FM1-375怎么练 FM1到375怎么练最便宜
\u6c42\u4e00\u4e2aFM1--375\u7684\u529f\u7565\u9996\u5148\u58f0\u660e \u672c\u5e16\u662f\u8f6c\u7684\uff01 \u4f46\u662f\u53d1\u5e16\u4eba\u5199\u7684\u4e0d\u591f\u8be6\u7ec6\uff0c\u6211\u7528\u6b64\u65b9\u6cd5\u51b2\u5230375\u540e \u8865\u5145\u4e0b\u7ecf\u9a8c\u5fc3\u5f97\uff01\uff01\u5e0c\u671b\u80fd\u5e2e\u52a9\u60f3\u8981\u5b66\u9644\u9b54\u7684\u670b\u53cb\uff01
\u57fa\u672c\u8fc7\u7a0b\uff1a
0\u201440 \u5206\u89e3
40\u201475 [\u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u751f\u547d] = 1 x \u5947\u5f02\u4e4b\u5c18
75 - 85 [\u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u504f\u659c] =1 x [\u6b21\u7ea7\u9b54\u6cd5\u7cbe\u534e], 1 x [\u5947\u5f02\u4e4b\u5c18]
85 - 100 [\u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u8010\u529b] = 3 x [\u5947\u5f02\u4e4b\u5c18]
101 -105 [\u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u8010\u529b] = 3 x [\u5947\u5f02\u4e4b\u5c18]
105 - 120
\u3010[\u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u654f\u6377] = 2 x [\u5947\u5f02\u4e4b\u5c18], 1 x [\u5f3a\u6548\u9b54\u6cd5\u7cbe\u534e]\u3011 x 15
120 - 130
\u3010[\u9644\u9b54\u76fe\u724c - \u521d\u7ea7\u8010\u529b] = 1 x [\u6b21\u7ea7\u661f\u754c\u7cbe\u534e] , 2 x [\u5947\u5f02\u4e4b\u5c18]\u3011 x 10
\u8865\u5145\uff1a \u5947\u5f02\u4e4b\u5c18 \u53ef\u4ee5\u5927\u91cf\u6536\u8d2d \u4e9a\u9ebb\u5e03+\u6bdb\u6599 \u81ea\u5df1\u662f\u88c1\u7f1d\u5c31\u597d\u529e\u4e86 \u5982\u679c\u4e0d\u662f\uff0c\u90a3\u5c31\u8bf7\u4f1a\u91cc\u7684\u4eba\u5e2e\u5fd9\u505a\u4e00\u4e9b\u7eff\u88c5\u81ea\u5df1\u62c6\u5427\uff01
130 - 150
\u3010[\u9644\u9b54\u62a4\u8155 - \u6b21\u7ea7\u8010\u529b] = 2 x [\u7075\u9b42\u4e4b\u5c18] \u3011 x 25
150 - 151
\u3010[\u7b26\u6587\u91d1\u68d2] = 1 x [\u91d1\u68d2], 1 x [\u5f69\u8272\u73cd\u73e0], 2 x [\u5f3a\u6548\u661f\u754c\u7cbe\u534e], 2 x [\u7075\u9b42\u4e4b\u5c18]\u3011 x 1
151 - 160
\u3010[\u9644\u9b54\u62a4\u8155 - \u6b21\u7ea7\u8010\u529b] = 2 x [\u7075\u9b42\u4e4b\u5c18]\u3011 x 9
160 - 165
\u3010[\u9644\u9b54\u76fe\u724c - \u6b21\u7ea7\u8010\u529b] = 1 x [\u6b21\u7ea7\u79d8\u6cd5\u7cbe\u534e], 1 x [\u7075\u9b42\u4e4b\u5c18]\u3011 x 5
