Agent skill

Performing Yara Rule Development For Detection

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing…

Apache-2.0Auto-check passedSecurity

Install Performing Yara Rule Development For Detection

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-yara-rule-development-for-detection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-yara-rule-development-for-detection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-yara-rule-development-for-detection .claude/skills/performing-yara-rule-development-for-detection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-yara-rule-development-for-detection
GitHub stars
34k
Token cost
~3k tokens
SKILL.md length
412 words
Files
8 (incl. scripts, references, assets)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing…

  • Works in 3 steps: Analyze Sample for Unique Patterns → Write and Test YARA Rules → Performance Testing and Optimization
  • Building detection signatures for threat hunting
  • SKILL.md covers Overview, When to Use, Prerequisites and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Performing Yara Rule Development For Detection is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing false positives. Use when building detection signatures for threat hunting, classifying malware families, or authoring rules from IOCs such as C2 URLs, mutex names, and encryption constants.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in Security, covering Security operations. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Building detection signatures for threat hunting
  • Classifying malware families
  • Authoring rules from IOCs such as C2 URLs
  • Encryption constants

Example prompts

  • “Use the performing-yara-rule-development-for-detection skill to develop precise YARA and YARA-X rules for malware detection by identifying unique…”
  • “/performing-yara-rule-development-for-detection”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Analyze Sample for Unique Patterns
  2. Write and Test YARA Rules
  3. Performance Testing and Optimization

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • virustotal.github.io
    • reversinglabs.com
    • cyberthreatintelligencenetwork.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Yara Rule Development For Detection loads about 3k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 412 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 412 words, ~2,970 tokens.

Download SKILL.mdSave it as .claude/skills/performing-yara-rule-development-for-detection/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
performing-yara-rule-development-for-detection
description
Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing false positives. Use when building detection signatures for threat hunting, classifying malware families, or authoring rules from IOCs such as C2 URLs, mutex names, and encryption constants.
domain
cybersecurity
subdomain
malware-analysis
tags
yara, malware-detection, signature-development, threat-hunting, pattern-matching, yara-x, indicator-development
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
DE.AE-02, RS.AN-03, ID.RA-01, DE.CM-01
mitre_attack
T1027, T1055, T1140, T1497

Performing YARA Rule Development for Detection

Overview

YARA is the pattern matching swiss knife for malware researchers, enabling identification and classification of malware based on textual or binary patterns. Effective YARA rules combine unique string patterns, byte sequences, PE header characteristics, import table analysis, and conditional logic to detect malware families while avoiding false positives. Modern YARA-X (rewritten in Rust, stable since June 2025) brings improved performance and new modules. Rules should target unpacked malware artifacts like hardcoded stack strings, C2 URLs, mutex names, encryption constants, and unique code sequences rather than packer signatures.

When to Use

  • When conducting security assessments that involve performing yara rule development for detection
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Python 3.9+ with yara-python library
  • YARA 4.5+ or YARA-X 0.10+
  • PE analysis tools (pefile, pestudio)
  • Hex editor for identifying unique byte patterns
  • Access to malware samples (VirusTotal, MalwareBazaar)
  • Understanding of PE file format, strings, and import tables

Key Concepts

Rule Structure

Every YARA rule consists of three sections: meta (optional descriptive metadata), strings (pattern definitions), and condition (matching logic). String types include text strings (ASCII/wide/nocase), hex patterns with wildcards and jumps, and regular expressions. Conditions combine string matches with file properties using boolean operators.

String Selection Strategy

Effective rules target patterns that are unique to the malware family and survive recompilation. Hardcoded stack strings are excellent choices because compilers embed them consistently. C2 domain patterns, custom encryption routines, unique error messages, and specific API call sequences provide stable detection anchors. Avoid compiler-generated boilerplate and common library strings.

Show full SKILL.md (141 more words)Show less
Performance Optimization

YARA evaluates conditions short-circuit style. Place the most discriminating and cheapest-to-evaluate conditions first. Use filesize limits to skip irrelevant files quickly. Minimize regex usage in favor of hex patterns. Use private rules as building blocks for complex detection logic without generating standalone matches.

Workflow

Step 1: Analyze Sample for Unique Patterns
python
#!/usr/bin/env python3
"""Extract candidate strings and byte patterns for YARA rule creation."""
import pefile
import re
import sys
from collections import Counter


def extract_strings(filepath, min_length=6):
    """Extract ASCII and wide strings from binary."""
    with open(filepath, 'rb') as f:
        data = f.read()

