Deepsec Documentation Guide
vercel-labs/deepsec
Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.
Agent skill
Acquire and analyze mobile device data using Cellebrite UFED Touch/4PC, UFED Physical Analyzer, and open-source alternatives (ALEAPP, iLEAPP, MEAT, libimobiledevice) to extract communications, call…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-mobile-device-forensics-with-cellebrite -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-mobile-device-forensics-with-cellebrite --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/performing-mobile-device-forensics-with-cellebrite .claude/skills/performing-mobile-device-forensics-with-cellebrite && 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 "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .claude/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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/performing-mobile-device-forensics-with-cellebriteType 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 performing-mobile-device-forensics-with-cellebrite -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-mobile-device-forensics-with-cellebrite --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/performing-mobile-device-forensics-with-cellebrite .agents/skills/performing-mobile-device-forensics-with-cellebrite && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .agents/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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 performing-mobile-device-forensics-with-cellebrite -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-mobile-device-forensics-with-cellebrite --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/performing-mobile-device-forensics-with-cellebrite .cursor/skills/performing-mobile-device-forensics-with-cellebrite && 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 "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .cursor/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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/performing-mobile-device-forensics-with-cellebrite--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 performing-mobile-device-forensics-with-cellebrite -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-mobile-device-forensics-with-cellebrite --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/performing-mobile-device-forensics-with-cellebrite .gemini/skills/performing-mobile-device-forensics-with-cellebrite && 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 "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .gemini/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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 performing-mobile-device-forensics-with-cellebriteInstalls 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 performing-mobile-device-forensics-with-cellebrite -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/performing-mobile-device-forensics-with-cellebrite .github/skills/performing-mobile-device-forensics-with-cellebrite && 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 "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .github/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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 performing-mobile-device-forensics-with-cellebrite -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 performing-mobile-device-forensics-with-cellebrite --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/performing-mobile-device-forensics-with-cellebrite .opencode/skills/performing-mobile-device-forensics-with-cellebrite && 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 "performing-mobile-device-forensics-with-cellebrite" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-mobile-device-forensics-with-cellebrite into .opencode/skills/performing-mobile-device-forensics-with-cellebrite/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-mobile-device-forensics-with-cellebrite", 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.
performing-mobile-device-forensics-with-cellebriteAcquire and analyze mobile device data using Cellebrite UFED Touch/4PC, UFED Physical Analyzer, and open-source alternatives (ALEAPP, iLEAPP, MEAT, libimobiledevice) to extract communications, call…
Performing Mobile Device Forensics With Cellebrite is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Acquire and analyze mobile device data using Cellebrite UFED Touch/4PC, UFED Physical Analyzer, and open-source alternatives (ALEAPP, iLEAPP, MEAT, libimobiledevice) to extract communications, call logs, location data, and application artifacts. Use when extracting or recovering deleted evidence from smartphones or tablets during criminal, corporate, or employee-misuse investigations.
Its SKILL.md is about 3.1k 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. 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.
5 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.
Shell commands in SKILL.md call:
adbpython3pipcursorapt-getFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From 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.
Performing Mobile Device Forensics With Cellebrite loads about 3.1k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 443 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 noted patterns worth knowing about, such as sudo or a known installer.
sudo apt-get install libimobiledevice-utils # iOS acquisition on LinuxAutomated 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). 443 words, ~3,132 tokens.
