Skip to content

Commit 3f5bc48

Browse files
committed
fix: no-shortcut-reference-link
1 parent c0d1e22 commit 3f5bc48

File tree

15 files changed

+35
-37
lines changed

15 files changed

+35
-37
lines changed

.remarkrc

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -7,7 +7,6 @@
77
["remark-lint-maximum-line-length", false],
88
["remark-lint-no-file-name-articles", false],
99
["remark-lint-no-literal-urls", false],
10-
["remark-lint-no-shortcut-reference-link", false],
1110
["remark-lint-no-trailing-spaces", false],
1211
["remark-lint-no-undefined-references", false],
1312
["remark-lint-no-unused-definitions", false],

locale/ar/get-involved/node-meetups.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -329,7 +329,7 @@ layout: contribute.hbs
329329

330330
##### إرفين
331331

332-
* [اللقاء]https://www.meetup.com/Node-JS-OC/)
332+
* [اللقاء](https://www.meetup.com/Node-JS-OC/)
333333
* تردد اللقاء - شهريا
334334
* كيف تقدم طلب محاضرة ؟ اتصل بالمنظمين في صفحة اللقاءات.
335335
* المنظم - فرشيد عاطف

locale/en/blog/feature/streams2.md

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -196,7 +196,7 @@ Note that `stream.Readable` is an abstract class designed to be
196196
extended with an underlying implementation of the `_read(size)`
197197
method. (See below.)
198198

199-
### new stream.Readable([options])
199+
### new stream.Readable(\[options\])
200200

201201
* `options` {Object}
202202
* `highWaterMark` {Number} The maximum number of bytes to store in
@@ -458,7 +458,7 @@ can be `'utf8'`, `'utf16le'` (`'ucs2'`), `'ascii'`, or `'hex'`.
458458
The encoding can also be set by specifying an `encoding` field to the
459459
constructor.
460460
461-
### readable.read([size])
461+
### readable.read(\[size\])
462462
463463
* `size` {Number | null} Optional number of bytes to read.
464464
* Return: {Buffer | String | null}
@@ -479,7 +479,7 @@ a future `'readable'` event will be emitted when more is available.
479479
Calling `stream.read(0)` will always return `null`, and will trigger a
480480
refresh of the internal buffer, but otherwise be a no-op.
481481
482-
### readable.pipe(destination, [options])
482+
### readable.pipe(destination, \[options\])
483483
484484
* `destination` {Writable Stream}
485485
* `options` {Object} Optional
@@ -515,7 +515,7 @@ reader.on("end", function() {
515515
Note that `process.stderr` and `process.stdout` are never closed until
516516
the process exits, regardless of the specified options.
517517
518-
### readable.unpipe([destination])
518+
### readable.unpipe(\[destination\])
519519
520520
* `destination` {Writable Stream} Optional
521521
@@ -549,7 +549,7 @@ Note that `stream.Writable` is an abstract class designed to be
549549
extended with an underlying implementation of the
550550
`_write(chunk, encoding, cb)` method. (See below.)
551551
552-
### new stream.Writable([options])
552+
### new stream.Writable(\[options\])
553553
554554
* `options` {Object}
555555
* `highWaterMark` {Number} Buffer level when `write()` starts
@@ -595,7 +595,7 @@ the class that defines it, and should not be called directly by user
595595
programs. However, you **are** expected to override this method in
596596
your own extension classes.
597597
598-
### writable.write(chunk, [encoding], [callback])
598+
### writable.write(chunk, \[encoding\], \[callback\])
599599
600600
* `chunk` {Buffer | String} Data to be written
601601
* `encoding` {String} Optional. If `chunk` is a string, then encoding
@@ -612,7 +612,7 @@ the buffer is full, and the data will be sent out in the future. The
612612
The specifics of when `write()` will return false, is determined by
613613
the `highWaterMark` option provided to the constructor.
614614
615-
### writable.end([chunk], [encoding], [callback])
615+
### writable.end(\[chunk\], \[encoding\], \[callback\])
616616
617617
* `chunk` {Buffer | String} Optional final data to be written
618618
* `encoding` {String} Optional. If `chunk` is a string, then encoding
@@ -698,7 +698,7 @@ Rather than implement the `_read()` and `_write()` methods, Transform
698698
classes must implement the `_transform()` method, and may optionally
699699
also implement the `_flush()` method. (See below.)
700700
701-
### new stream.Transform([options])
701+
### new stream.Transform(\[options\])
702702
703703
* `options` {Object} Passed to both Writable and Readable
704704
constructors.

