Create Sigma Rule
TracecatHQ/tracecat
Turns a threat report, a malware analysis, vendor tool documentation, or a raw log sample into draft Sigma detection rules, validated against sigma-cli where a shell exists and labelled "not…
Agent skill
Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipeline --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .claude/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.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/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .claude/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipelineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipeline --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .agents/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .agents/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipeline --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .cursor/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .cursor/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/building-automated-malware-submission-pipeline--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipeline --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .gemini/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .gemini/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipelineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .github/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .github/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-automated-malware-submission-pipeline --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/building-automated-malware-submission-pipeline .opencode/skills/building-automated-malware-submission-pipeline && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "building-automated-malware-submission-pipeline" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-automated-malware-submission-pipeline into .opencode/skills/building-automated-malware-submission-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-automated-malware-submission-pipeline", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
building-automated-malware-submission-pipelineBuilds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and…
Building Automated Malware Submission Pipeline is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).
It sits in Security, covering Security operations and Reverse engineering and malware. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
mb-api.abuse.chjbxcloud.joesecurity.orgFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Building Automated Malware Submission Pipeline loads about 4.7k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 406 words of instructions outside code blocks.
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.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 406 words, ~4,731 tokens.
.claude/skills/building-automated-malware-submission-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Use this skill when:
Do not use for analyzing live malware samples in production environments — always use isolated sandbox infrastructure.
requests, vt-py, pefile librariesCollect suspicious files from multiple sources:
import requests
import hashlib
import os
from pathlib import Path
from datetime import datetime
class MalwareCollector:
def __init__(self, quarantine_dir="/opt/malware_quarantine"):
self.quarantine_dir = Path(quarantine_dir)
self.quarantine_dir.mkdir(exist_ok=True)
def collect_from_edr(self, edr_api_url, api_token):
"""Pull quarantined files from CrowdStrike Falcon"""
headers = {"Authorization": f"Bearer {api_token}"}
# Get recent quarantine events
response = requests.get(
f"{edr_api_url}/quarantine/queries/quarantined-files/v1",
headers=headers,
params={"filter": "state:'quarantined'", "limit": 50}
)
file_ids = response.json()["resources"]
for file_id in file_ids:
# Download quarantined file
dl_response = requests.get(
f"{edr_api_url}/quarantine/entities/quarantined-files/v1",
headers=headers,
params={"ids": file_id}
)
file_data = dl_response.content
sha256 = hashlib.sha256(file_data).hexdigest()
filepath = self.quarantine_dir / f"{sha256}.sample"
filepath.write_bytes(file_data)
yield {"sha256": sha256, "path": str(filepath), "source": "edr"}
def collect_from_email_gateway(self, smtp_quarantine_path):
"""Pull attachments from email gateway quarantine"""
import email
from email import policy
for eml_file in Path(smtp_quarantine_path).glob("*.eml"):
msg = email.message_from_binary_file(
eml_file.open("rb"), policy=policy.default
)
for attachment in msg.iter_attachments():
content = attachment.get_content()
if isinstance(content, str):
content = content.encode()
sha256 = hashlib.sha256(content).hexdigest()
filename = attachment.get_filename() or "unknown"
filepath = self.quarantine_dir / f"{sha256}.sample"
filepath.write_bytes(content)
yield {
