dedup-collapse the deck.
|
||
# Build a per-job URL from job_id so each is unique AND clickable.
|
||
"apply_url": (f"https://www.workindia.in/jobs/{it.get('job_id')}/"
|
||
if it.get("job_id") else it.get("source_url")),
|
||
"offsite_apply": False, # WorkIndia is a platform — apply on WorkIndia
|
||
"details": details,
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
def wellfound_to_scoutjob(it: dict, board: str = "Wellfound") -> dict | None:
|
||
"""blackfalcondata/wellfound-scraper (enrichDetail=True) — startup roles with skills, equity,
|
||
funding stage. Apply is on-platform (Wellfound), so apply_url = the listing (offsite_apply=False)."""
|
||
title = (it.get("title") or "").strip()
|
||
org = (it.get("companyName") or "").strip()
|
||
if not title or not org:
|
||
return None
|
||
locs = it.get("locationNames") or []
|
||
city = (str(locs[0]).split(",")[0].strip() or None) if isinstance(locs, list) and locs else None
|
||
skills = [str(s) for s in (it.get("skills") or []) if s]
|
||
# Salary → LPA only when the listing is in INR (don't pollute the India band with USD); else the
|
||
# raw compensation string is the honest label.
|
||
lpa, pay = None, "Not disclosed"
|
||
cur = (it.get("salaryCurrency") or "").upper()
|
||
if it.get("salaryMax") and cur == "INR":
|
||
lpa, pay = _salary_from_amount(int(it["salaryMax"]), per_year=True)
|
||
elif it.get("compensation"):
|
||
pay = str(it["compensation"])
|
||
elif it.get("salaryMin") or it.get("salaryMax"):
|
||
pay = f"{cur or '$'} {it.get('salaryMin') or ''}–{it.get('salaryMax') or ''}".strip()
|
||
details = _compact({
|
||
"description": _plain(it.get("description")),
|
||
"employment_type": (it.get("jobType") or "").replace("_", " ").title() or None,
|
||
"skills": skills,
|
||
"funding_stage": it.get("companyFundingStage"),
|
||
"equity": it.get("equityMin") or it.get("equity"),
|
||
"visa": it.get("visaSponsorship"),
|
||
})
|
||
return {
|
||
"id": f"wellfound-{it.get('id')}",
|
||
"title": title,
|
||
"organization": org,
|
||
"logo": _logo(org, _logo_url(it)),
|
||
"posted_date": _posted_date(it),
|
||
"location_city": city,
|
||
"location_country": None,
|
||
"location_mode": "remote" if it.get("remote") else None,
|
||
"matchScore": 0,
|
||
"salary_lpa": lpa,
|
||
"payLabel": pay,
|
||
"required_skills": skills[:6],
|
||
"tags": skills[:3],
|
||
"applicants": None,
|
||
"note": f"Live from {board}",
|
||
"apply_url": it.get("portalUrl"),
|
||
"offsite_apply": False, # apply happens on Wellfound (on-platform), not an offsite employer redirect
|
||
"details": details,
|
||
"qx": 10,
|
||
}
|
||
|
||
|
||
# board key → normalizer
|
||
NORMALIZERS = {
|
||
"naukri": naukri_feed_to_scoutjob, # blackfalcondata (primary)
|
||
"foundit": foundit_to_scoutjob,
|
||
"linkedin": linkedin_to_scoutjob,
|
||
"naukri_v1": naukri_to_scoutjob, # muhammetakkurtt (fallback)
|
||
"indeed": indeed_to_scoutjob,
|
||
"ats": ats_to_scoutjob,
|
||
"wellfound": wellfound_to_scoutjob, # blackfalcondata (startup roles)
|
||
"workindia": workindia_to_scoutjob, # shahidirfan (India blue/grey-collar non-tech)
|
||
}
|