    # ASCII strings
    ascii_strings = re.findall(
        rb'[\x20-\x7e]{' + str(min_length).encode() + rb',}', data
    )

    # Wide (UTF-16LE) strings
    wide_strings = re.findall(
        rb'(?:[\x20-\x7e]\x00){' + str(min_length).encode() + rb',}', data
    )

    return {
        'ascii': [s.decode('ascii') for s in ascii_strings],
        'wide': [s.decode('utf-16-le') for s in wide_strings],
    }


def analyze_pe_imports(filepath):
    """Extract import table for API-based detection."""
    try:
        pe = pefile.PE(filepath)
    except pefile.PEFormatError:
        return []

    imports = []
    if hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in pe.DIRECTORY_ENTRY_IMPORT:
            dll_name = entry.dll.decode('utf-8', errors='replace')
            for imp in entry.imports:
                if imp.name:
                    func_name = imp.name.decode('utf-8', errors='replace')
                    imports.append(f"{dll_name}!{func_name}")
    return imports


def find_unique_byte_patterns(filepath, pattern_length=16):
    """Find unique byte sequences suitable for YARA hex patterns."""
    with open(filepath, 'rb') as f:
        data = f.read()

    try:
        pe = pefile.PE(filepath)
        # Focus on code section
        for section in pe.sections:
            if section.Characteristics & 0x20000000:  # IMAGE_SCN_MEM_EXECUTE
                code_start = section.PointerToRawData
                code_end = code_start + section.SizeOfRawData
                code_data = data[code_start:code_end]
                break
        else:
            code_data = data
    except Exception:
        code_data = data

    # Find byte patterns that appear exactly once
    patterns = []
    for i in range(0, len(code_data) - pattern_length, 4):
        pattern = code_data[i:i+pattern_length]
        if pattern.count(b'\x00') < pattern_length // 3:  # Skip null-heavy
            hex_pattern = ' '.join(f'{b:02X}' for b in pattern)
            patterns.append(hex_pattern)

    # Count frequency and return unique ones
    freq = Counter(patterns)
    unique = [p for p, count in freq.items() if count == 1]

    return unique[:20]  # Top 20 candidates


def suggest_rule_strings(filepath):
    """Suggest strings and patterns for YARA rule."""
    print(f"[+] Analyzing: {filepath}")

    # Extract strings
    strings = extract_strings(filepath)

    # Filter for suspicious/unique strings
    suspicious_keywords = [
        'http', 'https', 'cmd', 'powershell', 'mutex', 'pipe',
        'password', 'credential', 'inject', 'hook', 'debug',
        'sandbox', 'virtual', 'vmware', 'vbox',
    ]

    print("\n[+] Suspicious ASCII strings:")
    for s in strings['ascii']:
        if any(kw in s.lower() for kw in suspicious_keywords):
            print(f"  $ = \"{s}\" ascii")

    print("\n[+] Suspicious wide strings:")
    for s in strings['wide']:
        if any(kw in s.lower() for kw in suspicious_keywords):
            print(f"  $ = \"{s}\" wide")

    # Import analysis
    imports = analyze_pe_imports(filepath)
    suspicious_apis = [
        'VirtualAlloc', 'VirtualProtect', 'WriteProcessMemory',
        'CreateRemoteThread', 'NtUnmapViewOfSection', 'RtlMoveMemory',
        'OpenProcess', 'CreateToolhelp32Snapshot',
        'InternetOpenA', 'HttpSendRequestA',
        'CryptEncrypt', 'CryptDecrypt',
    ]

    print("\n[+] Suspicious imports:")
    for imp in imports:
        func = imp.split('!')[-1]
        if func in suspicious_apis:
            print(f"  {imp}")

    # Byte patterns
    print("\n[+] Candidate hex patterns:")
    patterns = find_unique_byte_patterns(filepath)
    for p in patterns[:5]:
        print(f"  $hex = {{ {p} }}")


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <sample_path>")
        sys.exit(1)
    suggest_rule_strings(sys.argv[1])
Step 2: Write and Test YARA Rules
python
import yara
import os

def create_yara_rule(rule_name, meta, strings, condition):
    """Generate a YARA rule from components."""
    meta_str = "\n".join(f'        {k} = "{v}"' for k, v in meta.items())
    strings_str = "\n".join(f"        {s}" for s in strings)

    rule = f"""rule {rule_name} {{
    meta:
{meta_str}

    strings:
{strings_str}

    condition:
        {condition}
}}"""
    return rule


def test_yara_rule(rule_text, test_dir):
    """Compile and test YARA rule against sample directory."""
    try:
        rules = yara.compile(source=rule_text)
    except yara.SyntaxError as e:
        print(f"[-] YARA syntax error: {e}")
        return None

    results = {"matches": [], "no_match": []}

    for filename in os.listdir(test_dir):
        filepath = os.path.join(test_dir, filename)
        if not os.path.isfile(filepath):
            continue

        matches = rules.match(filepath)
        if matches:
            results["matches"].append({
                "file": filename,
                "rules": [m.rule for m in matches],
            })
        else:
            results["no_match"].append(filename)

    print(f"[+] Matches: {len(results['matches'])}")
    print(f"[-] No match: {len(results['no_match'])}")
    return results