"sha256": sha256,
"path": str(filepath),
"source": "email",
"original_filename": filename,
"sender": msg["From"],
"subject": msg["Subject"]
}
def compute_hashes(self, filepath):
"""Calculate MD5, SHA1, SHA256 for a file"""
with open(filepath, "rb") as f:
content = f.read()
return {
"md5": hashlib.md5(content).hexdigest(),
"sha1": hashlib.sha1(content).hexdigest(),
"sha256": hashlib.sha256(content).hexdigest(),
"size": len(content)
}Check if the file is already known before sandbox submission:
import vt
class MalwarePreScreener:
def __init__(self, vt_api_key, mb_api_url="https://mb-api.abuse.ch/api/v1/"):
self.vt_client = vt.Client(vt_api_key)
self.mb_api_url = mb_api_url
def check_virustotal(self, sha256):
"""Lookup hash in VirusTotal"""
try:
file_obj = self.vt_client.get_object(f"/files/{sha256}")
stats = file_obj.last_analysis_stats
return {
"found": True,
"malicious": stats.get("malicious", 0),
"suspicious": stats.get("suspicious", 0),
"undetected": stats.get("undetected", 0),
"total": sum(stats.values()),
"threat_label": getattr(file_obj, "popular_threat_classification", {}).get(
"suggested_threat_label", "Unknown"
),
"type": getattr(file_obj, "type_description", "Unknown")
}
except vt.APIError:
return {"found": False}
def check_malwarebazaar(self, sha256):
"""Lookup hash in MalwareBazaar"""
response = requests.post(
self.mb_api_url,
data={"query": "get_info", "hash": sha256}
)
data = response.json()
if data["query_status"] == "ok":
entry = data["data"][0]
return {
"found": True,
"signature": entry.get("signature", "Unknown"),
"tags": entry.get("tags", []),
"file_type": entry.get("file_type", "Unknown"),
"first_seen": entry.get("first_seen", "Unknown")
}
return {"found": False}
def pre_screen(self, sha256):
"""Run all pre-screening checks"""
vt_result = self.check_virustotal(sha256)
mb_result = self.check_malwarebazaar(sha256)
verdict = "UNKNOWN"
if vt_result["found"] and vt_result.get("malicious", 0) > 10:
verdict = "KNOWN_MALICIOUS"
elif vt_result["found"] and vt_result.get("malicious", 0) == 0:
verdict = "LIKELY_CLEAN"
return {
"sha256": sha256,
"virustotal": vt_result,
"malwarebazaar": mb_result,
"pre_screen_verdict": verdict,
"needs_sandbox": verdict == "UNKNOWN"
}
def close(self):
self.vt_client.close()Cuckoo Sandbox Submission:
class SandboxSubmitter:
def __init__(self, cuckoo_url="http://cuckoo.internal:8090"):
self.cuckoo_url = cuckoo_url
def submit_to_cuckoo(self, filepath, timeout=300):
"""Submit file to Cuckoo Sandbox"""
with open(filepath, "rb") as f:
response = requests.post(
f"{self.cuckoo_url}/tasks/create/file",
files={"file": f},
data={
"timeout": timeout,
"options": "procmemdump=yes,route=none",
"priority": 2,
"machine": "win10_x64"
}
)
task_id = response.json()["task_id"]
return task_id
def wait_for_analysis(self, task_id, poll_interval=30, max_wait=600):
"""Wait for sandbox analysis to complete"""
import time
elapsed = 0
while elapsed < max_wait:
response = requests.get(f"{self.cuckoo_url}/tasks/view/{task_id}")
status = response.json()["task"]["status"]
if status == "reported":
return self.get_report(task_id)
elif status == "failed_analysis":
return {"error": "Analysis failed"}
time.sleep(poll_interval)
elapsed += poll_interval
return {"error": "Analysis timed out"}
def get_report(self, task_id):
"""Retrieve analysis report"""
response = requests.get(f"{self.cuckoo_url}/tasks/report/{task_id}")
report = response.json()
# Extract key indicators
return {
"task_id": task_id,
"score": report.get("info", {}).get("score", 0),
"signatures": [
{"name": s["name"], "severity": s["severity"], "description": s["description"]}
for s in report.get("signatures", [])
],
"network": {
"dns": [d["request"] for d in report.get("network", {}).get("dns", [])],
"http": [
{"url": h["uri"], "method": h["method"]}
for h in report.get("network", {}).get("http", [])
],
"hosts": report.get("network", {}).get("hosts", [])
},
"dropped_files": [
{"name": f["name"], "sha256": f["sha256"], "size": f["size"]}
for f in report.get("dropped", [])
],
"processes": [
{"name": p["process_name"], "pid": p["pid"], "command_line": p.get("command_line", "")}
for p in report.get("behavior", {}).get("processes", [])
],
"registry_keys": [
k for k in report.get("behavior", {}).get("summary", {}).get("regkey_written", [])
]
}
def submit_to_joesandbox(self, filepath, joe_api_key, joe_url="https://jbxcloud.joesecurity.org/api"):
"""Submit to Joe Sandbox Cloud"""
with open(filepath, "rb") as f:
response = requests.post(
f"{joe_url}/v2/submission/new",
headers={"Authorization": f"Bearer {joe_api_key}"},
files={"sample": f},
data={
"systems": "w10_64",
"internet-access": False,
"report-cache": True
}
)
return response.json()["data"]["webid"]class VerdictGenerator:
def __init__(self):
self.malicious_threshold = 7 # Cuckoo score threshold
def generate_verdict(self, pre_screen, sandbox_report):
