feat(observability): instrument AI + HTTP paths, serve /metrics on a side port (ADR-030, #15)

Wire the metrics package into the live paths and serve it:
- summarizer: per-endpoint latency by model/outcome(success|error|parse_error)/fallback + slog.
- youtube.FetchTranscript: latency by outcome (captions|none|rate_limited) + slog.
- chat: answer latency by model + slog.
- llm usage hook → token counts (prompt|completion) per model, wired in buildSummarizer/buildChat.
- oidc callback: login counter.
- cmdServe: wrap Router in metrics.HTTPMiddleware (request count + latency by bounded
  route pattern) and serve /metrics on TAPIR_METRICS_ADDR (default :9090), a SEPARATE
  port — never on the public app mux.

BDD: observability.feature scenarios un-pended + mapped. TDD: summarizer wiring tested
black-box via the /metrics scrape; metrics-not-on-public-mux asserted.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

internal/adapters/chat/chat.go

@@ -13,8 +13,12 @@ package chat
 import (
 	"context"
 	"fmt"
+	"log/slog"
 	"strings"
+	"time"
 	"unicode/utf8"
+
+	"gitea.d-ma.be/mathias/tapir/internal/metrics"
 )
 
 // Completer is the minimal LLM chat surface the Service needs. *llm.Client
@@ -113,10 +117,14 @@ func (s *Service) Answer(ctx context.Context, req Request) (Reply, error) {
 	system := buildSystem(transcript, truncated)
 	user := buildUser(req.History, req.Question)
 
+	start := time.Now()
 	out, err := s.newClient(model).Complete(ctx, system, user)
 	if err != nil {
 		return Reply{}, fmt.Errorf("chat: %s: %w", model, err)
 	}
+	dur := time.Since(start)
+	metrics.ObserveChat(model, dur)
+	slog.Default().Info("chat answer", "model", model, "elapsed_ms", dur.Milliseconds())
 	answer := strings.TrimSpace(out)
 	if answer == "" {
 		return Reply{}, fmt.Errorf("chat: %s returned an empty answer", model)