165 - 180
\u3010[\u9644\u9b54\u62a4\u8155 - \u7cbe\u795e] = 1 x [\u6b21\u7ea7\u79d8\u6cd5\u7cbe\u534e]\u3011 x 15
\u8865\u5145\uff1a \u8fd9\u91cc\u6ca1\u4ec0\u4e48\u8bf4\u7684\uff0c\u8fd8\u662f\u6536\u5e03\u505a\u4e1c\u897f\u62c6\uff01
180 - 200
\u3010[\u9644\u9b54\u62a4\u8155 - \u529b\u91cf] = 1 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 20
200 - 201
\u3010[\u7b26\u6587\u771f\u94f6\u68d2] = 1 x [\u771f\u94f6\u68d2], 1 x [\u9ed1\u73cd\u73e0], 2 x [\u5f3a\u6548\u79d8\u6cd5\u7cbe\u534e], 2 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 1
201 - 205
\u3010[\u9644\u9b54\u62a4\u8155 - \u529b\u91cf] = 1 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 4
205 - 225
\u3010[\u9644\u9b54\u62ab\u98ce - \u5f3a\u6548\u9632\u5fa1] = 3 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 20
225 - 235
\u3010[\u9644\u9b54\u624b\u5957 - \u654f\u6377] = 1 x [\u6b21\u7ea7\u865a\u7a7a\u7cbe\u534e], 1 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 10
235 - 245
\u3010[\u9644\u9b54\u80f8\u7532 - \u8d85\u5f3a\u751f\u547d] = 6 x [\u5e7b\u8c61\u4e4b\u5c18]\u3011 x 10
\u8865\u5145\uff1a \u8fd9\u91cc\u91cd\u70b9\u5f3a\u8c03\u4e00\u4e0b \u5e7b\u8c61\u4e4b\u5c18 \u5443... \u4e07\u6076\u768440+\u88c5\u5907\uff01 \u8d35\u7684\u8981\u6b7b\u7684\u9b54\u7eb9\u5e03
\u5982\u679c\u81ea\u5df1\u662fQS \u5c31\u4e00\u4e2a\u4eba\u53bb\u5355\u5237ZUL\u5427 \u4e00\u5c0f\u65f6\u597d\u591a\u7ec4 \uff01 \u7136\u540e\u6700\u597d\u7684\u529e\u6cd5\u5c31\u662f\u505a \u56fe\u6837\uff1a\u767d\u8272\u5f3a\u76d7\u9762\u7f69
\u5982\u679c\u6ca1\u6709\uff0c \u90a3\u5c31\u53ea\u80fd\u5237ZUL\u6216\u8005AH\u4e86 =\u3002=# \u6211\u5c31\u662f\u76f4\u63a5\u5728AH \u4e70\u7684\uff01
245 - 265
\u3010[\u9644\u9b54\u62a4\u8155 - \u5f3a\u6548\u529b\u91cf] = 2 x [\u68a6\u5883\u4e4b\u5c18], 1 x [\u5f3a\u6548\u865a\u7a7a\u7cbe\u534e]\u3011 x 20
265 - 300
\u3010[\u9644\u9b54\u76fe\u724c - \u5f3a\u6548\u8010\u529b] = 10 x [\u68a6\u5883\u4e4b\u5c18]\u3011 x 25