"""Combine pre-screening and sandbox results for final verdict"""
iocs = {
"ips": [],
"domains": [],
"urls": [],
"hashes": [],
"registry_keys": [],
"files_dropped": []
}
# Extract IOCs from sandbox report
if sandbox_report:
iocs["domains"] = sandbox_report.get("network", {}).get("dns", [])
iocs["ips"] = sandbox_report.get("network", {}).get("hosts", [])
iocs["urls"] = [
h["url"] for h in sandbox_report.get("network", {}).get("http", [])
]
iocs["hashes"] = [
f["sha256"] for f in sandbox_report.get("dropped_files", [])
]
iocs["registry_keys"] = sandbox_report.get("registry_keys", [])[:10]
iocs["files_dropped"] = sandbox_report.get("dropped_files", [])
# Determine verdict
vt_malicious = pre_screen.get("virustotal", {}).get("malicious", 0)
sandbox_score = sandbox_report.get("score", 0) if sandbox_report else 0
sig_count = len(sandbox_report.get("signatures", [])) if sandbox_report else 0
combined_score = (vt_malicious * 2) + (sandbox_score * 10) + (sig_count * 5)
if combined_score >= 100:
verdict = "MALICIOUS"
confidence = "HIGH"
elif combined_score >= 50:
verdict = "SUSPICIOUS"
confidence = "MEDIUM"
elif combined_score >= 20:
verdict = "POTENTIALLY_UNWANTED"
confidence = "LOW"
else:
verdict = "CLEAN"
confidence = "HIGH"
return {
"verdict": verdict,
"confidence": confidence,
"combined_score": combined_score,
"iocs": iocs,
"vt_detections": vt_malicious,
"sandbox_score": sandbox_score,
"signatures": sandbox_report.get("signatures", []) if sandbox_report else []
}def push_to_splunk(verdict_result, splunk_url, splunk_token):
"""Send malware analysis verdict to Splunk HEC"""
import json
event = {
"sourcetype": "malware_analysis",
"source": "malware_pipeline",
"event": {
"sha256": verdict_result["sha256"],
"verdict": verdict_result["verdict"],
"confidence": verdict_result["confidence"],
"score": verdict_result["combined_score"],
"vt_detections": verdict_result["vt_detections"],
"sandbox_score": verdict_result["sandbox_score"],
"malware_family": verdict_result.get("threat_label", "Unknown"),
"iocs": verdict_result["iocs"],
"signatures": [s["name"] for s in verdict_result["signatures"]]
}
}
response = requests.post(
f"{splunk_url}/services/collector/event",
headers={
"Authorization": f"Splunk {splunk_token}",
"Content-Type": "application/json"
},
json=event,
verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true", # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
)
return response.status_code == 200
def push_iocs_to_blocklist(iocs, firewall_api):
"""Push extracted IOCs to blocking infrastructure"""
for ip in iocs.get("ips", []):
requests.post(
f"{firewall_api}/block",
json={"type": "ip", "value": ip, "action": "block", "source": "malware_pipeline"}
)
for domain in iocs.get("domains", []):
requests.post(
f"{firewall_api}/block",
json={"type": "domain", "value": domain, "action": "sinkhole", "source": "malware_pipeline"}
)def run_malware_pipeline(sample_path, config):
"""Execute full malware analysis pipeline"""
collector = MalwareCollector()
screener = MalwarePreScreener(config["vt_key"])
submitter = SandboxSubmitter(config["cuckoo_url"])
generator = VerdictGenerator()
# Step 1: Hash and pre-screen
hashes = collector.compute_hashes(sample_path)
pre_screen = screener.pre_screen(hashes["sha256"])
# Step 2: Submit to sandbox if unknown
sandbox_report = None
if pre_screen["needs_sandbox"]:
task_id = submitter.submit_to_cuckoo(sample_path)
sandbox_report = submitter.wait_for_analysis(task_id)
# Step 3: Generate verdict
verdict = generator.generate_verdict(pre_screen, sandbox_report)
verdict["sha256"] = hashes["sha256"]
verdict["threat_label"] = pre_screen.get("virustotal", {}).get("threat_label", "Unknown")
# Step 4: Push to SIEM
push_to_splunk(verdict, config["splunk_url"], config["splunk_token"])
# Step 5: Block if malicious
if verdict["verdict"] == "MALICIOUS":
push_iocs_to_blocklist(verdict["iocs"], config["firewall_api"])
screener.close()
return verdict| Term | Definition |
|---|---|
| Dynamic Analysis | Executing malware in a sandbox to observe runtime behavior (process creation, network, file system changes) |
| Static Analysis | Examining malware without execution (hash lookup, string analysis, PE header inspection) |
| Sandbox Evasion | Techniques malware uses to detect sandbox environments and alter behavior to avoid analysis |
| IOC Extraction | Automated process of identifying network indicators, file artifacts, and registry changes from sandbox reports |
| Multi-AV Scanning | Submitting samples to multiple antivirus engines (VirusTotal) for consensus-based detection |
| Verdict | Final classification of a sample: Malicious, Suspicious, Potentially Unwanted, or Clean |
MALWARE ANALYSIS REPORT — Pipeline Submission
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Sample: invoice_march.docx
SHA256: a1b2c3d4e5f6a7b8...
