/\n self.attr_items = [] # (kind, text)\n self._buf = []\n self._hidden_now = False\n self._stack = []\n self.incode = 0"
},
"output": "The file has been updated successfully. (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Edit",
"input": {
"replace_all": false,
"file_path": "",
"old_string": " def _flush(self):\n t = \" \".join(\"\".join(self._buf).split())\n if t:\n self.blocks.append((t, self._hidden_now))\n self._buf = []\n self._hidden_now = self.hidden > 0",
"new_string": " def _flush(self):\n t = \" \".join(\"\".join(self._buf).split())\n if t:\n if self._code_now:\n self.code_blocks.append(t)\n else:\n self.blocks.append((t, self._hidden_now))\n self._buf = []\n self._hidden_now = self.hidden > 0\n self._code_now = self.incode > 0"
},
"output": "The file has been updated successfully. (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Edit",
"input": {
"replace_all": false,
"file_path": "",
"old_string": " self._hidden_now = False\n self._stack = []\n self.incode = 0",
"new_string": " self._hidden_now = False\n self._code_now = False\n self._stack = []\n self.incode = 0"
},
"output": "The file has been updated successfully. (file state is current in your context — no need to Read it back)",
"error": false
},
{
"say": "Now the inline-boundary fix and code tracking in the tag handlers:"
},
{
"tool": "Edit",
"input": {
"replace_all": false,
"file_path": "",
"old_string": " is_hidden = bool(DISPLAY_NONE.search(a.get(\"style\", \"\")) or\n HIDDEN_CLASS.search(a.get(\"class\", \"\")) or\n \"hidden\" in a)\n if tag in BLOCK:\n self._flush()",
"new_string": " is_hidden = bool(DISPLAY_NONE.search(a.get(\"style\", \"\")) or\n HIDDEN_CLASS.search(a.get(\"class\", \"\")) or\n \"hidden\" in a)\n if tag in CODE_TAGS:\n self._flush()\n self.incode += 1\n self._code_now = True\n if tag in BLOCK:\n self._flush()\n elif tag in INLINE:\n # keep adjacent inline siblings (nav links, badges) from gluing\n # into a single token -- otherwise word counts come out low\n self._buf.append(\" \")"
},
"output": "The file has been updated successfully. (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Edit",
"input": {
"replace_all": false,
"file_path": "",
"old_string": " for t, mark in self._stack[i:]:\n if mark == \"skip\":\n self.skip = max(0, self.skip - 1)\n elif mark == \"hid\":\n self.hidden = max(0, self.hidden - 1)\n del self._stack[i:]\n break\n if tag in BLOCK and not self.skip:\n self._flush()\n self._hidden_now = self.hidden > 0",
"new_string": " for t, mark in self._stack[i:]:\n if mark == \"skip\":\n self.skip = max(0, self.skip - 1)\n elif mark == \"hid\":\n self.hidden = max(0, self.hidden - 1)\n if t in CODE_TAGS:\n self.incode = max(0, self.incode - 1)\n del self._stack[i:]\n break\n if tag in CODE_TAGS and not self.skip:\n self._flush()\n if tag in BLOCK and not self.skip:\n self._flush()\n elif tag in INLINE and not self.skip:\n self._buf.append(\" \")\n self._hidden_now = self.hidden > 0\n self._code_now = self.incode > 0"
},
"output": "The file has been updated successfully. (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 test_one.py index.html 2>&1 | sed -n '1,12p;55,80p'",
"description": "Retest extractor on homepage after inline fix"
},
"output": "lang: en\ntitle: wxrks: Your AI Translation Operating System\nmeta: {\"description\": \"wxrks is a modern Translation Management System with native AI, designed to unify cost, quality, workflow, and automation in localization.\", \"og:title\": \"wxrks: Your AI Translation Operating System\", \"og:description\": \"wxrks is a modern Translation Management System with native AI, designed to unify cost, quality, workflow, and automation in localization.\", \"twitter:title\": \"wxrks: Your AI Translation Operating System\", \"twitter:description\": \"wxrks is a modern Translation Manag\n\nblocks: 264 visible=264 hidden=0\nvisible words: 1837 hidden words: 0\nattr items: 30 words: 189\n\n--- FIRST 60 VISIBLE BLOCKS ---\n | Platform\n | The wxrks Platform\n | Unleash translation super-powers\n | Zendesk Translation\n | NEW\n | Make your Help Center multilingual with automated, in-context translations.