Agent skill

Performing Mobile Device Forensics With Cellebrite

by mukul975 in 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…

Apache-2.0Auto-check: notesSecurity

Install Performing Mobile Device Forensics With Cellebrite

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-mobile-device-forensics-with-cellebrite -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-mobile-device-forensics-with-cellebrite --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-mobile-device-forensics-with-cellebrite .claude/skills/performing-mobile-device-forensics-with-cellebrite && 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-mobile-device-forensics-with-cellebrite
GitHub stars
34k
Token cost
~3.1k tokens
SKILL.md length
443 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 5 steps: Prepare the Device and Isolation → Perform Device Acquisition → Analyze with ALEAPP (Android) or iLEAPP… → …
  • Recovering deleted evidence from smartphones
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls adb, python3 and pip

What it does

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.

When your agent uses it

  • Recovering deleted evidence from smartphones
  • Tablets during criminal
  • Employee-misuse investigations

Example prompts

  • “/performing-mobile-device-forensics-with-cellebrite”

Requirements

  • Python 3

Workflow steps

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

  1. Prepare the Device and Isolation
  2. Perform Device Acquisition
  3. Analyze with ALEAPP (Android) or iLEAPP (iOS)
  4. Extract Communications and Messaging Data
  5. Extract Location Data and Generate Report

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 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • adb
    • python3
    • pip
    • cursor
    • apt-get

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

  • Network

    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.

  • 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 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.

Always · name and description, kept in context so the agent knows when to use it
~110
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.6k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:69
    sudo apt-get install libimobiledevice-utils  # iOS acquisition on Linux

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). 443 words, ~3,132 tokens.

Download SKILL.mdSave it as .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.
name
performing-mobile-device-forensics-with-cellebrite
description
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.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, mobile-forensics, cellebrite, smartphone-analysis, ios-forensics, android-forensics
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1005, T1074, T1119, T1070, T1059

Performing Mobile Device Forensics with Cellebrite

When to Use

  • When extracting evidence from smartphones or tablets during an investigation
  • For recovering deleted messages, call logs, and location data from mobile devices
  • During investigations involving communications via messaging apps
  • When analyzing mobile application data for evidence of criminal activity
  • For corporate investigations involving employee mobile device misuse

Prerequisites

  • Cellebrite UFED Touch/4PC or UFED Physical Analyzer (licensed)
  • Alternative open-source tools: ALEAPP, iLEAPP, MEAT, libimobiledevice
  • Appropriate cables and adapters for target device
  • Faraday bag to isolate the device from network signals
  • Legal authorization (warrant, consent, or corporate policy)
  • Knowledge of iOS and Android file system structures

Workflow

Step 1: Prepare the Device and Isolation
bash
# 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
Step 2: Perform Device Acquisition
bash
# === 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
Step 3: Analyze with ALEAPP (Android) or iLEAPP (iOS)
bash
# === 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
Step 4: Extract Communications and Messaging Data
bash
# 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
Step 5: Extract Location Data and Generate Report
bash
# 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

Key Concepts

ConceptDescription
Logical extractionExtracts accessible user data through device APIs (contacts, messages, photos)
File system extractionFull access to the device file system including app databases
Physical extractionBit-for-bit copy of device storage including deleted and unallocated data
UFEDUniversal Forensic Extraction Device - Cellebrite's flagship acquisition platform
ADBAndroid Debug Bridge for communicating with Android devices
KnowledgeCiOS database tracking detailed app and device usage patterns
SQLite databasesPrimary storage format for mobile app data (messages, contacts, history)
Checkm8Hardware-based iOS exploit enabling extraction on A5-A11 devices

Tools & Systems

ToolPurpose
Cellebrite UFEDCommercial mobile device acquisition and analysis platform
Cellebrite Physical AnalyzerDeep analysis of mobile device extractions
ALEAPPOpen-source Android artifact parser and report generator
iLEAPPOpen-source iOS artifact parser and report generator
libimobiledeviceOpen-source iOS communication library
Magnet AXIOMCommercial mobile and computer forensics platform
MEATMobile Evidence Acquisition Toolkit
ADBAndroid Debug Bridge for device interaction and data extraction
Show full SKILL.md (147 more words)Show less

Common Scenarios

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.

Output Format

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

Files

SKILL.md and 3 other files (scripts, references) in skills/performing-mobile-device-forensics-with-cellebrite of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Performing Mobile Device Forensics With Cellebrite

What does Performing Mobile Device Forensics With Cellebrite do?

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.

When should I use Performing Mobile Device Forensics With Cellebrite?

Performing Mobile Device Forensics With Cellebrite fits situations like: recovering deleted evidence from smartphones; tablets during criminal; employee-misuse investigations.

How do I install Performing Mobile Device Forensics With Cellebrite in Claude Code?

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.

How do I install Performing Mobile Device Forensics With Cellebrite in Codex?

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.

Can I use Performing Mobile Device Forensics With Cellebrite 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-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.

What does Performing Mobile Device Forensics With Cellebrite need to run?

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.

Does Performing Mobile Device Forensics With Cellebrite access the network?

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.

Is Performing Mobile Device Forensics With Cellebrite safe to install?

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.

What licence does Performing Mobile Device Forensics With Cellebrite use?

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.

How many tokens does Performing Mobile Device Forensics With Cellebrite use?

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.

What are the alternatives to Performing Mobile Device Forensics With Cellebrite?

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.

Who maintains Performing Mobile Device Forensics With Cellebrite?

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.