\u3010[\u7b26\u6587\u5965\u91d1\u68d2] = 1 x [\u5965\u91d1\u68d2], 1 x [\u91d1\u73cd\u73e0], 10 x [\u5e7b\u5f71\u4e4b\u5c18], 4 x [\u5f3a\u6548\u4e0d\u706d\u7cbe\u534e], 4 x [\u5c0f\u5757\u9b54\u5149\u788e\u7247], 2 x [\u5927\u5757\u9b54\u5149\u788e\u7247]\u3011 x 1
\u8865\u5145\uff1a \u8fd9\u91cc\u91cd\u70b9\u8bf4\u4e00\u4e0b \u9644\u9b54\u76fe\u724c - \u5f3a\u6548\u8010\u529b \u8d2d\u4e70\u5730\u70b9\uff01 \u6b64\u914d\u65b9\u662f\u7ed1\u5b9a\u7684\uff01
LM\u662f\u5728 \u7cbe\u7075\u57ce \u6218\u58eb\u533a\u5f80\u5de6\u7b2c\u4e8c\u4e2a\u623f\u95f4 \uff08\u5546\u4eba-\u8fc8\u65af\u6797\u8fea\u5c14\uff09\u5904\u8d2d\u4e70 \uff01 \u4e00\u5929\u5237\u65b0\u4e00\u6b21 \u5728\u6700\u540e\u4e00\u9875\u91cc\u9762 \u5982\u679c\u6ca1\u6709\u5c31\u8868\u793a\u88ab\u4eba\u4e70\u8d70\u4e86\uff01
BL\u662f\u5728 \u5e7d\u6697\u57ce 64.2 37.7 \u4e39\u5c3c\u5c14\u00b7\u5df4\u7279\u83b1\u7279 \u5546\u4eba\u8d2d\u4e70\uff01 \uff08\u672c\u4eba\u662f\u73a9LM \u6240\u4ee5\u53ea\u80fd\u7ed9\u5904BL\u8d2d\u4e70\u7684 \u5750\u6807\uff09
300 - 301
\u7b26\u6587\u6076\u9b54\u68d2 (1 x \u9b54\u94c1\u68d2, 4 x \u5f3a\u6548\u4e0d\u706d\u7cbe\u534e, 6 x \u5927\u9b54\u5149\u788e\u7247, 1 x \u7b26\u6587\u5965\u91d1\u68d2) x 1
301 - 305
\u4ed8\u9b54\u62ab\u98ce - \u8d85\u5f3a\u9632\u5fa1 (8 x \u5e7b\u5f71\u4e4b\u5c18) x 7
305 - 315
\u4ed8\u9b54\u62a4\u8155 - \u88ad\u51fb/\u8155\u529b (6 x \u5965\u672f\u4e4b\u5c18) x 10
315 - 325
\u4ed8\u9b54\u62ab\u98ce - \u6781\u6548\u62a4\u7532 \u6216 \u4ed8\u9b54\u624b\u5957 - \u88ad\u51fb (8 x \u5965\u672f\u4e4b\u5c18) x 10
325 - 335
\u4ed8\u9b54\u80f8\u7532 - \u6781\u6548\u7cbe\u795e (2 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 10
335 - 340
\u4ed8\u9b54\u76fe\u724c - \u6781\u6548\u8010\u529b (15 x \u5965\u672f\u4e4b\u5c18) x 5
340 - 345
\u8d85\u5f3a\u5deb\u5e08\u6cb9 (3 x \u5965\u672f\u4e4b\u5c18, 1 x \u6076\u68a6\u85e4, 1 x \u6d78\u900f\u8bd5\u74f6) x 10
345 - 350
\u4ed8\u9b54\u624b\u5957 - \u6781\u6548\u529b\u91cf (12 x \u5965\u672f\u4e4b\u5c18, 1 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 5
350 - 351
\u7b26\u6587\u575a\u68d2 (1 x \u575a\u94a2\u68d2, 8 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e, 8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 1 x \u539f\u59cb\u529b\u91cf, 1 x \u7b26\u6587\u9b54\u94c1\u68d2) x 1