\n | CLI Tool\n | NEW\n | Execute localization workflows for automation systems and AI agents directly from code.\n | Contentful Translation\n | NEW\n | Smooth localization in Contentful, with AI translation and human review in one place.\n | Webflow Translation\n | NEW\n | Automate your Webflow localization process and publish multilingual sites reliably.\n | AEM Translation\n | NEW\n | Translate your AEM sites into every language from one wizard, and review each page in AEM.\n\n--- LAST 25 VISIBLE BLOCKS ---\n | June 19, 2026\n | Get started\n | for free\n | Try wxrks for as long as you would like with our free Starter plan. Purchase … [328 more characters]",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "ls -1 | wc -l",
"description": "Check crawl progress"
},
"output": " 8173",
"error": false
},
{
"say": "Count rose 1768 → 1837, recovering the glued nav tokens. Let me confirm the footer now separates correctly:"
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport extract\nd=extract.parse(open('raw/index.html','rb').read())\nvis=[b for b,h in d.blocks if not h]\nfor b in vis[-18:]: print('|',b[:200])\nprint('code blocks:',len(d.code_blocks))\n\"",
"description": "Verify footer link separation"
},
"output": "| Live Interactive Workshop December 5, 2024\n| Company\n| Who We Are Our Mission Careers In the News Privacy Policy Terms of Use Terms (DPA) Trust Center Acceptable Use Policy Cookie Policy EULA\n| Use Cases\n| For Translators For Enterprise For Developers For Translation Agencies For Small-Med Businesses ChatGPT Integration\n| Content-Type\n| Content Localization Website Localization App Localization Localization Glossary eLearning Localization Product Localization\n| Industries\n| Financial Marketing Legal Medical Software Education Business eLearning\n| Resources\n| Blog Workshop Help Center Community Case Studies Security Translation Memory AI Translation CAT Tool Machine Translation TMS\n| Guides\n| Software Localization Localization Strategy Localization Glossary Transparent Translation Company Translation Agencies\n| Developers\n| API CLI Status\n| © 2026 wxrks. All rights reserved.\n| Request an AI summary of wxrks\n| Make it wxrks\ncode blocks: 0\nShell cwd was reset to ",
"error": false
},
{
"say": "Extractor is now correct. Writing the full analysis engine (per-page counts + CAT-style repetition):"
},
{
"tool": "Write",
"input": {
"file_path": "",
"content": "#!/usr/bin/env python3\n\"\"\"Word-count + repetition analysis over the crawled wxrks.com pages.\"\"\"\nimport os, re, sys, json, csv, hashlib, collections\nfrom multiprocessing import Pool\nfrom urllib.parse import urlsplit\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nimport extract\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nRAW = os.path.join(HERE, \"raw\")\nOUT = os.path.join(HERE, \"out\")\nos.makedirs(OUT, exist_ok=True)\n\nLOCALES = [\"pt\", \"es\", \"fr\", \"de\", \"ko\", \"it\", \"ar\", \"zh\", \"ja\", \"sv\"]\nCJK_LOCALES = {\"zh\", \"ja\"}\n\n\ndef locale_of(path):\n seg = path.strip(\"/\").split(\"/\")[0] if path.strip(\"/\") else \"\"\n return seg if seg in LOCALES else \"en\"\n\n\ndef section_of(path, loc):\n p = path.strip(\"/\")\n if loc != \"en\" and p.startswith(loc):\n p = p[len(loc):].strip(\"/\")\n if not p:\n return \"home\"\n first = p.split(\"/\")[0]\n if first in (\"blog\", \"artigos\", \"diario\", \"blogue\", \"beulrogeu\",\n \"blogeintrage\"):\n return \"blog\" if \"/\" in p else \"blog-index\"\n return first\n\n\ndef h8(s):\n return hashlib.blake2b(s.encode(\"utf-8\"), digest_size=8).digest()\n\n\ndef one(args):\n fname, url = args\n try:\n raw = open(os.path.join(RAW, fname), \"rb\").read()\n except Exception:\n return None\n d = extract.parse(raw)\n path = urlsplit(url).path\n loc = locale_of(path)\n\n vis = [b for b, hid in d.blocks if not hid]\n hid = [b for b, hid in d.blocks if hid]\n body = \"\\n\".join(vis)\n\n bw, bchars, bcjk, blatin = extract.count_text(body)\n hw = extract.count_text(\"\\n\".join(hid))[0] if