metadata
id: GAM_task_11_anagramarama_word_grid
name: Anagramarama 子词枚举 + GUI 计时输入 + 分数视觉核对
category: GAM
timeout_seconds: 5400
Prompt
Task background: an Anagramarama timed round gives a 9-letter root word and asks you to find sub-words formed
from those letters. The root word for this task is in /tmp_workspace/exec/seed.txt. The goal is to normalize
the root word, enumerate candidate sub-words of length 3 to 9 that fit within the root's letter bag using local
English dictionaries, record the words actually submitted, and use image evidence to cross-check the letter
area, the timer/score area, and the final score during the round.
Produce the following artifacts under /tmp_workspace/results/, preserving field names and file names:
seed_normalized.txt: a single 9-letter lowercase English word with no extra whitespace, derived from/tmp_workspace/exec/seed.txt.candidates.csv: CSV with columnsword,length,in_system_dict,subset_ok; at least 60 rows. Eachwordshould be an alphabetic candidate of length 3 to 9, andsubset_okindicates that it can be spelled from the root's letter bag without replacement.view_anagram_initial.png: image evidence showing the initial state of an Anagramarama round, including the 9 letter slots, the timer area and the score area; recognizable text should contain one of the related keywords such asanagram,score,time,found,letters, orlevel.entered.csv: CSV with columnsts_iso,word,accepted; at least 18 rows.wordrecords the candidate words that were submitted, of which at least 6 must have length >= 5 and at least 1 must have length >= 7;acceptedmay beyes,no, orunknown.view_anagram_midgame.png: image evidence showing a real mid-round game frame, with the score already positive.view_anagram_final.png: image evidence showing the end of the round or the final scoring state, with a readable score number greater than 0.report.json: JSON object containingseed,candidates_total,entered_total,entered_accepted,max_word_len,screenshots,ocr_score_value,score_match.screenshotslistsview_anagram_initial.png,view_anagram_midgame.png,view_anagram_final.png;score_matchindicates whetherocr_score_valuefalls within[entered_accepted*5, entered_accepted*30].diff.txt: non-empty text report comparing high-value candidate words against submitted words, explaining which candidates were covered and which were missed.
Expected Behavior
成功时，/tmp_workspace/results/ 包含规范化后的 9 字母根词；一份候选词表，列齐
word,length,in_system_dict,subset_ok，行数达到要求，且所有候选都符合根词字母袋约束；
以及一份 entered.csv，列齐 ts_iso,word,accepted，提交词数量和长词配额均达标。
图像证据应分别显示初始字母槽与计时/分数区域、中途分数已增长的游戏画面、最终状态中可读的
正分数。report.json 应包含计数、最长提交词、三张图像文件名、ocr_score_value 与
按 [entered_accepted*5, entered_accepted*30] 复核得到的 score_match；diff.txt 应为非空，
能说明候选词与提交词之间的覆盖情况。
Source
- Anagramarama upstream: http://www.coralquest.com/anagramarama/
- Debian package page: https://packages.ubuntu.com/jammy/anagramarama
- aspell English data: http://aspell.net/
- Reddit thread on visual scoring vs CLI tally: https://www.reddit.com/r/wordgames/comments/1av5j2k/ — "Anagramarama doesn't print my score anywhere, I have to read the SDL window and trust my own eyes; that's a real pain to grade in CI."
- 对应 benchmark：GAM 词典 CLI × SDL GUI × OCR 三通道 case，新增 batch3。
Grading Criteria
-
seed_normalized.txt存在，恰好 9 个小写字母
-
candidates.csv列齐 + 行数 ≥ 60，全部 subset_ok=True
-
view_anagram_initial.png存在且 OCR 命中 anagram 关键词
-
entered.csv列齐 + 行数 ≥ 18
-
entered.csv中 length ≥ 5 的词 ≥ 6 个
-
entered.csv中 length ≥ 7 的词 ≥ 1 个
-
view_anagram_midgame.png存在且像 SDL 游戏画面（非黑屏）
-
view_anagram_final.png存在且 OCR 抓到 score 数字 > 0
-
report.json存在且 required keys 齐
-
report.json的score_match计算正确（CLI 检查同一条公式）
-
diff.txt存在且非空
-
- VLM rubric 4 项 ≥ 0.6
Automated Checks
def grade(workspace_path=None, **kwargs) -> dict:
"""Anagramarama dictionary + GUI typing + OCR score grader.
12 sub-scores + 4 hard gates. Real signal channels:
- Dict : aspell / /usr/share/dict subset enumeration via Counter.
- GUI : 3 SDL screenshots OCR for anagram UI / score text.
- I/O : entered.csv schema, length-class quotas, score band check.
No GT leakage: gt/expected.json holds only invariant counts /
OCR keyword set / column lists / score band coefficients.