351 - 360
\u4ed8\u9b54\u624b\u5957 - \u6781\u6548\u529b\u91cf (12 x \u5965\u672f\u4e4b\u5c18, 1 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 15
360 - 370
\u4ed8\u9b54\u6212\u6307 - \u6253\u51fb (8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 24 x \u5965\u672f\u4e4b\u5c18) x 10 (\u9700\u8981\u865a\u7a7a\u8d22\u56e2\u58f0\u671b\u5d07\u656c\uff0c\u914d\u65b9\u540d\u53eb\u4ed8\u9b54\u6212\u6307\uff1a\u6b66\u5668\u529b\u91cf\uff0c\u4e0d\u8fc7\u4ed8\u9b54\u540d\u5b57\u53eb\u6253\u51fb)
370 - 375
\u4ed8\u9b54\u6212\u6307 - \u6cbb\u7597\u529b\u91cf (8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 10 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e, 20 x \u5965\u672f\u4e4b\u5c18) x 5 (\u6c99\u5c14\u5854\u5d07\u656c)
\u8865\u5145\uff1a \u7b49\u4f60\u51b2\u5230\u8fd9\u91cc\u7684\u65f6\u5019 \u76f8\u4fe1\u4e5f\u6ca1\u4ec0\u4e48\u53ef\u8bf4\u7684\u4e86 \u5982\u679c\u94b1\u591a\u5c31\u76f4\u63a5\u51b2\u4e0b\u6765 \u5982\u679c\u60f3\u7701\u94b1 \u5c31\u4ece305\u5f00\u59cb
\u8d54\u672c\u5728\u4e3b\u57ce\u558a \u4ed8\u9b54\u62a4\u8155 - \u88ad\u51fb/\u8155\u529b \u7b49\u7b49 \u8fd9\u6837\u53ef\u4ee5\u8282\u7701\u4e00\u4e9b\u94b1\uff01 \u5e0c\u671b\u80fd\u5e2e\u52a9\u5230\u5927\u5bb6\uff01
\u8865\u5145\u4e00\u4e0b
\u5173\u4e8e \u5f3a\u6548\u865a\u7a7a\u7cbe\u534e\uff01 \u8fd9\u4e2a\u505a\u767d\u8272\u5f3a\u76d7 \u662f\u53ef\u4ee5\u62c6\u51fa\u6765\u7684\uff01 \u81f3\u4e8e\u51e0\u7387\u561b \u5c31\u8981\u770bRP\u4e86\uff01
\u6211\u662f\u76f4\u63a5\u5728AH\u6536\u7684 \u5927\u6982300G\u4e00\u7ec4\uff01 \u56e0\u4e3a\u7528\u7684\u4e0d\u662f\u5f88\u591a \u6700\u591a\u4e5f\u5c31\u4e0d\u52302\u7ec4\uff01 \u6240\u4ee5\u6211\u5c31\u76f4\u63a5\u5728AH\u4e70\u4e86\uff01
\u6765\u6e90\uff1aNGA \u4f5c\u8005: \u9f99\u9038\u6d77\u6d0b