hid else 0\n cw = extract.count_text(\"\\n\".join(d.code_blocks))[0] if d.code_blocks else 0\n\n meta_txt = \" \".join([d.title.strip()] + [v for v in d.meta.values()])\n mw = extract.count_text(meta_txt)[0] if meta_txt.strip() else 0\n attr_txt = \" \".join(t for _, t in d.attr_items)\n aw = extract.count_text(attr_txt)[0] if attr_txt.strip() else 0\n\n # CAT-style segments over the visible body\n segs = []\n for b in vis:\n for s in extract.segments(b):\n n = extract.norm_seg(s)\n if not n:\n continue\n w = extract.count_text(s)[0]\n if w:\n segs.append((h8(n), w, s))\n return {\n \"url\": url, \"file\": fname, \"path\": path, \"locale\": loc,\n \"section\": section_of(path, loc), \"lang_attr\": d.lang,\n \"title\": d.title.strip()[:200],\n \"blocks\": len(vis), \"hidden_blocks\": len(hid),\n \"body_words\": bw, \"latin_words\": blatin, \"cjk_chars\": bcjk,\n \"chars_nospace\": bchars,\n \"hidden_words\": hw, \"code_words\": cw,\n \"meta_words\": mw, \"attr_words\": aw,\n \"segs\": [(a, b) for a, b, _ in segs],\n \"seg_samples\": [(a, c[:300]) for a, b, c in segs],\n \"content_hash\": hashlib.blake2b(\n extract.norm_seg(body).encode(), digest_size=12).hexdigest(),\n }\n\n\ndef main():\n res = json.load(open(os.path.join(HERE, \"crawl_results.json\")))\n tasks = [(v[\"path\"], u) for u, v in sorted(res.items())\n if v.get(\"html\") and v.get(\"status\") == 200 and v.get(\"path\")]\n print(f\"pages to analyze: {len(tasks)}\", flush=True)\n\n recs = []\n with Pool(os.cpu_count() or 4) as p:\n for i, r in enumerate(p.imap_unordered(one, tasks, chunksize=16), 1):\n if r:\n recs.append(r)\n if i % 1000 == 0:\n print(f\" {i}/{len(tasks)}\", flush=True)\n\n recs.sort(key=lambda r: r[\"url\"])\n print(f\"analyzed {len(recs)} pages\", flush=True)\n\n # ---- global segment sample map + page frequency\n sample = {}\n seg_pages = collections.Counter() # hash -> # pages containing it\n for r in recs:\n for hh, tx in r[\"seg_samples\"]:\n if hh not in sample:\n sample[hh] = tx\n for hh in {h for h, _ in r[\"segs\"]}:\n seg_pages[hh] += 1\n del r[\"seg_samples\"]\n\n # ---- per-locale repetition (CAT-style: 1st occurrence new, rest repeats)\n per_loc = {}\n for loc in [\"en\"] + LOCALES:\n pages = [r for r in recs if r[\"locale\"] == loc]\n seen = set()\n tot = new = rep = 0\n for r in pages:\n for hh, w in r[\"segs\"]:\n tot += w\n if hh in seen:\n rep += w\n else:\n seen.add(hh)\n new += w\n per_loc[loc] = {\n \"pages\": len(pages),\n \"body_words\": sum(r[\"body_words\"] for r in pages),\n \"meta_words\": sum(r[\"meta_words\"] for r in pages),\n \"attr_words\": sum(r[\"attr_words\"] for r in pages),\n \"code_words\": sum(r[\"code_words\"] for r in pages),\n \"cjk_chars\": sum(r[\"cjk_chars\"] for r in pages),\n \"chars_nospace\": sum(r[\"chars_nospace\"] for r in pages),\n \"seg_total_words\": tot, \"seg_new_words\": new,\n \"seg_repeated_words\": rep,\n \"distinct_segments\": len(seen),\n \"dup_pages\": len(pages) - len({r[\"content_hash\"] for r in pages}),\n }\n\n # ---- boilerplate: segments present on >=50% of English pages\n en_pages = [r for r in recs if r[\"locale\"] == \"en\"]\n n_en = max(1, len(en_pages))\n boiler = [(hh, c) for hh, c in seg_pages.items() if c >= 0.5 * n_en]\n boiler_h = {hh for hh, _ in boiler}\n boiler_words_per_page = collections.Counter()\n for r in en_pages:\n w = sum(w for hh, w in r[\"segs\"] if hh in boiler_h)\n boiler_words_per_page[r[\"url\"]] = w\n\n top_boiler = sorted(boiler, key=lambda x: -x[1])[:40]\n\n # ---- write outputs\n with open(os.path.join(OUT, \"pages.csv\"), \"w\", newline=\"\") as f:\n cols = [\"url\", \"locale\", \"section\", \"lang_attr\", \"title\", \"blocks\",\n \"body_words\", \"meta_words\", \"attr_words\", \"code_words\",\n \"cjk_chars\", \"chars_nospace\", \"hidden_words\", \"content_hash\"]\n wr = csv.DictWriter(f, fieldnames=cols, extrasaction=\"ignore\")\n wr.writeheader()\n for r in recs:\n wr.writerow(r)\n\n summary = {\n \"pages_total\": len(recs),\n \"per_locale\": per_loc,\n \"boilerplate_segments\": len(boiler_h),\n \"boiler_words_median_en\": (\n sorted(boiler_words_per_page.values())[len(en_pages)//2]\n if en_pages else 0),\n \"top_boilerplate\": [\n {\"pages\": c, \"text\": sample.get(hh, \"\")[:160]}\n for hh, c in top_boiler],\n }\n json.dump(summary, open(os.path.join(OUT, \"summary.json\"), \"w\"),\n indent=1, ensure_ascii=False)\n\n # section breakdown for English\n sec = collections.Counter()\n secw = collections.Counter()\n for r in en_pages:\n sec[r[\"section\"]] += 1\n secw[r[\"section\"]] += r[\"body_words\"]\n json.dump({\"counts\": dict(sec), \"words\": dict(secw)},\n open(os.path.join(OUT, \"en_sections.json\"), \"w\"), indent=1)\n\n print(json.dumps({k: {kk: vv for kk, vv in v.items()}\n for k, v in per_loc.items()}, indent=1))\n print(\"pages_total\", len(recs))\n\n\nif __name__ == \"__main__\":\n main()\n"
},
"output": "File created successfully at: (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "ls -1 | wc -l",
"description": "Check crawl progress"
},
"output": " 9564",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "tail -c 2000 ",
"description": "Check crawl log tail"
},
"output": "(Bash completed with no output)",
"error": false
},
{
"say": "Nearly done. Let me validate on a CJK page and a blog post — those stress different parts of the counter."
},
{
"tool": "Bash",
"input": {
"command": "cd && ls raw | grep -E '^(zh|ja)\\.html$|^zh_blog' | head -5",
"description": "List CJK page files"
},
"output": "This Bash command contains multiple operations. The following part requires approval: grep -E '^(zh|ja)\\.html$|^zh_blog'",
"error": true
},
{
"tool": "Bash",
"input": {
"command": "cd && ls raw | grep -E '^blog_' | head -5",
"description": "List English blog post files"
},
"output": "blog_10-best-document-translation-services-for-your-business.html\nblog_10-best-practices-for-multilingual-website-design-fc.html\nblog_10-ui-localization-best-practices-for-a-successful-global-launch-fc.html\nblog_12-keys-to-translating-marketing-materials-for-diverse-audiences-fc.html\nblog_12-things-to-consider-when-entering-a-foreign-software-market-fc.html\nShell cwd was reset to ",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport extract, glob, os\nfor f in ['raw/zh.html','raw/ja.html','raw/blog_10-best-document-translation-services-for-your-business.html']:\n if not os.path.exists(f): print('missing',f); continue\n d=extract.parse(open(f,'rb').read())\n vis=[b for b,h in d.blocks if not h]\n body='\\n'.join(vis)\n w,ch,cjk,lat=extract.count_text(body)\n print('==',f,'lang=',d.lang)\n print(' blocks',len(vis),'words',w,'latin',lat,'cjk_chars',cjk,'chars_nospace',ch)\n print(' sample:', ' / '.join(vis[2:6])[:300])\n print(' segments in body:', sum(len(extract.segments(b)) for b in vis))\n\"",
"description": "Validate counter on Chinese, Japanese, and English blog pages"
},
"output": "== raw/zh.html lang= zh\n blocks 262 words 2699 latin 780 cjk_chars 1919 chars_nospace 6279\n sample: 释放翻译超能力 / 高级概述 / 工具 / 增强的动作\n segments in body: 286\n== raw/ja.html lang= ja\n blocks 263 words 3723 latin 786 cjk_chars 2937 chars_nospace 7340\n sample: 翻訳の超能力を解き放つ / ハイレベルな概要 / ツール / 拡張アクション\n segments in body: 288\n== raw/blog_10-best-document-translation-services-for-your-business.html lang= en\n blocks 302 words 3320 latin 3312 cjk_chars 8 chars_nospace 19177\n sample: Unleash translation super-powers / High-level overview / tools / Augmented Actions\n segments in body: 391\nShell cwd was reset to ",
"error": false
},
{
"say": "Important: blog pages begin with the same mega-menu text as the homepage — so boilerplate repeats on every page. The repetition analysis will be decisive here. Checking crawl status:"
},
{
"tool": "Bash",
"input": {
"command": "ls -1 | wc -l",
"description": "Check crawl progress"
},
"output": " 10515",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "grep -E 'DONE|seeded' ",
"description": "Get crawl completion stats"
},
"output": "DONE total=10769 html200=10515",
"error": false
},
{
"say": "Crawl complete: 10,515 pages fetched. Let me check what didn't resolve, then run the analysis."