locale/en/docs/guides/simple-profiling.md

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -157,7 +157,7 @@ up by language. First, we look at the summary section and see:
157157
This tells us that 97% of all samples gathered occurred in C++ code and that
158158
when viewing other sections of the processed output we should pay most attention
159159
to work being done in C++ (as opposed to JavaScript). With this in mind, we next
160-
find the [C++] section which contains information about which C++ functions are
160+
find the \[C++\] section which contains information about which C++ functions are
161161
taking the most CPU time and see:
162162

163163
```
@@ -174,7 +174,7 @@ taken up by a function called PBKDF2 which corresponds to our hash generation
174174
from a user's password. However, it may not be immediately obvious how the lower
175175
two entries factor into our application (or if it is we will pretend otherwise
176176
for the sake of example). To better understand the relationship between these
177-
functions, we will next look at the [Bottom up (heavy) profile] section which
177+
functions, we will next look at the \[Bottom up (heavy) profile\] section which
178178
provides information about the primary callers of each function. Examining this
179179
section, we find:
180180

locale/en/get-involved/node-meetups.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -356,7 +356,7 @@ REQUIREMENTS
356356
* Frequency of meetups - every 6-9 month
357357
* How to submit a talk? Contact organizers in the meetup page or use contacts information below
358358
* Organizer name - Denis Izmaylov
359-
* Organizer contact info - [Telegram](https://t.me/DenisIzmaylov) [Twitter](https://twitter.com/DenisIzmaylov] [Facebook](https://facebook.com/denis.izmaylov)
359+
* Organizer contact info - [Telegram](https://t.me/DenisIzmaylov) [Twitter](https://twitter.com/DenisIzmaylov) [Facebook](https://facebook.com/denis.izmaylov)
360360

361361
### South Africa
362362

locale/es/docs/guides/simple-profiling.md

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -129,7 +129,7 @@ Opening processed.txt in your favorite text editor will give you a few different
129129
215 0.6% Unaccounted
130130
```
131131

132-
This tells us that 97% of all samples gathered occurred in C++ code and that when viewing other sections of the processed output we should pay most attention to work being done in C++ (as opposed to JavaScript). With this in mind, we next find the [C++] section which contains information about which C++ functions are taking the most CPU time and see:
132+
This tells us that 97% of all samples gathered occurred in C++ code and that when viewing other sections of the processed output we should pay most attention to work being done in C++ (as opposed to JavaScript). With this in mind, we next find the \[C++\] section which contains information about which C++ functions are taking the most CPU time and see:
133133

134134
```
135135
[C++]:
@@ -139,7 +139,7 @@ This tells us that 97% of all samples gathered occurred in C++ code and that whe
139139
3165 8.4% 8.6% _malloc_zone_malloc
140140
```
141141

142-
We see that the top 3 entries account for 72.1% of CPU time taken by the program. From this output, we immediately see that at least 51.8% of CPU time is taken up by a function called PBKDF2 which corresponds to our hash generation from a user's password. However, it may not be immediately obvious how the lower two entries factor into our application (or if it is we will pretend otherwise for the sake of example). To better understand the relationship between these functions, we will next look at the [Bottom up (heavy) profile] section which provides information about the primary callers of each function. Examining this section, we find:
142+
We see that the top 3 entries account for 72.1% of CPU time taken by the program. From this output, we immediately see that at least 51.8% of CPU time is taken up by a function called PBKDF2 which corresponds to our hash generation from a user's password. However, it may not be immediately obvious how the lower two entries factor into our application (or if it is we will pretend otherwise for the sake of example). To better understand the relationship between these functions, we will next look at the \[Bottom up (heavy) profile\] section which provides information about the primary callers of each function. Examining this section, we find:
143143

