Best fit
- Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies.
affaan-m/ECC
Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies.
npx skills add https://github.com/affaan-m/ECC --skill "skills/perl-security"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: Comprehensive security guidelines for Perl applications covering input validation, injection prevention, and secure coding practices.
npx skills add https://github.com/affaan-m/ECC --skill "skills/perl-security"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
Review the “Safe Process Execution” section in the pinned source before continuing.
Handling user input in Perl applications
Start with taint-aware input boundaries, then move outward: validate and untaint inputs, keep filesystem and process execution constrained, and use parameterized DBI queries everywhere. The examples below show the safe defaults this skill expects you to apply before shipping Per…
Perl's taint mode (-T) tracks data from external sources and prevents it from being used in unsafe operations without explicit validation.
perl !/usr/bin/perl -T use v5.36;
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use perl-security to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned perl-security source to [task]. Pay particular attention to these source sections: “Safe Process Execution”, “When to Activate”, “How It Works”, “Taint Mode”, “Enabling Taint Mode”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current perl-security result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “Safe Process Execution” has been checked.
The source section “When to Activate” has been checked.
The source section “How It Works” has been checked.
The source section “Taint Mode” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
テイントモード、入力バリデーション、安全なプロセス実行、DBIパラメータ化クエリ、Webセキュリティ(XSS/SQLi/CSRF)、perlcriticセキュリティポリシーを網羅する包括的なPerlセキュリティ。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detail全面的Perl安全指南,涵盖污染模式、输入验证、安全进程执行、DBI参数化查询、Web安全(XSS/SQLi/CSRF)以及perlcritic安全策略。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
Comprehensive security guidelines for Perl applications covering input validation, injection prevention, and secure coding practices.
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/perl-security". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
テイントモード、入力バリデーション、安全なプロセス実行、DBIパラメータ化クエリ、Webセキュリティ(XSS/SQLi/CSRF)、perlcriticセキュリティポリシーを網羅する包括的なPerlセキュリティ。
全面的Perl安全指南,涵盖污染模式、输入验证、安全进程执行、DBI参数化查询、Web安全(XSS/SQLi/CSRF)以及perlcritic安全策略。
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Comprehensive security guidelines for Perl applications covering input validation, injection prevention, and secure coding practices.
Start with taint-aware input boundaries, then move outward: validate and untaint inputs, keep filesystem and process execution constrained, and use parameterized DBI queries everywhere. The examples below show the safe defaults this skill expects you to apply before shipping Perl code that touches user input, the shell, or the network.
Perl's taint mode (-T) tracks data from external sources and prevents it from being used in unsafe operations without explicit validation.
#!/usr/bin/perl -T
use v5.36;
# Tainted: anything from outside the program
my $input = $ARGV[0]; # Tainted
my $env_path = $ENV{PATH}; # Tainted
my $form = <STDIN>; # Tainted
my $query = $ENV{QUERY_STRING}; # Tainted
# Sanitize PATH early (required in taint mode)
$ENV{PATH} = '/usr/local/bin:/usr/bin:/bin';
delete @ENV{qw(IFS CDPATH ENV BASH_ENV)};
use v5.36;
# Good: Validate and untaint with a specific regex
sub untaint_username($input) {
if ($input =~ /^([a-zA-Z0-9_]{3,30})$/) {
return $1; # $1 is untainted
}
die "Invalid username: must be 3-30 alphanumeric characters\n";
}
# Good: Validate and untaint a file path
sub untaint_filename($input) {
if ($input =~ m{^([a-zA-Z0-9._-]+)$}) {
return $1;
}
die "Invalid filename: contains unsafe characters\n";
}
# Bad: Overly permissive untainting (defeats the purpose)
sub bad_untaint($input) {
$input =~ /^(.*)$/s;
return $1; # Accepts ANYTHING — pointless
}
use v5.36;
# Good: Allowlist — define exactly what's permitted
sub validate_sort_field($field) {
my %allowed = map { $_ => 1 } qw(name email created_at updated_at);
die "Invalid sort field: $field\n" unless $allowed{$field};
return $field;
}
# Good: Validate with specific patterns
sub validate_email($email) {
if ($email =~ /^([a-zA-Z0-9._%+-]+\@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})$/) {
return $1;
}
die "Invalid email address\n";
}
sub validate_integer($input) {
if ($input =~ /^(-?\d{1,10})$/) {
return $1 + 0; # Coerce to number
}
die "Invalid integer\n";
}
# Bad: Blocklist — always incomplete
sub bad_validate($input) {
die "Invalid" if $input =~ /[<>"';&|]/; # Misses encoded attacks
return $input;
}
use v5.36;
sub validate_comment($text) {
die "Comment is required\n" unless length($text) > 0;
die "Comment exceeds 10000 chars\n" if length($text) > 10_000;
return $text;
}
Catastrophic backtracking occurs with nested quantifiers on overlapping patterns.