.claude/skills/performing-mobile-device-forensics-with-cellebrite/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.# CRITICAL: Immediately place device in airplane mode or Faraday bag
# This prevents remote wipe commands and additional data changes
# Document device state before acquisition
# Record: make, model, IMEI, serial number, OS version, screen lock status
# Photograph the device from all angles
# For Android - Enable USB debugging if accessible
# Settings > Developer Options > USB Debugging > Enable
# For iOS - Trust the forensic workstation
# When prompted on device, tap "Trust This Computer"
# If device is locked, document lock type (PIN, pattern, biometric)
# Cellebrite UFED can bypass certain lock types depending on device model
# Install open-source tools as alternatives
pip install aleapp # Android Logs Events And Protobuf Parser
pip install ileapp # iOS Logs Events And Properties Parser
sudo apt-get install libimobiledevice-utils # iOS acquisition on Linux# === Cellebrite UFED Acquisition ===
# 1. Launch UFED 4PC or connect UFED Touch
# 2. Select Device > Identify device model automatically
# 3. Choose extraction type:
# - Logical: App data, contacts, messages, call logs (fastest, least data)
# - File System: Full file system access including databases
# - Physical: Bit-for-bit image including deleted data (most complete)
# - Advanced (Checkm8/GrayKey): For locked iOS devices (specific models)
# 4. Select output format and destination
# 5. Begin extraction
# === Open-source iOS acquisition with libimobiledevice ===
# List connected iOS devices
idevice_id -l
# Get device information
ideviceinfo -u <UDID>
# Create iOS backup (logical acquisition)
idevicebackup2 backup --full /cases/case-2024-001/mobile/ios_backup/
# For encrypted backups (contains more data including passwords)
idevicebackup2 backup --full --password /cases/case-2024-001/mobile/ios_backup/
# === Android acquisition with ADB ===
# List connected devices
adb devices
# Full backup (requires screen unlock)
adb backup -apk -shared -all -f /cases/case-2024-001/mobile/android_backup.ab
# Extract specific app data
adb shell pm list packages | grep -i "whatsapp\|telegram\|signal"
adb pull /data/data/com.whatsapp/ /cases/case-2024-001/mobile/whatsapp/
# For rooted Android devices - full filesystem
adb shell "su -c 'dd if=/dev/block/mmcblk0 bs=4096'" | \
dd of=/cases/case-2024-001/mobile/android_physical.dd
# Hash the acquisition
sha256sum /cases/case-2024-001/mobile/*.dd > /cases/case-2024-001/mobile/acquisition_hashes.txt# === Android analysis with ALEAPP ===
# ALEAPP processes Android file system extractions
python3 -m aleapp \
-t fs \
-i /cases/case-2024-001/mobile/android_extraction/ \
-o /cases/case-2024-001/analysis/aleapp_report/
# ALEAPP extracts and reports on:
# - Call logs, SMS/MMS messages
# - Chrome browser history and searches
# - WiFi connection history
# - Installed applications
# - Google account activity
# - Location data (Google Maps, Photos)
# - WhatsApp, Telegram, Signal messages
# - App usage statistics
# - Device settings and accounts
# === iOS analysis with iLEAPP ===
python3 -m ileapp \
-t tar \
-i /cases/case-2024-001/mobile/ios_backup.tar \
-o /cases/case-2024-001/analysis/ileapp_report/
# iLEAPP extracts and reports on:
# - iMessage and SMS messages
# - Safari browsing history
# - WiFi and Bluetooth connections
# - Health data and location history
# - App usage (KnowledgeC)
# - Photos with EXIF/GPS data
# - Notes, Calendar, Reminders
# - Keychain data (if decryptable)
# - Screen time data# Extract WhatsApp messages from Android
python3 << 'PYEOF'
import sqlite3
import os
# WhatsApp database location
db_path = "/cases/case-2024-001/mobile/android_extraction/data/data/com.whatsapp/databases/msgstore.db"
if os.path.exists(db_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Extract messages
cursor.execute("""
SELECT
key_remote_jid AS contact,
CASE WHEN key_from_me = 1 THEN 'SENT' ELSE 'RECEIVED' END AS direction,
data AS message_text,
datetime(timestamp/1000, 'unixepoch') AS msg_time,
media_mime_type,
media_size
FROM messages
WHERE data IS NOT NULL
ORDER BY timestamp DESC
LIMIT 1000
""")
with open('/cases/case-2024-001/analysis/whatsapp_messages.csv', 'w') as f:
f.write("contact,direction,message,timestamp,media_type,media_size\n")
for row in cursor.fetchall():
f.write(','.join(str(x) for x in row) + '\n')
