Phase 2: Market Intel + Smart Match + Ops Timeline

job_dashboard_server.py

@@ -253,6 +253,104 @@ class DashboardHandler(SimpleHTTPRequestHandler):
             data = refresh_cache(force=True)
             return self._json_response({"ok": True, "total": data["total"]})
         
+        # GET /api/market-intel — salary distribution + trend analysis
+        elif path == "/api/market-intel":
+            data = refresh_cache()
+            jobs = data.get("jobs", [])
+            salaries = [j["salary"] for j in jobs if j["salary"]]
+            
+            brackets = [
+                {"label": "Under $80k", "min": 0, "max": 80000},
+                {"label": "$80k–$100k", "min": 80000, "max": 100000},
+                {"label": "$100k–$120k", "min": 100000, "max": 120000},
+                {"label": "$120k–$140k", "min": 120000, "max": 140000},
+                {"label": "$140k+", "min": 140000, "max": 999999},
+            ]
+            distribution = [{"label": b["label"], "count": sum(1 for s in salaries if b["min"] <= s < b["max"])} for b in brackets]
+            
+            locs = {}
+            for j in jobs:
+                loc = j["location"] or "Unknown"
+                if j["salary"]:
+                    locs.setdefault(loc, []).append(j["salary"])
+            loc_stats = {loc: {"avg": round(sum(s)/len(s)), "min": min(s), "max": max(s), "count": len(s)} for loc, s in locs.items()}
+            
+            months = {}
+            for j in jobs:
+                if j["date_added"]:
+                    m = j["date_added"][:7]
+                    months[m] = months.get(m, 0) + 1
+            
+            sources = {}
+            for j in jobs:
+                src = j["source"] or "Unknown"
+                s = sources.setdefault(src, {"count": 0, "salary_sum": 0, "salary_count": 0})
+                s["count"] += 1
+                if j["salary"]:
+                    s["salary_sum"] += j["salary"]
+                    s["salary_count"] += 1
+            src_stats = {src: {"count": d["count"], "avg_salary": round(d["salary_sum"]/d["salary_count"]) if d["salary_count"] else None} for src, d in sources.items()}
+            
+            statuses = {}
+            for j in jobs:
+                s = j["status"]
+                statuses[s] = statuses.get(s, 0) + 1
+            
+            return self._json_response({
+                "total_tracked": len(jobs),
+                "with_salary": len(salaries),
+                "avg_salary": round(sum(salaries)/len(salaries)) if salaries else 0,
+                "median_salary": sorted(salaries)[len(salaries)//2] if salaries else 0,
+                "min_salary": min(salaries) if salaries else 0,
+                "max_salary": max(salaries) if salaries else 0,
+                "salary_distribution": distribution,
+                "by_location": loc_stats,
+                "monthly_trend": dict(sorted(months.items())),
+                "by_source": src_stats,
+                "by_status": statuses,
+                "refreshed_at": data.get("refreshed_at", ""),
+            })
+        
+        # GET /api/jobs/<id>/match — match analysis for a job
+        match_job_match = re.match(r"^/api/jobs/([a-f0-9\-]+)/match$", path)
+        if match_job_match:
+            job_id = match_job_match.group(1)
+            data = refresh_cache()
+            job = next((j for j in data.get("jobs", []) if j["id"] == job_id), None)
+            if not job:
+                return self._json_response({"error": "Job not found"}, 404)
+            score = 50
+            signals = []
+            if job.get("strong_match"):
+                score += 20; signals.append("Strong match flag")
+            if job.get("salary"):
+                score += 5; signals.append("Has salary data")
+            if job.get("notes") and len(job.get("notes", "")) > 50:
+                score += 10; signals.append("Detailed JD summary")
+            elif job.get("notes"):
+                score += 5; signals.append("Has JD summary")
+            if job.get("cover_letter_url"):
+                score += 10; signals.append("Cover letter exists")
+            if job.get("role") and job.get("name"):
+                score += 5; signals.append("Company + role populated")
+            if job.get("url"):
+                score += 5; signals.append("Job URL available")
+            score = min(score, 99)
+            return self._json_response({
+                "score": score,
+                "signals": signals,
+                "strong_match": job.get("strong_match", False),
+                "has_cl": bool(job.get("cover_letter_url")),
+                "has_salary": bool(job.get("salary")),
+                "has_notes": bool(job.get("notes")),
+                "job_id": job_id,
+            })
+        
+        # GET /ops — serve the ops timeline page
+        elif path == "/ops":
+            self.path = "/ops.html"
+            return super().do_GET()
+        
         # Serve draft files (cover letters) — preserve extension for browser rendering
         # Support both /drafts/ and /jobs/drafts/ paths (Tailscale Serve uses /jobs/drafts/ from prompt)
         drafts_path = None