Regex Patterns

Practical regular expression cookbook. Patterns for validation, parsing, extraction, and refactoring across JavaScript, Python, Go, and command-line tools.

When to Use

Validating user input (email, URL, IP, phone, dates)
Parsing log lines or structured text
Extracting data from strings (IDs, numbers, tokens)
Search-and-replace in code (rename variables, update imports)
Filtering lines in files or command output
Debugging regexes that don't match as expected

Quick Reference

Metacharacters

Pattern	Matches	Example
`.`	Any character (except newline)	`a.c` matches `abc`, `a1c`
`\d`	Digit `[0-9]`	`\d{3}` matches `123`
`\w`	Word char `[a-zA-Z0-9_]`	`\w+` matches `hello_123`
`\s`	Whitespace `[ \t\n\r\f]`	`\s+` matches spaces/tabs
`\b`	Word boundary	`\bcat\b` matches `cat` not `scatter`
`^`	Start of line	`^Error` matches line starting with Error
`$`	End of line	`\.js$` matches line ending with .js
`\D`, `\W`, `\S`	Negated: non-digit, non-word, non-space

Quantifiers

Pattern	Meaning
`*`	0 or more (greedy)
`+`	1 or more (greedy)
`?`	0 or 1 (optional)
`{3}`	Exactly 3
`{2,5}`	Between 2 and 5
`{3,}`	3 or more
`*?`, `+?`	Lazy (match as few as possible)

Groups and Alternation

Pattern	Meaning
`(abc)`	Capture group
`(?:abc)`	Non-capturing group
`(?P<name>abc)`	Named group (Python)
`(?<name>abc)`	Named group (JS/Go)
`a\|b`	Alternation (a or b)
`[abc]`	Character class (a, b, or c)
`[^abc]`	Negated class (not a, b, or c)
`[a-z]`	Range

Lookahead and Lookbehind

Pattern	Meaning
`(?=abc)`	Positive lookahead (followed by abc)
`(?!abc)`	Negative lookahead (not followed by abc)
`(?<=abc)`	Positive lookbehind (preceded by abc)
`(?<!abc)`	Negative lookbehind (not preceded by abc)

Validation Patterns

Email

# Basic (covers 99% of real emails)
^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$

# Stricter (no consecutive dots, no leading/trailing dots in local part)
^[a-zA-Z0-9]([a-zA-Z0-9._%+-]*[a-zA-Z0-9])?@[a-zA-Z0-9]([a-zA-Z0-9-]*[a-zA-Z0-9])?(\.[a-zA-Z]{2,})+$

URL

# HTTP/HTTPS URLs
https?://[a-zA-Z0-9]([a-zA-Z0-9-]*[a-zA-Z0-9])?(\.[a-zA-Z0-9]([a-zA-Z0-9-]*[a-zA-Z0-9])?)*(/[^\s]*)?

# With optional port and query
https?://[^\s/]+(/[^\s?]*)?(\?[^\s#]*)?(#[^\s]*)?

IP Addresses

# IPv4
\b(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b

# IPv4 (simple, allows invalid like 999.999.999.999)
\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b

# IPv6 (simplified)
(?:[0-9a-fA-F]{1,4}:){7}[0-9a-fA-F]{1,4}

Phone Numbers

# US phone (various formats)
(?:\+1[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}
# Matches: +1 (555) 123-4567, 555.123.4567, 5551234567

# International (E.164)
\+[1-9]\d{6,14}

Dates and Times

# ISO 8601 date
\d{4}-(?:0[1-9]|1[0-2])-(?:0[1-9]|[12]\d|3[01])

# ISO 8601 datetime
\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(?:\.\d+)?(?:Z|[+-]\d{2}:\d{2})

# US date (MM/DD/YYYY)
(?:0[1-9]|1[0-2])/(?:0[1-9]|[12]\d|3[01])/\d{4}

# Time (HH:MM:SS, 24h)
(?:[01]\d|2[0-3]):[0-5]\d:[0-5]\d

Passwords (Strength Check)