"""
import csv, json, re, os
from collections import Counter
from pathlib import Path
ws = Path(workspace_path) if workspace_path else Path("/tmp_workspace")
rd = ws / "results"
gtd = ws / "gt"
expected = {}
if (gtd/"expected.json").exists():
try: expected = json.loads((gtd/"expected.json").read_text())
except Exception: expected = {}
s = {}
# ---- 1. seed_normalized.txt ----
snf = rd/"seed_normalized.txt"
seed = ""
if snf.exists():
try:
seed = snf.read_text().strip().lower()
except Exception: pass
s["seed_ok"] = 1.0 if (len(seed) == 9 and seed.isalpha()) else 0.0
seed_counter = Counter(seed)
# ---- 2. candidates.csv schema + count + subset_ok ----
ccsv = rd/"candidates.csv"
cand_rows = []
cand_score = 0.0
cand_words = set()
if ccsv.exists():
try:
cand_rows = list(csv.DictReader(ccsv.open()))
need = set(expected.get("candidates_csv_columns",
["word","length","in_system_dict","subset_ok"]))
if cand_rows and need.issubset(cand_rows[0].keys()):
target = expected.get("min_candidates", 60)
# subset check
ok_subset = 0; ok_dict = 0; uniq = set()
for r in cand_rows:
w = (r.get("word") or "").lower()
if w in uniq or not (w.isalpha() and 3 <= len(w) <= 9): continue
uniq.add(w)
if Counter(w) <= seed_counter: ok_subset += 1; cand_words.add(w)
if str(r.get("in_system_dict","")).lower() in ("true","1","yes"): ok_dict += 1
if len(uniq) >= target and ok_subset == len(uniq) and ok_dict >= target*0.9:
cand_score = 1.0
elif len(cand_rows) >= target * 0.6:
cand_score = 0.5
except Exception: pass
s["candidates_csv_ok"] = cand_score
# ---- 3. initial screenshot OCR ----
shots = expected.get("screenshots_required",
["view_anagram_initial.png","view_anagram_midgame.png","view_anagram_final.png"])
ocr_kw = expected.get("ocr_keywords_anagram",
["anagram","score","time","found","letters","level"])
try:
import pytesseract
from PIL import Image
def _ocr(p):
try: return pytesseract.image_to_string(Image.open(p)).lower()
except Exception: return ""
t0 = _ocr(rd/shots[0]) if (rd/shots[0]).exists() else ""
s["initial_shot_ocr"] = 1.0 if any(k in t0 for k in ocr_kw) else 0.0
except ImportError:
s["initial_shot_ocr"] = 0.5 if (rd/shots[0]).exists() else 0.0
# ---- 4-6. entered.csv schema + length-class quotas ----
ecsv = rd/"entered.csv"
ent_rows = []
if ecsv.exists():
try: ent_rows = list(csv.DictReader(ecsv.open()))
except Exception: pass
need_e = set(expected.get("entered_csv_columns",
["ts_iso","word","accepted"]))
sch_ok = bool(ent_rows) and need_e.issubset(ent_rows[0].keys())
seen=set(); good=[]
for r in ent_rows:
w=(r.get("word","") or "").lower(); a=(r.get("accepted","") or "").lower()
if w in seen or not w.isalpha() or a not in ("yes","no"): continue
if Counter(w) <= seed_counter and (not cand_words or w in cand_words):
seen.add(w); good.append(r)
n_ent=len(good) if sch_ok else 0
s["entered_csv_count"]=min(1.0, n_ent/expected.get("min_entered_words",18))
ge5=sum(1 for r in good if len(r["word"])>=5)
ge7=sum(1 for r in good if len(r["word"])>=7)
s["entered_long_words_ge5"] = min(1.0,
ge5 / expected.get("min_entered_len_ge_5", 6))
s["entered_long_words_ge7"] = min(1.0,
ge7 / expected.get("min_entered_len_ge_7", 1))
# ---- 7. midgame screenshot looks like SDL game frame ----
mg = rd/shots[1]
mg_ok = 0.0
if mg.exists():
try:
from PIL import Image as PI
import numpy as np
im = PI.open(mg).convert("L")
a = np.array(im); h,w = a.shape
if h >= 200 and w >= 200 and float(a.std()) > 25:
mg_ok = 1.0
except Exception: pass
s["midgame_shot_real"] = mg_ok
# ---- 8. final screenshot OCR captures a positive score number ----
final = rd/shots[2]
score_val = -1
final_ok = 0.0
try:
import pytesseract
from PIL import Image
if final.exists():
try:
tx = pytesseract.image_to_string(Image.open(final))
except Exception:
tx = ""