15 \u9644\u9b54\u80f8\u7532 - \u521d\u7ea7\u751f\u547d \u5947\u5f02\u4e4b\u5c1850 \u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u8010\u529b \u5947\u5f02\u4e4b\u5c18 (3)80 \u9644\u9b54\u62a4\u8155 - \u521d\u7ea7\u529b\u91cf \u5947\u5f02\u4e4b\u5c18 5 (\u8981\u5728AH\u4e70\u914d\u65b9)125 \u9644\u9b54\u9774\u5b50 - \u521d\u7ea7\u8010\u529b \u5947\u5f02\u4e4b\u5c18 (8)140 \u9644\u9b54\u6b66\u5668 - \u6b21\u7ea7\u653b\u51fb \u7075\u9b42\u4e4b\u5c18 (2)155 \u9644\u9b54\u62ab\u98ce - \u9632\u5fa1 \u7075\u9b42\u4e4b\u5c18 (3)170 \u9644\u9b54\u9774\u5b50 - \u6b21\u7ea7\u8010\u529b \u7075\u9b42\u4e4b\u5c18 (4)180 \u9644\u9b54\u62a4\u8155 - \u529b\u91cf \u5e7b\u8c61\u4e4b\u5c18210 \u9644\u9b54\u624b\u5957 - \u654f\u6377 \u5e7b\u8c61\u4e4b\u5c18225 \u9644\u9b54\u624b\u5957 - \u529b\u91cf \u5e7b\u8c61\u4e4b\u5c18 (3)250 \u9b54\u5316\u745f\u94f6 \u745f\u94f6\u952d, \u68a6\u5883\u4e4b\u5c18 (3)245 \u9644\u9b54\u62a4\u8155 - \u5f3a\u6548\u8010\u529b \u68a6\u5883\u4e4b\u5c18 5265 \u9644\u9b54\u76fe\u724c - \u5f3a\u6548\u8010\u529b \u5e7b\u8c61\u4e4b\u5c18 10285 \u9644\u9b54\u62ab\u98ce - \u8d85\u5f3a\u9632\u5fa1 \u5e7b\u5f71\u4e4b\u5c18 8290\u7684\u65f6\u5019\u4f60\u53bb\u6708\u5149\u6797\u5730\u90a3\u4e2a\u6c38\u591c\u6e2f\uff0c\u8bb0\u5f97\u4e70\u9644\u9b54\u5965\u91d1\u68d2\u7684\u914d\u65b9\uff0c\u5fc5\u5907\u54e6\uff0c299\u7684\u65f6\u5019\u7528\u8fd9\u4e2a\u51b2\u4e0a\u53bb\u6700\u540e\u4e00\u70b9\u5427\u90a3\u91cc\u8fd8\u53ef\u4ee5\u4e70\u5230\u4e00\u4e2a\u62ab\u98ce70\u62a4\u7532\u7684\u914d\u65b9\uff08285 \u9644\u9b54\u62ab\u98ce - \u8d85\u5f3a\u9632\u5fa1 \u5e7b\u5f71\u4e4b\u5c18 8 \uff09300 - 301 \u3000\u3000\u7b26\u6587\u6076\u9b54\u68d2 (1 x \u9b54\u94c1\u68d2, 4 x \u5f3a\u6548\u4e0d\u706d\u7cbe\u534e, 6 x \u5927\u9b54\u5149\u788e\u7247, 1 x \u7b26\u6587\u5965\u91d1\u68d2) x 1 \u3000\u3000301 - 305 \u3000\u3000\u4ed8\u9b54\u62ab\u98ce - \u8d85\u5f3a\u9632\u5fa1 (8 x \u5e7b\u5f71\u4e4b\u5c18) x 7 \u3000\u3000305 - 315 \u3000\u3000\u4ed8\u9b54\u62a4\u8155 - \u88ad\u51fb/\u8155\u529b (6 x \u5965\u672f\u4e4b\u5c18) x 10 \u3000\u3000315 - 325 \u3000\u3000\u4ed8\u9b54\u62ab\u98ce - \u6781\u6548\u62a4\u7532 \u6216 \u4ed8\u9b54\u624b\u5957 - \u88ad\u51fb (8 x \u5965\u672f\u4e4b\u5c18) x 10 \u3000\u3000325 - 335 \u3000\u3000\u4ed8\u9b54\u80f8\u7532 - \u6781\u6548\u7cbe\u795e (2 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 