144144
```
145145
ticks parent name

locale/fr/docs/guides/simple-profiling.md

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -129,7 +129,7 @@ Opening processed.txt in your favorite text editor will give you a few different
129129
215 0.6% Unaccounted
130130
```
131131

132-
This tells us that 97% of all samples gathered occurred in C++ code and that when viewing other sections of the processed output we should pay most attention to work being done in C++ (as opposed to JavaScript). With this in mind, we next find the [C++] section which contains information about which C++ functions are taking the most CPU time and see:
132+
This tells us that 97% of all samples gathered occurred in C++ code and that when viewing other sections of the processed output we should pay most attention to work being done in C++ (as opposed to JavaScript). With this in mind, we next find the \[C++\] section which contains information about which C++ functions are taking the most CPU time and see:
133133

134134
```
135135
[C++]:
@@ -139,7 +139,7 @@ This tells us that 97% of all samples gathered occurred in C++ code and that whe
139139
3165 8.4% 8.6% _malloc_zone_malloc
140140
```
141141

142-
We see that the top 3 entries account for 72.1% of CPU time taken by the program. From this output, we immediately see that at least 51.8% of CPU time is taken up by a function called PBKDF2 which corresponds to our hash generation from a user's password. However, it may not be immediately obvious how the lower two entries factor into our application (or if it is we will pretend otherwise for the sake of example). To better understand the relationship between these functions, we will next look at the [Bottom up (heavy) profile] section which provides information about the primary callers of each function. Examining this section, we find:
142+
We see that the top 3 entries account for 72.1% of CPU time taken by the program. From this output, we immediately see that at least 51.8% of CPU time is taken up by a function called PBKDF2 which corresponds to our hash generation from a user's password. However, it may not be immediately obvious how the lower two entries factor into our application (or if it is we will pretend otherwise for the sake of example). To better understand the relationship between these functions, we will next look at the \[Bottom up (heavy) profile\] section which provides information about the primary callers of each function. Examining this section, we find:
143143

144144
```
145145
ticks parent name

locale/it/about/community.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -21,7 +21,7 @@ Ci sono quattro tipi di coinvolgimenti con il Comitato della Community:
2121
* Un **Osservatore** è un individuo che ha richiesto o a cui è stato richiesto di assistere ad un incontro del CommComm. È anche il primo step per diventare un Membro.
2222
* Un **Membro** è un collaboratore con diritti di voto che ha soddisfatto i requisiti di partecipazione ed è stato eletto dalla procedura di votazione del CommComm.
2323

24-
Per la lista attuale dei membri del Comitato della Community, vedere il [README.md] del progetto (https://github.com/nodejs/community-committee).
24+
Per la lista attuale dei membri del Comitato della Community, vedere il [README.md del progetto](https://github.com/nodejs/community-committee).
2525

2626
## Contributors and Collaborators (Contributori e Collaboratori)
2727

locale/ja/docs/guides/simple-profiling.md

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -233,15 +233,15 @@ up by language. First, we look at the summary section and see:
233233
This tells us that 97% of all samples gathered occurred in C++ code and that
234234
when viewing other sections of the processed output we should pay most attention
235235
to work being done in C++ (as opposed to JavaScript). With this in mind, we next
236-
find the [C++] section which contains information about which C++ functions are
236+
find the \[C++\] section which contains information about which C++ functions are
237237
taking the most CPU time and see:
238238
239239
-->
240240
これは、収集されたすべてのサンプルの 97% が C++ コードで発生しており、
241241
処理された出力の他のセクションを見るときは
242242
(JavaScript ではなく) C++ で行われている作業に最も注意する必要があることを示しています。
243243
これを念頭に置いて、次にどの C++ 関数が最も CPU 時間を消費しているかについての情報を含む
244-
[C++] セクションを見てみます。
244+
\[C++\] セクションを見てみます。
245245

246246
```
247247
[C++]:
@@ -258,7 +258,7 @@ taken up by a function called PBKDF2 which corresponds to our hash generation
258258
from a user's password. However, it may not be immediately obvious how the lower
259259
two entries factor into our application (or if it is we will pretend otherwise
260260
for the sake of example). To better understand the relationship between these
261-
functions, we will next look at the [Bottom up (heavy) profile] section which
261+
functions, we will next look at the \[Bottom up (heavy) profile\] section which
262262
provides information about the primary callers of each function. Examining this
263263
section, we find:
264264
@@ -270,7 +270,7 @@ CPU 時間の少なくとも 51.8% が占められていることが分かり
270270
(またはそうである場合は例のために別のふりをすることになる)、
271271
すぐには明らかにならないかもしれません。
272272
これらの関数間の関係をよりよく理解するために、
273-
次に各関数の主な呼び出し元に関する情報を提供する [Bottom up (heavy) profile] セクションを見ていきます。
273+
次に各関数の主な呼び出し元に関する情報を提供する \[Bottom up (heavy) profile\] セクションを見ていきます。
274274
このセクションを調べると、次のことがわかります。
275275