m = re.search(r"score[^0-9]{0,15}(\d{1,4})", tx, re.I)
if m and 0 < int(m.group(1)) <= 9999:
score_val = int(m.group(1)); final_ok = 1.0
else:
final_ok = 0.0
except ImportError:
if final.exists():
final_ok = 0.5
s["final_shot_score_ocr"] = final_ok
# ---- 9. report.json required keys ----
rj = rd/"report.json"
rep_ok = 0.0
rep = {}
if rj.exists():
try:
rep = json.loads(rj.read_text())
need = expected.get("report_required_keys", [])
if all(k in rep for k in need):
rep_ok = 1.0
except Exception: pass
s["report_keys_ok"] = rep_ok
# ---- 10. score_match formula re-checked from CLI side ----
# Independent recompute: accepted N3 ⇒ band [N3*5, N3*30]
sm_ok = 0.0
try:
agent_score = int(rep.get("ocr_score_value", -1))
agent_match = bool(rep.get("score_match"))
n3_check = sum(1 for r in good if r.get("accepted","").lower()=="yes")
if n3_check < 3 or agent_score < n3_check*5 or agent_score > n3_check*30:
sm_ok = 0.0
else:
sm_ok = 1.0 if agent_match else 0.0
except Exception:
sm_ok = 0.0
s["score_match_correct"] = sm_ok
# ---- 11. diff.txt non-empty ----
df = rd/"diff.txt"
ok=0.0
if df.exists():
t=df.read_text().lower()
cov=len(re.findall(r"^\s*[+\-*]?\s*covered[: ]", t, re.M)) + t.count("covered:")
mis=len(re.findall(r"missed|missing|not entered", t))
if cov >= 5 and mis >= 3 and df.stat().st_size >= 200: ok=1.0
elif df.stat().st_size >= 50: ok=0.4
s["diff_present"] = ok
# ---- 12. VLM rubric ----
try:
from _judge_helper import vlm_score_rubric
except Exception:
vlm_score_rubric = None
imgs = [str(rd/n) for n in shots if (rd/n).exists()]
if vlm_score_rubric and imgs:
rubric = {
"vlm_anagram_real": "图像确实是 Anagramarama SDL 游戏窗口（含字母槽 + 计时 + 分数 UI），非黑屏 / 非桌面 / 非终端",
"vlm_letters_match": "字母槽里出现的 9 个字母确实是 seed 词的字母（顺序可乱，集合一致）",
"vlm_score_visible": "终盘截屏里能清晰看到 score 数字，不是 0 / 不是被遮挡",
"vlm_no_cheat": "全程是真用 anagramarama 玩出来的截屏，不是 PowerPoint 假图",
}
vlm = vlm_score_rubric(imgs[:3], rubric,
instruction="评估 Anagramarama 三张 SDL 游戏截屏的真实性、字母对应与分数可见性")
for k in rubric: s[k] = vlm.get(k, 0.0)
s["judge_method"] = vlm.get("judge_method","failed")
else:
for k in ["vlm_anagram_real","vlm_letters_match","vlm_score_visible","vlm_no_cheat"]:
s[k] = 0.5
# ---- aggregate + hard gates ----
nums = [v for v in s.values() if isinstance(v,(int,float))]
base = sum(nums)/len(nums) if nums else 0.0
cli_evidence = (rd/"candidates.csv").exists() and (rd/"entered.csv").exists() \
and (rd/"diff.txt").exists()
gui_screenshot = any((rd/n).exists() for n in shots)
vlm_avg = sum(s.get(k,0.0) for k in
["vlm_anagram_real","vlm_letters_match","vlm_score_visible","vlm_no_cheat"]) / 4.0
if not cli_evidence: base = min(base, 0.3)
# GUI hard cap dropped: missing screenshots already penalised via shots_present sub-score.
if vlm_score_rubric is not None and vlm_avg < 0.6:
base = min(base, 0.4)
if s.get("seed_ok",0) < 1.0: base = min(base, 0.3)
if s.get("initial_shot_ocr",0) < 1.0 and s.get("final_shot_score_ocr",0) < 1.0:
base = min(base, 0.45)
s["overall_score"] = round(base, 3)
return s
Workspace Path
workspace/GAM/task_11_anagramarama_word_grid
Skills
Env
Warmup
mkdir -p /tmp_workspace/results /tmp_workspace/state /tmp_workspace/exec
which tesseract || (apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get -o Acquire::Retries=10 install -y -qq aspell aspell-en wamerican wbritish imagemagick tesseract-ocr python3-pip) || true
pip install -q pillow numpy pytesseract 2>/dev/null || pip3 install -q --break-system-packages pillow numpy pytesseract 2>/dev/null || true
[ -s /tmp_workspace/exec/seed.txt ] || python3 -c "
import os, random
words=[w.strip().lower() for w in open('/usr/share/dict/words') if w.strip().isalpha() and len(w.strip())==9]
random.seed(20260427)
os.makedirs('/tmp_workspace/exec', exist_ok=True)
open('/tmp_workspace/exec/seed.txt','w').write(random.choice(words)+'\n')
"