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport json,collections\nr=json.load(open('crawl_results.json'))\nc=collections.Counter((v.get('status'), v.get('error','')) for v in r.values() if not (v.get('html') and v.get('status')==200))\nfor k,v in c.most_common(15): print(v,k)\nprint('--- sample non-200 urls ---')\nn=0\nfor u,v in r.items():\n if not (v.get('html') and v.get('status')==200):\n print(' ',v.get('status'),u[:100]); n+=1\n if n>=12: break\n\"",
"description": "Inspect non-200 / non-HTML results"
},
"output": "232 (404, 'http')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/\\\\\\' + esc(href) + \\\\\\'\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/ar/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/ar/\\\\\\' + esc(href) + \\\\\\'\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/de/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/de/\\\\\\' + esc(href) + \\\\\\'\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/es/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/es/\\\\\\' + esc(href) + \\\\\\'\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/fr/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/fr/\\\\\\' + esc(href) + \\\\\\'\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'InvalidURL(\\'URL can\\\\\\'t contain control characters. \"/it/\\\\\\' + esc(CAREERS.replace(/jobs\\\\\\\\\\\\\\\\/$/,\" (found at least \\\\\\' \\\\\\')\\')')\n1 (0, 'In … [1,398 more characters]",
"error": false
},
{
"say": "Non-200s are genuine broken links (232 × 404) plus a few JS-template artifacts — no real pages missed. Running the analysis:"
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 analyze.py 2>&1 | tail -70",
"description": "Run word count and repetition analysis",
"timeout": 600000
},
"output": " \"body_words\": 1653893,\n \"meta_words\": 96309,\n \"attr_words\": 9269,\n \"code_words\": 4,\n \"cjk_chars\": 11840,\n \"chars_nospace\": 9775524,\n \"seg_total_words\": 1653893,\n \"seg_new_words\": 788956,\n \"seg_repeated_words\": 864937,\n \"distinct_segments\": 50756,\n \"dup_pages\": 5\n },\n \"ar\": {\n \"pages\": 930,\n \"body_words\": 1403453,\n \"meta_words\": 78016,\n \"attr_words\": 9255,\n \"code_words\": 4,\n \"cjk_chars\": 6837,\n \"chars_nospace\": 7662077,\n \"seg_total_words\": 1403453,\n \"seg_new_words\": 654063,\n \"seg_repeated_words\": 749390,\n \"distinct_segments\": 50305,\n \"dup_pages\": 5\n },\n \"zh\": {\n \"pages\": 926,\n \"body_words\": 2361872,\n \"meta_words\": 140516,\n \"attr_words\": 9191,\n \"code_words\": 4,\n \"cjk_chars\": 1960452,\n \"chars_nospace\": 4336927,\n \"seg_total_words\": 2361872,\n \"seg_new_words\": 1149457,\n \"seg_repeated_words\": 1212415,\n \"distinct_segments\": 46929,\n \"dup_pages\": 5\n },\n \"ja\": {\n \"pages\": 909,\n \"body_words\": 3698763,\n \"meta_words\": 214579,\n \"attr_words\": 9199,\n \"code_words\": 4,\n \"cjk_chars\": 3284717,\n \"chars_nospace\": 5728019,\n \"seg_total_words\": 3698763,\n \"seg_new_words\": 1909691,\n \"seg_repeated_words\": 1789072,\n \"distinct_segments\": 50553,\n \"dup_pages\": 5\n },\n \"sv\": {\n \"pages\": 940,\n \"body_words\": 1384442,\n \"meta_words\": 84824,\n \"attr_words\": 9250,\n \"code_words\": 4,\n \"cjk_chars\": 48812,\n \"chars_nospace\": 8414073,\n \"seg_total_words\": 1384442,\n \"seg_new_words\": 699797,\n \"seg_repeated_words\": 684645,\n \"distinct_segments\": 49580,\n \"dup_pages\": 5\n … [171 more characters]",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport json\ns=json.load(open('out/summary.json'))\npl=s['per_locale']\nprint('pages_total',s['pages_total'])\nprint('boilerplate