276276
```

locale/ko/docs/guides/simple-profiling.md

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -315,7 +315,7 @@ up by language. First, we look at the summary section and see:
315315
This tells us that 97% of all samples gathered occurred in C++ code and that
316316
when viewing other sections of the processed output we should pay most attention
317317
to work being done in C++ (as opposed to JavaScript). With this in mind, we next
318-
find the [C++] section which contains information about which C++ functions are
318+
find the \[C++\] section which contains information about which C++ functions are
319319
taking the most CPU time and see:
320320
321321
```
@@ -329,7 +329,7 @@ taking the most CPU time and see:
329329

330330
이 부분을 보면 C++ 코드에서 수집된 샘플이 97%를 차지하는 것을 볼 수 있으므로 처리된 결과에서
331331
다른 부분을 볼 때 C++에서 이뤄진 작업에 대부분의 관심을 기울여야 합니다.(JavaScript 대비)
332-
그래서 C++ 함수가 대부분의 CPU 시간을 차지한 정보를 담고 있는 [C++] 부분을 찾아볼 것입니다.
332+
그래서 C++ 함수가 대부분의 CPU 시간을 차지한 정보를 담고 있는 \[C++\] 부분을 찾아볼 것입니다.
333333

334334
```
335335
[C++]:
@@ -346,7 +346,7 @@ taken up by a function called PBKDF2 which corresponds to our hash generation
346346
from a user's password. However, it may not be immediately obvious how the lower
347347
two entries factor into our application (or if it is we will pretend otherwise
348348
for the sake of example). To better understand the relationship between these
349-
functions, we will next look at the [Bottom up (heavy) profile] section which
349+
functions, we will next look at the \[Bottom up (heavy) profile\] section which
350350
provides information about the primary callers of each function. Examining this
351351
section, we find:
352352
@@ -370,7 +370,7 @@ section, we find:
370370
해시를 생성하는 PBKDF2 함수 호출이 최소 51.8%의 CPU 시간을 차지한 것을 바로 눈치챌 수 있습니다.
371371
하지만 더 낮은 비율을 가진 두 부분은 애플리케이션의 어떤 부분인지 바로 알 수 없습니다.(아니면
372372
예제를 위해서 그런 척 할 것입니다.) 이러한 함수 간의 관계를 더 이해하려면 각 함수의 주요 호출자 정보를
373-
제공하는 [Bottom up (heavy) profile] 부분을 봐야 합니다.
373+
제공하는 \[Bottom up (heavy) profile\] 부분을 봐야 합니다.
374374
이 부분을 찾아보면 다음과 같이 나와 있습니다.
375375

376376
```

0 commit comments

Comments
 (0)