use v5.36;
# Bad: Vulnerable to ReDoS (exponential backtracking)
my $bad_re = qr/^(a+)+$/; # Nested quantifiers
my $bad_re2 = qr/^([a-zA-Z]+)*$/; # Nested quantifiers on class
my $bad_re3 = qr/^(.*?,){10,}$/; # Repeated greedy/lazy combo
# Good: Rewrite without nesting
my $good_re = qr/^a+$/; # Single quantifier
my $good_re2 = qr/^[a-zA-Z]+$/; # Single quantifier on class
# Good: Use possessive quantifiers or atomic groups to prevent backtracking
my $safe_re = qr/^[a-zA-Z]++$/; # Possessive (5.10+)
my $safe_re2 = qr/^(?>a+)$/; # Atomic group
# Good: Enforce timeout on untrusted patterns
use POSIX qw(alarm);
sub safe_match($string, $pattern, $timeout = 2) {
my $matched;
eval {
local $SIG{ALRM} = sub { die "Regex timeout\n" };
alarm($timeout);
$matched = $string =~ $pattern;
alarm(0);
};
alarm(0);
die $@ if $@;
return $matched;
}
use v5.36;
# Good: Three-arg open, lexical filehandle, check return
sub read_file($path) {
open my $fh, '<:encoding(UTF-8)', $path
or die "Cannot open '$path': $!\n";
local $/;
my $content = <$fh>;
close $fh;
return $content;
}
# Bad: Two-arg open with user data (command injection)
sub bad_read($path) {
open my $fh, $path; # If $path = "|rm -rf /", runs command!
open my $fh, "< $path"; # Shell metacharacter injection
}
use v5.36;
use Fcntl qw(:DEFAULT :flock);
use File::Spec;
use Cwd qw(realpath);
# Atomic file creation
sub create_file_safe($path) {
sysopen(my $fh, $path, O_WRONLY | O_CREAT | O_EXCL, 0600)
or die "Cannot create '$path': $!\n";
return $fh;
}
# Validate path stays within allowed directory
sub safe_path($base_dir, $user_path) {
my $real = realpath(File::Spec->catfile($base_dir, $user_path))
// die "Path does not exist\n";
my $base_real = realpath($base_dir)
// die "Base dir does not exist\n";
die "Path traversal blocked\n" unless $real =~ /^\Q$base_real\E(?:\/|\z)/;
return $real;
}
Use File::Temp for temporary files (tempfile(UNLINK => 1)) and flock(LOCK_EX) to prevent race conditions.
use v5.36;
# Good: List form — no shell interpolation
sub run_command(@cmd) {
system(@cmd) == 0
or die "Command failed: @cmd\n";
}
run_command('grep', '-r', $user_pattern, '/var/log/app/');
# Good: Capture output safely with IPC::Run3
use IPC::Run3;
sub capture_output(@cmd) {
my ($stdout, $stderr);
run3(\@cmd, \undef, \$stdout, \$stderr);
if ($?) {
die "Command failed (exit $?): $stderr\n";
}
return $stdout;
}
# Bad: String form — shell injection!
sub bad_search($pattern) {
system("grep -r '$pattern' /var/log/app/"); # If $pattern = "'; rm -rf / #"
}
# Bad: Backticks with interpolation
my $output = `ls $user_dir`; # Shell injection risk
Also use Capture::Tiny for capturing stdout/stderr from external commands safely.
use v5.36;
use DBI;
my $dbh = DBI->connect($dsn, $user, $pass, {
RaiseError => 1,
PrintError => 0,
AutoCommit => 1,
});
# Good: Parameterized queries — always use placeholders
sub find_user($dbh, $email) {
my $sth = $dbh->prepare('SELECT * FROM users WHERE email = ?');
$sth->execute($email);
return $sth->fetchrow_hashref;
}
sub search_users($dbh, $name, $status) {
my $sth = $dbh->prepare(
'SELECT * FROM users WHERE name LIKE ? AND status = ? ORDER BY name'
);
$sth->execute("%$name%", $status);
return $sth->fetchall_arrayref({});
}
# Bad: String interpolation in SQL (SQLi vulnerability!)
sub bad_find($dbh, $email) {
my $sth = $dbh->prepare("SELECT * FROM users WHERE email = '$email'");
# If $email = "' OR 1=1 --", returns all users
$sth->execute;
return $sth->fetchrow_hashref;
}
use v5.36;
# Good: Validate column names against an allowlist
sub order_by($dbh, $column, $direction) {
my %allowed_cols = map { $_ => 1 } qw(name email created_at);
my %allowed_dirs = map { $_ => 1 } qw(ASC DESC);
die "Invalid column: $column\n" unless $allowed_cols{$column};
die "Invalid direction: $direction\n" unless $allowed_dirs{uc $direction};
my $sth = $dbh->prepare("SELECT * FROM users ORDER BY $column $direction");
$sth->execute;
return $sth->fetchall_arrayref({});
}
# Bad: Directly interpolating user-chosen column
sub bad_order($dbh, $column) {
$dbh->prepare("SELECT * FROM users ORDER BY $column"); # SQLi!