# At least 8 chars, 1 upper, 1 lower, 1 digit, 1 special
^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[!@#$%^&*()_+=-]).{8,}$

UUIDs

[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}

Semantic Version

\bv?(\d+)\.(\d+)\.(\d+)(?:-([\w.]+))?(?:\+([\w.]+))?\b
# Captures: major, minor, patch, prerelease, build
# Matches: 1.2.3, v1.0.0-beta.1, 2.0.0+build.123

Parsing Patterns

Log Lines

# Apache/Nginx access log
# Format: IP - - [date] "METHOD /path HTTP/x.x" status size
grep -oP '(\S+) - - \[([^\]]+)\] "(\w+) (\S+) \S+" (\d+) (\d+)' access.log

# Extract IP and status code
grep -oP '^\S+|"\s\K\d{3}' access.log

# Syslog format
# Format: Mon DD HH:MM:SS hostname process[pid]: message
grep -oP '^\w+\s+\d+\s[\d:]+\s(\S+)\s(\S+)\[(\d+)\]:\s(.*)' syslog

# JSON log — extract a field
grep -oP '"level"\s*:\s*"\K[^"]+' app.log
grep -oP '"message"\s*:\s*"\K[^"]+' app.log

Code Patterns

# Find function definitions (JavaScript/TypeScript)
grep -nP '(?:function\s+\w+|(?:const|let|var)\s+\w+\s*=\s*(?:async\s*)?\([^)]*\)\s*=>|(?:async\s+)?function\s*\()' src/*.ts

# Find class definitions
grep -nP 'class\s+\w+(?:\s+extends\s+\w+)?' src/*.ts

# Find import statements
grep -nP '^import\s+.*\s+from\s+' src/*.ts

# Find TODO/FIXME/HACK comments
grep -rnP '(?:TODO|FIXME|HACK|XXX|WARN)(?:\([^)]+\))?:?\s+' src/

# Find console.log left in code
grep -rnP 'console\.(log|debug|info|warn|error)\(' src/ --include='*.ts' --include='*.js'

Data Extraction

# Extract all email addresses from a file
grep -oP '[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}' file.txt

# Extract all URLs
grep -oP 'https?://[^\s<>"]+' file.html

# Extract all quoted strings
grep -oP '"[^"\\]*(?:\\.[^"\\]*)*"' file.json

# Extract numbers (integer and decimal)
grep -oP '-?\d+\.?\d*' data.txt

# Extract key-value pairs (key=value)
grep -oP '\b(\w+)=([^\s&]+)' query.txt

# Extract hashtags
grep -oP '#\w+' posts.txt

# Extract hex colors
grep -oP '#[0-9a-fA-F]{3,8}\b' styles.css

Language-Specific Usage

JavaScript

// Test if a string matches
const emailRegex = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/;
emailRegex.test('[email protected]'); // true

// Extract with capture groups
const match = '2026-02-03T12:30:00Z'.match(/(\d{4})-(\d{2})-(\d{2})/);
// match[1] = '2026', match[2] = '02', match[3] = '03'

// Named groups
const m = 'John Doe, age 30'.match(/(?<name>[A-Za-z ]+), age (?<age>\d+)/);
// m.groups.name = 'John Doe', m.groups.age = '30'

// Find all matches (matchAll returns iterator)
const text = 'Call 555-1234 or 555-5678';
const matches = [...text.matchAll(/\d{3}-\d{4}/g)];
// [{0: '555-1234', index: 5}, {0: '555-5678', index: 18}]

// Replace with callback
'hello world'.replace(/\b\w/g, c => c.toUpperCase());
// 'Hello World'

// Replace with named groups
'2026-02-03'.replace(/(?<y>\d{4})-(?<m>\d{2})-(?<d>\d{2})/, '$<m>/$<d>/$<y>');
// '02/03/2026'