segments (on >=50% of EN pages):',s['boilerplate_segments'])\nprint('median boilerplate words per EN page:',s['boiler_words_median_en'])\nprint()\nhdr=f\\\"{'loc':5}{'pages':>7}{'body_w':>11}{'meta_w':>9}{'alt_w':>8}{'net_new':>11}{'repeat':>11}{'rep%':>7}\\\"\nprint(hdr); print('-'*len(hdr))\ntot=dict(pages=0,body=0,meta=0,attr=0,new=0,rep=0)\nfor k,v in pl.items():\n rp=100*v['seg_repeated_words']/max(1,v['seg_total_words'])\n print(f\\\"{k:5}{v['pages']:>7}{v['body_words']:>11,}{v['meta_words']:>9,}{v['attr_words']:>8,}{v['seg_new_words']:>11,}{v['seg_repeated_words']:>11,}{rp:>6.1f}%\\\")\n tot['pages']+=v['pages']; tot['body']+=v['body_words']; tot['meta']+=v['meta_words']\n tot['attr']+=v['attr_words']; tot['new']+=v['seg_new_words']; tot['rep']+=v['seg_repeated_words']\nprint('-'*len(hdr))\nrp=100*tot['rep']/max(1,tot['rep']+tot['new'])\nprint(f\\\"{'ALL':5}{tot['pages']:>7}{tot['body']:>11,}{tot['meta']:>9,}{tot['attr']:>8,}{tot['new']:>11,}{tot['rep']:>11,}{rp:>6.1f}%\\\")\nprint()\nprint('TOP BOILERPLATE SEGMENTS (pages / text):')\nfor b in s['top_boilerplate'][:18]: print(f\\\" {b['pages']:>6} | {b['text'][:95]}\\\")\n\"",
"description": "Print full per-locale summary and boilerplate"
},
"output": "pages_total 10515\nboilerplate segments (on >=50% of EN pages): 976\nmedian boilerplate words per EN page: 742\n\nloc pages body_w meta_w alt_w net_new repeat rep%\n---------------------------------------------------------------------\nen 1152 1,950,503 104,710 15,216 866,851 1,083,652 55.6%\npt 953 1,580,758 98,017 9,211 808,472 772,286 48.9%\nes 954 1,785,074 101,063 9,218 873,363 911,711 51.1%\nfr 954 1,786,491 103,967 9,249 883,441 903,050 50.5%\nde 955 1,524,224 86,418 9,202 766,985 757,239 49.7%\nko 901 3,210,656 183,787 10,802 1,685,680 1,524,976 47.5%\nit 941 1,653,893 96,309 9,269 788,956 864,937 52.3%\nar 930 1,403,453 78,016 9,255 654,063 749,390 53.4%\nzh 926 2,361,872 140,516 9,191 1,149,457 1,212,415 51.3%\nja 909 3,698,763 214,579 9,199 1,909,691 1,789,072 48.4%\nsv 940 1,384,442 84,824 9,250 699,797 684,645 49.5%\n---------------------------------------------------------------------\nALL 10515 22,340,1291,292,206 109,062 11,086,756 11,253,373 50.4%\n\nTOP BOILERPLATE SEGMENTS (pages / text):\n 9575 | Svenska\n 9575 | 日本語\n 9575 | Français\n 9575 | العربية\n 9575 | Deutsch\n 9575 | For Creative Teams\n 9575 | NEW\n 9575 | English\n 9575 | Italiano\n 9575 | Sous-Chef\n 9575 | 中文\n 9575 | AEM Translation\n 9575 | Português\n 9575 | Español\n 9575 | Adapt cam … [361 more characters]",
"error": false
},
{
"say": "Now the English (source) detail, which is what actually drives a quote:"
},
{
"tool": "Write",
"input": {
"file_path": "",
"content": "#!/usr/bin/env python3\n\"\"\"English-source detail: sections, boilerplate cost, net-new words.\"\"\"\nimport os, sys, json, csv, collections, hashlib\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\nimport extract\nfrom urllib.parse import urlsplit\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nRAW = os.path.join(HERE, \"raw\")\nOUT = os.path.join(HERE, \"out\")\n\nLOCALES = {\"pt\", \"es\", \"fr\", \"de\", \"ko\", \"it\", \"ar\", \"zh\", \"ja\", \"sv\"}\n\n\ndef h8(s):\n return hashlib.blake2b(s.encode(\"utf-8\"), digest_size=8).digest()\n\n\nres = json.load(open(os.path.join(HERE, \"crawl_results.json\")))\nen = []\nfor u, v in sorted(res.items()):\n if not (v.get(\"html\") and v.get(\"status\") == 200 and v.get(\"path\")):\n continue\n p = urlsplit(u).path.strip(\"/\")\n if p.split(\"/\")[0] in LOCALES:\n continue\n en.append((u, v[\"path\"]))\nprint(f\"English