10 \u3000\u3000335 - 340 \u3000\u3000\u4ed8\u9b54\u76fe\u724c - \u6781\u6548\u8010\u529b (15 x \u5965\u672f\u4e4b\u5c18) x 5 \u3000\u3000340 - 345 \u3000\u3000\u8d85\u5f3a\u5deb\u5e08\u6cb9 (3 x \u5965\u672f\u4e4b\u5c18, 1 x \u6076\u68a6\u85e4, 1 x \u6d78\u900f\u8bd5\u74f6) x 10 \u3000\u3000345 - 350 \u3000\u3000\u4ed8\u9b54\u624b\u5957 - \u6781\u6548\u529b\u91cf (12 x \u5965\u672f\u4e4b\u5c18, 1 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 5 \u3000\u3000350 - 351 \u3000\u3000\u7b26\u6587\u575a\u68d2 (1 x \u575a\u94a2\u68d2, 8 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e, 8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 1 x \u539f\u59cb\u529b\u91cf, 1 x \u7b26\u6587\u9b54\u94c1\u68d2) x 1 \u3000\u3000351 - 360 \u3000\u3000\u4ed8\u9b54\u624b\u5957 - \u6781\u6548\u529b\u91cf (12 x \u5965\u672f\u4e4b\u5c18, 1 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e) x 15 \u3000\u3000360 - 370 \u3000\u3000\u4ed8\u9b54\u6212\u6307 - \u6253\u51fb (8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 24 x \u5965\u672f\u4e4b\u5c18) x 10 (\u9700\u8981\u865a\u7a7a\u8d22\u56e2\u58f0\u671b\u5d07\u656c\uff0c\u914d\u65b9\u540d\u53eb\u4ed8\u9b54\u6212\u6307\uff1a\u6b66\u5668\u529b\u91cf\uff0c\u4e0d\u8fc7\u4ed8\u9b54\u540d\u5b57\u53eb\u6253\u51fb) \u3000\u3000370 - 375 \u3000\u3000\u4ed8\u9b54\u6212\u6307 - \u6cbb\u7597\u529b\u91cf (8 x \u5927\u5757\u68f1\u5149\u788e\u7247, 10 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e, 20 x \u5965\u672f\u4e4b\u5c18) x 5 (\u6c99\u5c14\u5854\u5d07\u656c) \u3000\u3000\u5927\u7ea6\u6240\u6709\u6750\u6599\u6570\u91cf\uff1a \u3000\u30001 x \u9b54\u94c1\u68d2 \u3000\u30004 x \u5f3a\u6548\u4e0d\u706d\u7cbe\u534e \u3000\u30006 x \u5927\u9b54\u5149\u788e\u7247 \u3000\u30001 x \u7b26\u6587\u5965\u91d1\u68d2 \u3000\u300056 x \u5e7b\u5f71\u4e4b\u5c18 \u3000\u3000645 x \u5965\u672f\u4e4b\u5c18 \u3000\u300096 x \u5f3a\u6548\u4f4d\u9762\u7cbe\u534e \u3000\u300010 x \u6076\u68a6\u85e4 \u3000\u300010 x \u6d78\u900f\u8bd5\u74f6 \u3000\u30001 x \u575a\u94a2\u68d2 \u3000\u3000128 x \u5927\u5757\u68f1\u5149\u788e\u7247 \u3000\u30001 x \u539f\u59cb\u529b\u91cf