// Split with regex
'one, two;  three'.split(/[,;]\s*/);
// ['one', 'two', 'three']

Python

import re

# Match (anchored to start)
m = re.match(r'^(\w+)@(\w+)\.(\w+)$', '[email protected]')
if m:
    print(m.group(1))  # 'user'

# Search (find first match anywhere)
m = re.search(r'\d{3}-\d{4}', 'Call 555-1234 today')
print(m.group())  # '555-1234'

# Find all matches
emails = re.findall(r'[\w.+-]+@[\w.-]+\.\w{2,}', text)

# Named groups
m = re.match(r'(?P<name>\w+)\s+(?P<age>\d+)', 'Alice 30')
print(m.group('name'))  # 'Alice'

# Substitution
result = re.sub(r'\bfoo\b', 'bar', 'foo foobar foo')
# 'bar foobar bar'

# Sub with callback
result = re.sub(r'\b\w', lambda m: m.group().upper(), 'hello world')
# 'Hello World'

# Compile for reuse (faster in loops)
pattern = re.compile(r'\d{4}-\d{2}-\d{2}')
dates = pattern.findall(log_text)

# Multiline and DOTALL
re.findall(r'^ERROR.*$', text, re.MULTILINE)  # ^ and $ match line boundaries
re.search(r'start.*end', text, re.DOTALL)      # . matches newlines

# Verbose mode (readable complex patterns)
pattern = re.compile(r'''
    ^                   # Start of string
    (?P<year>\d{4})     # Year
    -(?P<month>\d{2})   # Month
    -(?P<day>\d{2})     # Day
    $                   # End of string
''', re.VERBOSE)

Go

import "regexp"

// Compile pattern (panics on invalid regex)
re := regexp.MustCompile(`\d{4}-\d{2}-\d{2}`)

// Match test
re.MatchString("2026-02-03")  // true

// Find first match
re.FindString("Date: 2026-02-03 and 2026-03-01")  // "2026-02-03"

// Find all matches
re.FindAllString(text, -1)  // []string of all matches

// Capture groups
re := regexp.MustCompile(`(\w+)@(\w+)\.(\w+)`)
match := re.FindStringSubmatch("[email protected]")
// match[0] = "[email protected]", match[1] = "user", match[2] = "example"

// Named groups
re := regexp.MustCompile(`(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})`)
match := re.FindStringSubmatch("2026-02-03")
for i, name := range re.SubexpNames() {
    if name != "" {
        fmt.Printf("%s: %s\n", name, match[i])
    }
}

// Replace
re.ReplaceAllString("foo123bar", "NUM")  // "fooNUMbar"

// Replace with function
re.ReplaceAllStringFunc(text, strings.ToUpper)

// Note: Go uses RE2 syntax — no lookahead/lookbehind

Command Line (grep/sed)

# grep -P uses PCRE (Perl-compatible — full features)
# grep -E uses Extended regex (no lookahead/lookbehind)

# Find lines matching a pattern
grep -P '\d{3}-\d{4}' file.txt

# Extract only the matching part
grep -oP '\d{3}-\d{4}' file.txt

# Invert match (lines NOT matching)
grep -vP 'DEBUG|TRACE' app.log

# sed replacement
sed 's/oldPattern/newText/g' file.txt         # Basic
sed -E 's/foo_([a-z]+)/bar_\1/g' file.txt     # Extended with capture group