pages: {len(en)}\")\n\npages = []\nseg_pages = collections.Counter()\nsample = {}\nfor u, f in en:\n d = extract.parse(open(os.path.join(RAW, f), \"rb\").read())\n vis = [b for b, hid in d.blocks if not hid]\n segs = []\n for b in vis:\n for s in extract.segments(b):\n n = extract.norm_seg(s)\n if not n:\n continue\n w = extract.count_text(s)[0]\n if w:\n hh = h8(n)\n segs.append((hh, w))\n sample.setdefault(hh, s[:200])\n for hh in {h for h, _ in segs}:\n seg_pages[hh] += 1\n meta_txt = \" \".join([d.title.strip()] + list(d.meta.values()))\n pages.append({\n \"url\": u,\n \"path\": urlsplit(u).path,\n \"title\": d.title.strip()[:120],\n \"body_words\": extract.count_text(\"\\n\".join(vis))[0] if vis else 0,\n \"meta_words\": extract.count_text(meta_txt)[0] if meta_txt.strip() else 0,\n \"attr_words\": extract.count_text(\n \" \".join(t for _, t in d.attr_items))[0] if d.attr_items else 0,\n \"segs\": segs,\n })\n\nN = len(pages)\nBOIL_T = 0.5 * N\nboiler = {hh for hh, c in seg_pages.items() if c >= BOIL_T}\n\n# per-page boilerplate vs unique\nfor p in pages:\n p[\"boiler_words\"] = sum(w for hh, w in p[\"segs\"] if hh in boiler)\n p[\"unique_words\"] = p[\"body_words\"] - p[\"boiler_words\"]\n\n\ndef section(path):\n p = path.strip(\"/\")\n if not p:\n return \"home\"\n parts = p.split(\"/\")\n if parts[0] == \"blog\":\n return \"blog-post\" if len(parts) > 1 else \"blog-index\"\n if parts[0] in (\"industries\", \"services\", \"authors\"):\n return parts[0]\n return \"marketing/other\"\n\n\nsec_pages = collections.Counter()\nsec_body = collections.Counter()\nsec_uniq = collections.Counter()\nfor p in pages:\n s = section(p[\"path\"])\n sec_pages[s] += 1\n sec_body[s] += p[\"body_words\"]\n sec_uniq[s] += p[\"unique_words\"]\n\n# CAT-style running dedup (page order = url sort)\nseen = set()\nnew = rep = 0\nfor p in pages:\n for hh, w in p[\"segs\"]:\n if hh in seen:\n rep += w\n else:\n seen.add(hh)\n new += w\n\nbody = sum(p[\"body_words\"] for p in pages)\nmeta = sum(p[\"meta_words\"] for p in pages)\nattr = sum(p[\"attr_words\"] for p in pages)\nboil_total = sum(p[\"boiler_words\"] for p in pages)\nuniq_total = sum(p[\"unique_words\"] for p in pages)\n\nprint(f\"\"\"\n=== ENGLISH SOURCE TOTALS ===\npages {N:>12,}\nbody words (gross) {body:>12,}\n of which boilerplate {boil_total:>12,} ({100*boil_total/body:.1f}%)\n page-unique content {uniq_total:>12,} ({100*uniq_total/body:.1f}%)\nmeta (title+description) {meta:>12,}\nimage alt / aria / labels {attr:>12,}\nGROSS TOTAL (all strings) {body+meta+attr:>12,}\n\nCAT-style dedup over body text:\n new (unique) words {new:>12,}\n repetitions {rep:>12,} ({100*rep/body:.1f}%)\n distinct segments {len(seen):>12,}\n boilerplate segments {len(boiler):>12,}\n median boiler words/page {sorted(p['boiler_words'] for p in pages)[N//2]:>12,}\n\"\"\")\n\nprint(\"=== BY SECTION (English) ===\")\nprint(f\"{'section':20}{'pages':>7}{'gross words':>14}{'unique words':>14}{'avg uniq/pg':>13}\")\nfor s, c in sec_pages.most_common():\n print(f\"{s:20}{c:>7}{sec_body[s]:>14,}{sec_uniq[s]:>14,}\"\n f\"{sec_uniq[s]//max(1,c):>13,}\")\n\nprint(\"\\n=== TOP 15 ENGLISH PAGES BY UNIQUE CONTENT ===\")\nfor p in sorted(pages, key=lambda x: -x[\"unique_words\"])[:15]:\n print(f\" {p['unique_words']:>6,} uniq / {p['body_words']:>6,} gross {p['path'][:70]}\")\n\nprint(\"\\n=== 15 THINNEST ENGLISH PAGES (unique content) ===\")\nfor p in sorted(pages, key=lambda x: x[\"unique_words\"])[:15]:\n print(f\" {p['unique_words']:>6,} uniq / {p['body_words']:>6,} gross {p['path'][:70]}\")\n\nwith open(os.path.join(OUT, \"english_pages.csv\"), \"w\", newline=\"\") as