FM0—40 分解
40—75 [附魔护腕 - 初级生命] = 1 x 奇异之尘
期间我在排战场的时间去刷怒焰峡谷，可以分解不少材料（AH里面低等级的材料非常贵）
75 - 85 [附魔护腕 - 初级偏斜] =1 x [次级魔法精华], 1 x [奇异之尘]
85 - 100 [附魔护腕 - 初级耐力] = 3 x [奇异之尘]
101 -105 [附魔护腕 - 初级耐力] = 3 x [奇异之尘]
105 - 120 [附魔护腕 - 初级敏捷] = 2 x [奇异之尘], 1 x [强效魔法精华]
120 - 130 [附魔盾牌 - 初级耐力] = 1 x [次级星界精华] , 2 x [奇异之尘]
130 - 150 [附魔护腕 - 次级耐力] = 2 x [灵魂之尘]
151 - 160 [附魔护腕 - 次级耐力] = 2 x [灵魂之尘]
160 - 165 [附魔盾牌 - 次级耐力] = 1 x [次级秘法精华], 1 x [灵魂之尘]
165 - 180 [附魔护腕 - 精神] = 1 x [次级秘法精华]
180 - 200 [附魔护腕 - 力量] = 1 x [幻象之尘]
201 - 205 [附魔护腕 - 力量] = 1 x [幻象之尘]
205 - 225 [附魔披风 - 强效防御] = 3 x [幻象之尘]
225 - 235 [附魔手套 - 敏捷] = 1 x [次级虚空精华], 1 x [幻象之尘]
235 - 245 [附魔胸甲 - 超强生命] = 6 x [幻象之尘]
245 - 250 [附魔护腕 - 强效力量] = 2 x [梦境之尘], 1 x [强效虚空精华]
250-275 次级法力之油3梦境＋2紫莲花
275-290 巫师油3个幻影＋2个火焰花
（配方在希利苏斯旅馆2楼NPC处购买）
291 - 300 [附魔披风 - 超级防御] = 8 x [幻影之尘]
（300前不是很难，我现在比较庆幸自己有个仓库号，在TBC前分解一些垃圾装备的材料正好派上用场，200-300之间如果AH的材料非常贵，那还是建议自己去刷吧，我昨天刷了一下午的STSM，收获：20+强效不灭，40+大魔，垃圾东西NPC也小赚了一笔，大魔冲技能用不上，但是放AH到是可以赚一点，）
301-310 用沙城附魔师给的配方 我选的是占卜声望
310—340 做油+胸甲精神
（300技能以上就开始花钱了，不忍心也没办法）
340—360后开始做油 配方应该是在银月的NPC处
2种油我总共做了50+瓶，，噩梦草的价钱很高，这一段至少花了400G+，成品就慢慢放AH消耗吧，比给自己F魔要好，稍微回收一点点资金。
目前练到360，下面就可以靠时光的戒指F魔冲了，幸好自己在升70之间把所有任务给的蓝装都放银行了，分解了一下估计也差不多了，哈哈，我太有远见了！）
总结一下，2天时间大概花了1800G左右，还是可以接受的，呵呵。
绛旓細浠橀瓟鎵嬪 - 鏋佹晥鍔涢噺 (12 x 濂ユ湳涔嬪皹, 1 x 寮烘晥浣嶉潰绮惧崕) x 5 350 - 351 绗︽枃鍧氭 (1 x 鍧氶挗妫, 8 x 寮烘晥浣嶉潰绮惧崕, 8 x 澶у潡妫卞厜纰庣墖, 1 x 鍘熷鍔涢噺, 1 x 绗︽枃榄旈搧妫) x 1 351 - 360 浠橀瓟鎵嬪 - 鏋佹晥鍔涢噺 (12 x 濂ユ湳涔嬪皹, 1 x 寮烘晥浣嶉潰绮惧崕) x 15 360 - 370 浠橀瓟...
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绛旓細341绗︽枃榄旈搧妫 榄旈搧妫*1 寮烘晥涓嶇伃绮惧崕*4 澶у潡榄斿厜纰庣墖*6 342-355 闄勯瓟瓒呯骇宸笀涔嬫补 濂ユ硶涔嬪皹*3 鍣╂ⅵ钘*1 鐏岄瓟涔嬬摱*1 356-360 闄勯瓟鎵嬪-鐗规晥鍔涢噺 濂ユ硶涔嬪皹*12 寮烘晥浣嶉潰绮惧崕*1 361 绗︽枃绮鹃噾妫 绮鹃噾妫*1 寮烘晥浣嶉潰绮惧崕*8澶у潡妫卞厜纰庣墖*8婧愮敓涔嬭兘*1 362-375 闄勯瓟鎴掓寚-娉曟湳鑳介噺 澶у潡妫卞厜...
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绛旓細鎴戝姞涓鍙ワ紝绗竴鏄粌瑁佺紳锛屼綘浼氬彂鐜版敹璐竷鏂欐媶鍋氱殑缁胯浼氫究瀹滃緢澶氾紝绗簩鍏嶈垂甯﹁鑹诧紝缁胯甯冩枡閮界粰浣狅紝鏂潶涔熸槸锛屼竴鐩村埌350閮介渶瑕侀潬瑁佺紳鐨勬敮鎸佺殑銆
绛旓細350-351 绗︽枃鍧氭 (1 脳 鍧氶挗妫掞紝8 脳 寮烘晥浣嶉潰绮惧崕锛8 脳 澶у潡妫卞厜纰庣墖锛1 脳 鍘熷鍔涢噺锛1 脳 绗︽枃榄旈搧妫) 脳 1 351-360 浠橀瓟鎵嬪-鏋佹晥鍔涢噺 (12 脳 濂ユ湳涔嬪皹锛1 脳 寮烘晥浣嶉潰绮惧崕) 脳 15 360-370 浠橀瓟鎴掓寚-鎵撳嚮 (8 脳 澶у潡妫卞厜纰庣墖锛24 脳 濂ユ湳涔嬪皹) 脳 10 (闇瑕佽櫄绌鸿储鍥㈠０鏈...