# Perl one-liner (most powerful)
perl -pe 's/(?<=price:\s)\d+/0/g' file.txt    # Lookbehind works in Perl

Search-and-Replace Patterns

Code Refactoring

# Rename a variable across files
grep -rlP '\boldName\b' src/ | xargs sed -i 's/\boldName\b/newName/g'

# Convert var to const (JavaScript)
sed -i -E 's/\bvar\b/const/g' src/*.js

# Convert single quotes to double quotes
sed -i "s/'/\"/g" src/*.ts

# Add trailing commas to object properties
sed -i -E 's/^(\s+\w+:.+[^,])$/\1,/' config.json

# Update import paths
sed -i 's|from '\''../old-path/|from '\''../new-path/|g' src/*.ts

# Convert snake_case to camelCase (Python → JavaScript naming)
perl -pe 's/_([a-z])/uc($1)/ge' file.txt

Text Cleanup

# Remove trailing whitespace
sed -i 's/[[:space:]]*$//' file.txt

# Remove blank lines
sed -i '/^$/d' file.txt

# Remove duplicate blank lines (keep at most one)
sed -i '/^$/N;/^\n$/d' file.txt

# Trim leading and trailing whitespace from each line
sed -i 's/^[[:space:]]*//;s/[[:space:]]*$//' file.txt

# Remove HTML tags
sed 's/<[^>]*>//g' file.html

# Remove ANSI color codes
sed 's/\x1b\[[0-9;]*m//g' output.txt

Common Gotchas

Greedy vs lazy matching

Pattern: <.*>     Input: <b>bold</b>
Greedy  matches: <b>bold</b>     (entire string between first < and last >)
Lazy    matches: <b>              (stops at first >)
Pattern: <.*?>    (lazy version)

Escaping special characters

Characters that need escaping in regex: . * + ? ^ $ { } [ ] ( ) | \
In character classes []: only ] - ^ \ need escaping

# To match a literal dot:  \.
# To match a literal *:    \*
# To match a literal \:    \\
# To match [ or ]:         \[ or \]

Newlines and multiline

By default . does NOT match newline.
By default ^ and $ match start/end of STRING.

# To make . match newlines:
JavaScript: /pattern/s (dotAll flag)
Python: re.DOTALL or re.S
Go: (?s) inline flag

# To make ^ $ match line boundaries:
JavaScript: /pattern/m (multiline flag)
Python: re.MULTILINE or re.M
Go: (?m) inline flag

Backtracking and performance

# Catastrophic backtracking (avoid these patterns on untrusted input):
(a+)+        # Nested quantifiers
(a|a)+       # Overlapping alternation
(.*a){10}    # Ambiguous .* with repetition

# Safe alternatives:
[a]+         # Instead of (a+)+
a+           # Instead of (a|a)+
[^a]*a       # Possessive/atomic instead of .*a

Tips

Start simple and add complexity. \d+ is almost always enough — you rarely need [0-9]+.
Test your regex on real data, not just the happy path. Edge cases (empty strings, special characters, Unicode) break naive patterns.
Use non-capturing groups (?:...) when you don't need the captured value. It's slightly faster and cleaner.
In JavaScript, always use the g flag for matchAll and global replace. Without it, only the first match is found/replaced.
Go's regexp package uses RE2 (no lookahead/lookbehind). If you need those, use a different approach or the regexp2 package.
grep -P (PCRE) is the most powerful command-line regex. Use it over grep -E when you need lookahead, \d, or \b.
For complex patterns, use verbose mode (re.VERBOSE in Python, /x in Perl) with comments explaining each part.
Regex is the wrong tool for parsing HTML, XML, or JSON. Use a proper parser. Regex works for extracting simple values from these formats, not for structural parsing.

🔤 Regex Patterns OpenClaw Skill - ClawHub

What this skill does

Install

Full SKILL.md

Regex Patterns

When to Use

Quick Reference

Metacharacters

Quantifiers

Groups and Alternation

Lookahead and Lookbehind

Validation Patterns

Email

URL

IP Addresses

Phone Numbers

Dates and Times

Passwords (Strength Check)

UUIDs

Semantic Version

Parsing Patterns

Log Lines

Code Patterns

Data Extraction

Language-Specific Usage

JavaScript

Python

Go

Command Line (grep/sed)

Search-and-Replace Patterns

Code Refactoring

Text Cleanup

Common Gotchas

Greedy vs lazy matching

Escaping special characters

Newlines and multiline

Backtracking and performance

Tips

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