conn.close()
print("WhatsApp messages extracted successfully")
PYEOF
# Extract iOS iMessage/SMS from sms.db
python3 << 'PYEOF'
import sqlite3
db_path = "/cases/case-2024-001/mobile/ios_extraction/HomeDomain/Library/SMS/sms.db"
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute("""
SELECT
h.id AS phone_number,
CASE WHEN m.is_from_me = 1 THEN 'SENT' ELSE 'RECEIVED' END AS direction,
m.text,
datetime(m.date/1000000000 + 978307200, 'unixepoch') AS msg_time,
m.service
FROM message m
JOIN handle h ON m.handle_id = h.ROWID
ORDER BY m.date DESC
""")
with open('/cases/case-2024-001/analysis/imessage_sms.csv', 'w') as f:
f.write("phone,direction,text,timestamp,service\n")
for row in cursor.fetchall():
f.write(','.join(str(x) for x in row) + '\n')
conn.close()
PYEOF# Extract GPS data from photos
pip install pillow
python3 << 'PYEOF'
from PIL import Image
from PIL.ExifTags import TAGS, GPSTAGS
import os, json
def get_gps(exif_data):
gps_info = {}
for key, val in exif_data.items():
decoded = GPSTAGS.get(key, key)
gps_info[decoded] = val
if 'GPSLatitude' in gps_info and 'GPSLongitude' in gps_info:
lat = gps_info['GPSLatitude']
lon = gps_info['GPSLongitude']
lat_val = lat[0] + lat[1]/60 + lat[2]/3600
lon_val = lon[0] + lon[1]/60 + lon[2]/3600
if gps_info.get('GPSLatitudeRef') == 'S': lat_val = -lat_val
if gps_info.get('GPSLongitudeRef') == 'W': lon_val = -lon_val
return lat_val, lon_val
return None
locations = []
photo_dir = "/cases/case-2024-001/mobile/ios_extraction/CameraRollDomain/Media/DCIM/"
for root, dirs, files in os.walk(photo_dir):
for fname in files:
if fname.lower().endswith(('.jpg', '.jpeg', '.heic')):
try:
img = Image.open(os.path.join(root, fname))
exif = img._getexif()
if exif and 34853 in exif:
coords = get_gps(exif[34853])
if coords:
locations.append({'file': fname, 'lat': coords[0], 'lon': coords[1]})
except Exception:
pass
with open('/cases/case-2024-001/analysis/photo_locations.json', 'w') as f:
json.dump(locations, f, indent=2)
print(f"Found {len(locations)} geotagged photos")
PYEOF
# Extract location history from Google Location History (Android)
# File: /data/data/com.google.android.gms/databases/lbs.db
# or exported Google Takeout location data| Concept | Description |
|---|---|
| Logical extraction | Extracts accessible user data through device APIs (contacts, messages, photos) |
| File system extraction | Full access to the device file system including app databases |
| Physical extraction | Bit-for-bit copy of device storage including deleted and unallocated data |
| UFED | Universal Forensic Extraction Device - Cellebrite's flagship acquisition platform |
| ADB | Android Debug Bridge for communicating with Android devices |
| KnowledgeC | iOS database tracking detailed app and device usage patterns |
| SQLite databases | Primary storage format for mobile app data (messages, contacts, history) |
| Checkm8 | Hardware-based iOS exploit enabling extraction on A5-A11 devices |
| Tool | Purpose |
|---|---|
| Cellebrite UFED | Commercial mobile device acquisition and analysis platform |
| Cellebrite Physical Analyzer | Deep analysis of mobile device extractions |
| ALEAPP | Open-source Android artifact parser and report generator |
| iLEAPP | Open-source iOS artifact parser and report generator |
| libimobiledevice | Open-source iOS communication library |
| Magnet AXIOM | Commercial mobile and computer forensics platform |
| MEAT | Mobile Evidence Acquisition Toolkit |
| ADB | Android Debug Bridge for device interaction and data extraction |
Scenario 1: Criminal Communications Investigation Acquire device with UFED physical extraction, decrypt messaging databases, extract WhatsApp/Telegram/Signal conversations, recover deleted messages from WAL files, build communication timeline, export for legal proceedings.
Scenario 2: Employee Data Theft via Personal Phone Perform logical extraction with employee consent, analyze corporate email and cloud storage app data, check for screenshots of confidential documents, review file transfer app activity, examine browser history for cloud uploads.
Scenario 3: Missing Person Location Tracking Extract location data from Google Location History, parse GPS data from photos, analyze WiFi connection history for last known locations, check fitness app data for movement patterns, examine messaging apps for last communications.