File Type: Microsoft Word Document (macro-enabled)
Pre-Screening:
VirusTotal: 34/72 malicious (Emotet.Downloader)
MalwareBazaar: Tags: emotet, macro, downloader
Sandbox Analysis (Cuckoo):
Score: 9.2/10 (MALICIOUS)
Signatures:
- Macro executes PowerShell download cradle (severity: 8)
- Process injection into explorer.exe (severity: 9)
- Connects to known Emotet C2 server (severity: 9)
Extracted IOCs:
C2 IPs: 185.234.218[.]50:8080, 45.77.123[.]45:443
Domains: update-service[.]evil[.]com
Dropped Files: payload.dll (SHA256: b2c3d4e5...)
Registry: HKCU\Software\Microsoft\Windows\CurrentVersion\Run\Update
VERDICT: MALICIOUS (Emotet Downloader) — Confidence: HIGH
ACTIONS:
[DONE] IOCs pushed to Splunk threat intel
[DONE] C2 IPs blocked on firewall
[DONE] Domain sinkholed on DNS
[DONE] Hash blocked on endpoint© 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
SKILL.md and 3 other files (scripts, references) in skills/building-automated-malware-submission-pipeline of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Building Automated Malware Submission Pipeline next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Building Automated Malware Submission Pipeline this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Create Sigma RuleTracecatHQ/tracecat | 3.8k | — | ~16k | Automated safety check: Pass | MIT | |
| YARA-X Rule Authoringtrailofbits/skills | 7.5k | — | ~5.9k | Automated safety check: Pass | CC-BY-SA-4.0 | |
| Incident Response NetworkLeoYeAI/openclaw-master-skills | 2.2k | — | ~5k | Automated safety check: Pass | Apache-2.0 | |
| vphone600 Kernel Symbol AnalysisLakr233/vphone-cli | 15k | — | ~530 | Automated safety check: Pass | MIT | |
| Webhome Extension Builderwebhtv/webhtv | 1.7k | — | ~2.8k | Automated safety check: Pass | GPL-3.0 |
TracecatHQ/tracecat
Turns a threat report, a malware analysis, vendor tool documentation, or a raw log sample into draft Sigma detection rules, validated against sigma-cli where a shell exists and labelled "not…
trailofbits/skills
Guides writing, reviewing and tuning YARA-X malware detection rules, covering string selection, performance, false-positive reduction and migration from legacy YARA.
LeoYeAI/openclaw-master-skills
Network forensics evidence collection and analysis during security incidents.
Lakr233/vphone-cli
Looks up symbols and addresses in vphone600 release and research kernel datasets, and cross-references XNU source, with findings that separate fact from inference.
webhtv/webhtv
Build, review, debug, reverse-engineer, and package WebHome injected extension scripts for FongMi/WebHome App WebView pages.
boyang-hu/website-rebuild-skill
1:1 rebuild of award-winning creative websites (WebGL / scroll-animation / portfolio sites).
mukul975/Anthropic-Cybersecurity-Skills
Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
mukul975/Anthropic-Cybersecurity-Skills
Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
mukul975/Anthropic-Cybersecurity-Skills
Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.
mukul975/Anthropic-Cybersecurity-Skills
Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.
Categories
Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and…. Building Automated Malware Submission Pipeline is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration.
Building Automated Malware Submission Pipeline fits situations like: SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage; tasks that involve Security operations; tasks that involve Reverse engineering and malware.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a claude-code`. Or copy the skill folder (skills/building-automated-malware-submission-pipeline in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/building-automated-malware-submission-pipeline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline -a codex`. Or copy the skill folder (skills/building-automated-malware-submission-pipeline in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/building-automated-malware-submission-pipeline in your project. Codex loads it when a task matches its description.
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 building-automated-malware-submission-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-automated-malware-submission-pipeline, .gemini/skills/building-automated-malware-submission-pipeline, .github/skills/building-automated-malware-submission-pipeline and .opencode/skills/building-automated-malware-submission-pipeline in your project.
Going by SKILL.md and its folder, Building Automated Malware Submission Pipeline needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 2 domains. In commands or code: mb-api.abuse.ch and jbxcloud.joesecurity.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
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.
Building Automated Malware Submission Pipeline 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.
About 4.7k tokens (SKILL.md is roughly 19k 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 494 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Building Automated Malware Submission Pipeline: Create Sigma Rule (TracecatHQ/tracecat, 3.8k stars), YARA-X Rule Authoring (trailofbits/skills, 7.5k stars), Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars) and vphone600 Kernel Symbol Analysis (Lakr233/vphone-cli, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.