}
use v5.36;
# DBIx::Class generates safe parameterized queries
my @users = $schema->resultset('User')->search({
status => 'active',
email => { -like => '%@example.com' },
}, {
order_by => { -asc => 'name' },
rows => 50,
});
use v5.36;
use HTML::Entities qw(encode_entities);
use URI::Escape qw(uri_escape_utf8);
# Good: Encode output for HTML context
sub safe_html($user_input) {
return encode_entities($user_input);
}
# Good: Encode for URL context
sub safe_url_param($value) {
return uri_escape_utf8($value);
}
# Good: Encode for JSON context
use JSON::MaybeXS qw(encode_json);
sub safe_json($data) {
return encode_json($data); # Handles escaping
}
# Template auto-escaping (Mojolicious)
# <%= $user_input %> — auto-escaped (safe)
# <%== $raw_html %> — raw output (dangerous, use only for trusted content)
# Template auto-escaping (Template Toolkit)
# [% user_input | html %] — explicit HTML encoding
# Bad: Raw output in HTML
sub bad_html($input) {
print "<div>$input</div>"; # XSS if $input contains <script>
}
use v5.36;
use Crypt::URandom qw(urandom);
use MIME::Base64 qw(encode_base64url);
sub generate_csrf_token() {
return encode_base64url(urandom(32));
}
Use constant-time comparison when verifying tokens. Most web frameworks (Mojolicious, Dancer2, Catalyst) provide built-in CSRF protection — prefer those over hand-rolled solutions.
use v5.36;
# Mojolicious session + headers
$app->secrets(['long-random-secret-rotated-regularly']);
$app->sessions->secure(1); # HTTPS only
$app->sessions->samesite('Lax');
$app->hook(after_dispatch => sub ($c) {
$c->res->headers->header('X-Content-Type-Options' => 'nosniff');
$c->res->headers->header('X-Frame-Options' => 'DENY');
$c->res->headers->header('Content-Security-Policy' => "default-src 'self'");
$c->res->headers->header('Strict-Transport-Security' => 'max-age=31536000; includeSubDomains');
});
Always encode output for its context: HTML::Entities::encode_entities() for HTML, URI::Escape::uri_escape_utf8() for URLs, JSON::MaybeXS::encode_json() for JSON.
requires 'DBI', '== 1.643';# .perlcriticrc — security-focused configuration
severity = 3
theme = security + core
# Require three-arg open
[InputOutput::RequireThreeArgOpen]
severity = 5
# Require checked system calls
[InputOutput::RequireCheckedSyscalls]
functions = :builtins
severity = 4
# Prohibit string eval
[BuiltinFunctions::ProhibitStringyEval]
severity = 5
# Prohibit backtick operators
[InputOutput::ProhibitBacktickOperators]
severity = 4
# Require taint checking in CGI
[Modules::RequireTaintChecking]
severity = 5
# Prohibit two-arg open
[InputOutput::ProhibitTwoArgOpen]
severity = 5
# Prohibit bare-word filehandles
[InputOutput::ProhibitBarewordFileHandles]
severity = 5
# Check a file
perlcritic --severity 3 --theme security lib/MyApp/Handler.pm
# Check entire project
perlcritic --severity 3 --theme security lib/
# CI integration
perlcritic --severity 4 --theme security --quiet lib/ || exit 1
| Check | What to Verify |
|---|---|
| Taint mode | -T flag on CGI/web scripts |
| Input validation | Allowlist patterns, length limits |
| File operations | Three-arg open, path traversal checks |
| Process execution | List-form system, no shell interpolation |
| SQL queries | DBI placeholders, never interpolate |
| HTML output | encode_entities(), template auto-escape |
| CSRF tokens | Generated, verified on state-changing requests |
| Session config | Secure, HttpOnly, SameSite cookies |
| HTTP headers | CSP, X-Frame-Options, HSTS |
| Dependencies | Pinned versions, audited modules |
| Regex safety | No nested quantifiers, anchored patterns |
| Error messages | No stack traces or paths leaked to users |
# 1. Two-arg open with user data (command injection)
open my $fh, $user_input; # CRITICAL vulnerability
# 2. String-form system (shell injection)
system("convert $user_file output.png"); # CRITICAL vulnerability
# 3. SQL string interpolation
$dbh->do("DELETE FROM users WHERE id = $id"); # SQLi
# 4. eval with user input (code injection)
eval $user_code; # Remote code execution
# 5. Trusting $ENV without sanitizing
my $path = $ENV{UPLOAD_DIR}; # Could be manipulated
system("ls $path"); # Double vulnerability
# 6. Disabling taint without validation
($input) = $input =~ /(.*)/s; # Lazy untaint — defeats purpose
# 7. Raw user data in HTML
print "<div>Welcome, $username!</div>"; # XSS
# 8. Unvalidated redirects
print $cgi->redirect($user_url); # Open redirect
Remember: Perl's flexibility is powerful but requires discipline. Use taint mode for web-facing code, validate all input with allowlists, use DBI placeholders for every query, and encode all output for its context. Defense in depth — never rely on a single layer.