Scenario 4: Child Exploitation Investigation Physical extraction preserving all data including deleted content, hash all images against NCMEC/ICSE databases, extract communication records, recover deleted media from unallocated space, document chain of custody meticulously for prosecution.
Mobile Forensics Summary:
Device: Samsung Galaxy S23 Ultra (SM-S918B)
OS: Android 14, One UI 6.0
IMEI: 353456789012345
Extraction: Physical (via Cellebrite UFED)
Duration: 45 minutes
Extracted Data:
Contacts: 1,234
Call Logs: 5,678
SMS/MMS: 3,456
WhatsApp Msgs: 12,345 (234 deleted, recovered)
Telegram Msgs: 2,345
Photos/Videos: 4,567 (345 geotagged)
Browser History: 2,345 URLs
WiFi Networks: 67 saved connections
Installed Apps: 145
Key Findings:
- Deleted WhatsApp conversation with suspect recovered
- 23 geotagged photos at crime scene location
- Browser searches related to investigation subject
- Signal app used during incident timeframe (encrypted, partial recovery)
Reports:
ALEAPP Report: /analysis/aleapp_report/index.html
Messages Export: /analysis/whatsapp_messages.csv
Locations: /analysis/photo_locations.json© 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/performing-mobile-device-forensics-with-cellebrite of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Performing Mobile Device Forensics With Cellebrite 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 |
|---|---|---|---|---|---|---|
| Performing Mobile Device Forensics With Cellebrite this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| Deepsec Documentation Guidevercel-labs/deepsec | 8.1k | — | ~956 | Automated safety check: Pass | Apache-2.0 | |
| Skill Scannergetsentry/skills | 1k | 4 repos | ~2.5k | Automated safety check: Warn | Apache-2.0 | |
| Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit | 481 | 1 repos | ~3.3k | Automated safety check: Pass | None | |
| Security Alert Triageelastic/agent-skills | 592 | 1 repos | ~3.5k | Automated safety check: Notes | Apache-2.0 | |
| Shiro Attack CLISummerSec/ShiroAttack2 | 2.6k | — | ~945 | Automated safety check: Pass | MIT |
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getsentry/skills
Scan agent skills for security issues. An agent skill from getsentry/skills.
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elastic/agent-skills
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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.
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Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
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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
Acquire and analyze mobile device data using Cellebrite UFED Touch/4PC, UFED Physical Analyzer, and open-source alternatives (ALEAPP, iLEAPP, MEAT, libimobiledevice) to extract communications, call…. Performing Mobile Device Forensics With Cellebrite is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Acquire and analyze mobile device data using Cellebrite UFED Touch/4PC, UFED Physical Analyzer, and open-source alternatives (ALEAPP, iLEAPP, MEAT, libimobiledevice) to extract communications, call logs, location data, and application artifacts.
Performing Mobile Device Forensics With Cellebrite fits situations like: recovering deleted evidence from smartphones; tablets during criminal; employee-misuse investigations.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-mobile-device-forensics-with-cellebrite -a claude-code`. Or copy the skill folder (skills/performing-mobile-device-forensics-with-cellebrite in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-mobile-device-forensics-with-cellebrite in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-mobile-device-forensics-with-cellebrite -a codex`. Or copy the skill folder (skills/performing-mobile-device-forensics-with-cellebrite in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-mobile-device-forensics-with-cellebrite 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 performing-mobile-device-forensics-with-cellebrite -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-mobile-device-forensics-with-cellebrite, .gemini/skills/performing-mobile-device-forensics-with-cellebrite, .github/skills/performing-mobile-device-forensics-with-cellebrite and .opencode/skills/performing-mobile-device-forensics-with-cellebrite in your project.
Going by SKILL.md and its folder, Performing Mobile Device Forensics With Cellebrite needs Python for the scripts in its folder and the command-line tools its instructions call (adb, python3, pip, cursor and apt-get). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. 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.
Performing Mobile Device Forensics With Cellebrite 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 3.1k tokens (SKILL.md is roughly 13k 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 491 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Performing Mobile Device Forensics With Cellebrite: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 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.