Add a local benchmark for /tldr with a dashboard

`go test -tags bench ./internal/tldr -run TestBench` runs the real /tldr
pipeline against one or more Ollama models and prompt variants on a chat dump
from disk (text copied from Discord, "Author: message" lines, or a
DiscordChatExporter JSON export) and writes a self-contained HTML dashboard
plus a JSON of the results. Scoring uses an optional facts file (a fact
passes only when one bullet holds all its details) and automatic checks:
numbers not in the chat, copying the chat, an English intro, cut-off output.

TestFactsSkeleton proposes candidate facts from the chat to prune by hand.
Chat dumps and results stay local: bench-out/ and the private folders are
git-ignored. See internal/tldr/tldrbench/README.md.

Co-Authored-By: Claude Opus 5.5 <[email protected]>

internal/tldr/tldrbench/suggest.go

@@ -0,0 +1,140 @@
+package tldrbench
+
+import (
+	"fmt"
+	"regexp"
+	"sort"
+	"strings"
+	"unicode"
+)
+
+var (
+	// Things a good overview has to get right: figures, times, money, days.
+	figureRe  = regexp.MustCompile(`\d+(?:[.,:]\d+)*(?:h\d*|€|%|k|gb|mb|min)?`)
+	weekdayRe = regexp.MustCompile(`(?i)\b(segunda|ter[çc]a|quarta|quinta|sexta|s[áa]bado|domingo|monday|tuesday|wednesday|thursday|friday|saturday|sunday|amanh[ãa]|tomorrow|hoje|today)\b`)
+	moneyRe   = regexp.MustCompile(`(?i)€|\$|\beur\b|\beuros?\b`)
+	wordRe    = regexp.MustCompile(`[\p{L}][\p{L}'-]{5,}`)
+
+	// Common long words that make poor topic keywords.
+	stopwords = map[string]bool{
+		"porque": true, "também": true, "tambem": true, "depois": true, "ontem": true, "ainda": true, "estava": true,
+		"podemos": true, "devíamos": true, "devemos": true, "fazer": true, "mesmo": true, "sempre": true, "ficou": true,
+		"because": true, "should": true, "would": true, "could": true, "really": true, "maybe": true, "already": true,
+	}
+)
+
+// signals counts how many different "this matters" markers a line has.
+func signals(line string) int {
+	n := 0
+	if figureRe.MatchString(line) {
+		n++
+	}
+	if weekdayRe.MatchString(line) {
+		n++
+	}
+	if moneyRe.MatchString(line) {
+		n++
+	}
+	if strings.Contains(line, "[link") || strings.Contains(line, "(preview:") {
+		n++
+	}
+	return n
+}
+
+// SuggestFacts proposes candidate facts from transcript lines ("Author: text").
+// It does not understand the chat: it picks the lines that carry figures,
+// times, days, money or links, and for each suggests patterns (the figures and
+// days in it, plus one topic word). You are meant to prune and merge them into
+// the 10 to 20 facts that matter, then check the patterns against a real
+// overview. At most max candidates are returned, chronologically.
+func SuggestFacts(lines []string, max int) []Fact {
+	type scored struct {
+		idx, signals int
+		line         string
+	}
+	var cands []scored
+	for i, ln := range lines {
+		ln = strings.TrimSpace(ln)
+		if ln == "" {
+			continue
+		}
+		if s := signals(ln); s > 0 {
+			cands = append(cands, scored{i, s, ln})
+		}
+	}
+	// Keep the strongest, then restore chat order.
+	sort.SliceStable(cands, func(a, b int) bool { return cands[a].signals > cands[b].signals })
+	if max > 0 && len(cands) > max {
+		cands = cands[:max]
+	}
+	sort.Slice(cands, func(a, b int) bool { return cands[a].idx < cands[b].idx })
+
+	var facts []Fact
+	for n, c := range cands {
+		facts = append(facts, Fact{
+			Name: fmt.Sprintf("fact %d: %s", n+1, excerpt(c.line, 48)),
+			All:  suggestPatterns(c.line),
+			Note: c.line,
+		})
+	}
+	return facts
+}
+
+func suggestPatterns(line string) []string {
+	// Drop the "Author: " prefix so names do not become patterns.
+	text := line
+	if i := strings.Index(line, ": "); i > 0 && i <= 32 {
+		text = line[i+2:]
+	}
+
+	seen := map[string]bool{}
+	var pats []string
+	add := func(p string) {
+		key := strings.ToLower(p)
+		if p == "" || len(pats) >= 3 {
+			return
+		}
+		for have := range seen {
+			if strings.Contains(have, key) || strings.Contains(key, have) {
+				return // already covered, e.g. "segunda" next to "segunda-feira"
+			}
+		}
+		seen[key] = true
+		pats = append(pats, p)
+	}
+
+	// A topic word first, so the fact stays tied to its subject.
+	best := ""
+	for _, w := range wordRe.FindAllString(text, -1) {
+		lw := strings.ToLower(w)
+		if stopwords[lw] || strings.ContainsAny(lw, "0123456789") {
+			continue
+		}
+		if len([]rune(w)) > len([]rune(best)) {
+			best = w
+		}
+	}
+	if best != "" {
+		add(regexp.QuoteMeta(strings.ToLower(trimWord(best))))
+	}
+	for _, d := range weekdayRe.FindAllString(text, -1) {
+		add(regexp.QuoteMeta(strings.ToLower(d)))
+	}
+	for _, f := range figureRe.FindAllString(text, -1) {
+		add(regexp.QuoteMeta(strings.ToLower(f)))
+	}
+	return pats
+}
+
+func trimWord(w string) string {
+	return strings.TrimFunc(w, func(r rune) bool { return !unicode.IsLetter(r) })
+}
+
+func excerpt(s string, n int) string {
+	s = strings.Join(strings.Fields(s), " ")
+	r := []rune(s)
+	if len(r) <= n {
+		return s
+	}
+	return string(r[:n]) + "…"
+}