# Example: Create a rule for a hypothetical malware family
example_rule = create_yara_rule(
    rule_name="MalwareFamily_Variant_A",
    meta={
        "description": "Detects MalwareFamily Variant A",
        "author": "Malware Analysis Team",
        "date": "2025-01-01",
        "hash": "abc123...",
        "tlp": "WHITE",
    },
    strings=[
        '$mutex = "Global\\\\UniqueM4lwareMutex" ascii wide',
        '$c2_pattern = /https?:\\/\\/[a-z]{5,10}\\.(xyz|top|buzz)\\/gate\\.php/',
        '$api1 = "VirtualAllocEx" ascii',
        '$api2 = "WriteProcessMemory" ascii',
        '$api3 = "CreateRemoteThread" ascii',
        '$hex_decrypt = { 8B 45 ?? 33 C1 89 45 ?? 83 C1 04 }',
        '$pdb = "C:\\\\Users\\\\" ascii',
    ],
    condition=(
        'uint16(0) == 0x5A4D and filesize < 2MB and '
        '($mutex or $c2_pattern) and '
        '2 of ($api*) and '
        '$hex_decrypt'
    ),
)

print(example_rule)
Step 3: Performance Testing and Optimization
python
import time

def benchmark_rule(rule_text, scan_directory, iterations=3):
    """Benchmark YARA rule scan performance."""
    rules = yara.compile(source=rule_text)

    files = []
    for root, _, filenames in os.walk(scan_directory):
        for f in filenames:
            files.append(os.path.join(root, f))

    print(f"[+] Benchmarking against {len(files)} files "
          f"({iterations} iterations)")

    times = []
    for i in range(iterations):
        start = time.perf_counter()
        matches = 0
        for filepath in files:
            try:
                result = rules.match(filepath)
                if result:
                    matches += 1
            except Exception:
                pass
        elapsed = time.perf_counter() - start
        times.append(elapsed)
        print(f"  Iteration {i+1}: {elapsed:.3f}s ({matches} matches)")

    avg_time = sum(times) / len(times)
    files_per_sec = len(files) / avg_time
    print(f"\n[+] Average: {avg_time:.3f}s ({files_per_sec:.0f} files/sec)")
    return avg_time

Validation Criteria

  • YARA rules compile without syntax errors
  • Rules detect target malware family samples with zero false negatives
  • False positive rate below 0.1% when scanned against clean file corpus
  • Rule performance allows scanning 1000+ files per second
  • Rules survive minor malware modifications (recompilation, string changes)
  • Metadata includes hash, author, date, description, and TLP marking

References

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/performing-yara-rule-development-for-detection of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • assets/template.md
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py
  • scripts/process.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Performing Yara Rule Development For Detection

What does Performing Yara Rule Development For Detection do?

Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing…. Performing Yara Rule Development For Detection is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Develops precise YARA and YARA-X rules for malware detection by identifying unique strings, byte sequences, PE header traits, and behavioral indicators in unpacked malware artifacts while minimizing false positives.

When should I use Performing Yara Rule Development For Detection?

Performing Yara Rule Development For Detection fits situations like: building detection signatures for threat hunting; classifying malware families; authoring rules from IOCs such as C2 URLs; encryption constants.

How do I install Performing Yara Rule Development For Detection in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-yara-rule-development-for-detection -a claude-code`. Or copy the skill folder (skills/performing-yara-rule-development-for-detection in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-yara-rule-development-for-detection in your project. Claude Code loads it when a task matches its description.

How do I install Performing Yara Rule Development For Detection in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-yara-rule-development-for-detection -a codex`. Or copy the skill folder (skills/performing-yara-rule-development-for-detection in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-yara-rule-development-for-detection in your project. Codex loads it when a task matches its description.

Can I use Performing Yara Rule Development For Detection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-yara-rule-development-for-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-yara-rule-development-for-detection, .gemini/skills/performing-yara-rule-development-for-detection, .github/skills/performing-yara-rule-development-for-detection and .opencode/skills/performing-yara-rule-development-for-detection in your project.

What does Performing Yara Rule Development For Detection need to run?

Going by SKILL.md and its folder, Performing Yara Rule Development For Detection needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Performing Yara Rule Development For Detection access the network?

SKILL.md names 4 domains. As links in the text: github.com, virustotal.github.io, reversinglabs.com and cyberthreatintelligencenetwork.com. This is read from the text; nothing was executed.

Is Performing Yara Rule Development For Detection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Yara Rule Development For Detection use?

Performing Yara Rule Development For Detection is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Yara Rule Development For Detection use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Performing Yara Rule Development For Detection?

Skills that share tags, products or a category with Performing Yara Rule Development For Detection: Security Alert Triage (elastic/agent-skills, 592 stars), Kubernetes Network Security Audit (kubeshark/kubeshark, 12k stars), Security Detection Rule Management (elastic/agent-skills, 592 stars) and Chaitin CLI (chaitin/chaitin-cli, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performing Yara Rule Development For Detection?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.