绛旓細鎴戝氨鏄缁僃M鐨刞`寮濮嬪憿鍒嗚В瑁呭灏辫兘鎻愬崌鐐规暟``浣嗗悗闈㈠彧鑳介潬FM鎵嶈兘鍗嘸`涓寮濮嬪憿``浣犲彲浠ュ幓涔扮偣缁胯鎴栬呮妸鎵撳埌鐨勮澶囧瓨涓嬫潵鍒嗚В``浣嗘槸鍒版妧鑳200宸﹀彸鏃跺檂`FM闇瑕佺伒榄備箣灏樹箣绫讳笢瑗跨殑鏃跺檂`鍘讳拱閭ｄ簺瑁呭灏卞お璐典簡``涓嶈繃瑕佹槸鎰挎剰鍑洪挶鐨勮瘽``1澶╀箣鍐呭氨鑳藉啿鍒300浠ヤ笂 浣嗘槸鍚庨潰鍛㈠氨鍥伴毦澶氫簡``FM涓娆￠渶瑕...
绛旓細15 闄勯瓟鑳哥敳 - 鍒濈骇鐢熷懡 濂囧紓涔嬪皹 50 闄勯瓟鎶よ厱 - 鍒濈骇鑰愬姏 濂囧紓涔嬪皹 (3)80 闄勯瓟鎶よ厱 - 鍒濈骇鍔涢噺 濂囧紓涔嬪皹 5 (瑕佸湪AH涔伴厤鏂)125 闄勯瓟闈村瓙 - 鍒濈骇鑰愬姏 濂囧紓涔嬪皹 (8)140 闄勯瓟姝﹀櫒 - 娆＄骇鏀诲嚮 鐏甸瓊涔嬪皹 (2)155 闄勯瓟鎶 - 闃插尽 鐏甸瓊涔嬪皹 (3)170 闄勯瓟闈村瓙 - 娆＄骇鑰愬姏 鐏甸瓊涔嬪皹 (4)180...
绛旓細FM灏辩湅浣犳兂鐪侀挶杩樻槸瑕侀熷害浜 鐪嬩笅姣忎釜FM鎶鑳<榛勮壊鍜屾繁鑹茬殑>鐪嬪摢涓狥M闇瑕佺殑鏉愭枡姣旇緝灏.浣犵殑FM鎶鑳藉彧鏈125鍙互鎵炬湅鍙嬪湪琛鑹-STSM鐨勭豢鑹茶澶囨潵鍒嗚В鏉愭枡鍗囨妧鑳
绛旓細110-130涔嬮棿鍙互鐢╗寮烘晥榄旀硶鏉朷 鏉ユ彁鍗囩瓑绾у己鏁堥瓟娉曟潠- (寮烘晥榄旀硶绮惧崕x1 鏅氭湪鏌磝1) [70-110-130-150]129-130 绗︽枃閾舵- [100-130-150-170] <110绾т互涓婇檮榄斿繀澶囬亾鍏>(閾舵x1 绗︽枃閾滄x1 寮烘晥榄旀硶绮惧崕x3 濂囧紓涔嬪皹x6) -1 3锛岀瓑绾ч渶姹20, 鎶鑳介渶姹125, 鎶鑳藉彲鎻愬崌鑷225 [鍚勫ぇ鏂版墜涓诲煄鐨嗘湁...