f:\n wr = csv.DictWriter(f, fieldnames=[\"url\", \"path\", \"title\", \"body_words\",\n \"boiler_words\", \"unique_words\",\n \"meta_words\", \"attr_words\"],\n extrasaction=\"ignore\")\n wr.writeheader()\n for p in sorted(pages, key=lambda x: -x[\"unique_words\"]):\n wr.writerow(p)\n\njson.dump({\"pages\": N, \"body\": body, \"meta\": meta, \"attr\": attr,\n \"boiler\": boil_total, \"unique\": uniq_total,\n \"cat_new\": new, \"cat_rep\": rep,\n \"distinct_segments\": len(seen),\n \"sections\": {s: {\"pages\": sec_pages[s], \"gross\": sec_body[s],\n \"unique\": sec_uniq[s]} for s in sec_pages}},\n open(os.path.join(OUT, \"english_summary.json\"), \"w\"), indent=1)\nprint(\"\\nwrote out/english_pages.csv + out/english_summary.json\")\n"
},
"output": "File created successfully at: (file state is current in your context — no need to Read it back)",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 report_en.py 2>&1 | tail -70",
"description": "Generate English source detail report",
"timeout": 600000
},
"output": "English pages: 1152\n\n=== ENGLISH SOURCE TOTALS ===\npages 1,152\nbody words (gross) 1,950,503\n of which boilerplate 678,013 (34.8%)\n page-unique content 1,272,490 (65.2%)\nmeta (title+description) 104,710\nimage alt / aria / labels 15,216\nGROSS TOTAL (all strings) 2,070,429\n\nCAT-style dedup over body text:\n new (unique) words 866,851\n repetitions 1,083,652 (55.6%)\n distinct segments 61,485\n boilerplate segments 120\n median boiler words/page 674\n\n=== BY SECTION (English) ===\nsection pages gross words unique words avg uniq/pg\nblog-post 935 1,748,819 1,145,325 1,224\nmarketing/other 179 147,218 96,327 538\nauthors 26 36,882 20,298 780\nindustries 7 9,393 5,220 745\nservices 3 5,418 3,643 1,214\nhome 1 1,837 1,237 1,237\nblog-index 1 936 440 440\n\n=== TOP 15 ENGLISH PAGES BY UNIQUE CONTENT ===\n 10,348 uniq / 11,002 gross /blog/ai-statistics-500-facts-driving-global-innovation\n 6,441 uniq / 7,112 gross /blog/what-is-translation-memory-tm\n 5,043 uniq / 5,721 gross /blog/websites-and-translation-in-the-agent-era\n 4,424 uniq / 4,529 gross /merging-minds\n 4 … [1,800 more characters]",
"error": false
},
{
"say": "A few pages came back with 0 words — I need to confirm they're genuinely empty and not JS-rendered content I'm missing."
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport json\nr=json.load(open('crawl_results.json'))\nfor u in ['https://wxrks.com/chat','https://wxrks.com/podcast','https://wxrks.com/support-us','https://wxrks.com/signup','https://wxrks.com/survey']:\n v=r.get(u,{})\n print(u, '->', v.get('status'), v.get('bytes'), 'final:', v.get('final_url'))\n\"",
"description": "Inspect zero-word pages metadata"
},
"output": "https://wxrks.com/chat -> 200 28853 final: https://wxrks.com/chat\nhttps://wxrks.com/podcast -> 200 28379 final: https://wxrks.com/podcast\nhttps://wxrks.com/support-us -> 200 29069 final: https://wxrks.com/support-us\nhttps://wxrks.com/signup -> 200 141466 final: https://wxrks.com/signup\nhttps://wxrks.com/survey -> 200 130327 final: https://wxrks.com/survey\nShell cwd was reset to ",
"error": false
},
{
"tool": "Bash",
"input": {
"command": "wc -c ",
"description": "Check file sizes of zero-word pages"
},
"output": " 28853 \n 28379 \n 29069 \n 86301 total",
"error": false
},
{
"say": "28KB of HTML but zero text — let me see what's actually in them."
},
{
"tool": "Bash",
"input": {
"command": "cd && python3 -c \"\nimport re\nfor f in ['raw/chat.html','raw/podcast.html']:\n s=open(f,encoding='utf-8',errors='replace').read()\n print('='*20,f,len(s))\n b=re.search(r']*>(.*)