analyzing-disk-image-with-autopsy
Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and…
Skill by mukul975
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/analyzing-disk-image-with-autopsy
What's inside
- Skill name
analyzing-disk-image-with-autopsy- Description
- Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.
- License
- Apache-2.0
- Instructions
- 243 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
- Instructions call the network
Agent Skills spec issues
- body: contains an absolute path from the author's machine
Files (4)
- LICENSE
- SKILL.md
- api-reference.md · references/
- agent.py · scripts/
SKILL.md (first lines)
# Analyzing Disk Image with Autopsy ## When to Use - When you have a forensic disk image and need structured analysis of its contents - During investigations requiring file recovery, keyword searching, and timeline analysis - When non-technical stakeholders need visual reports from forensic evidence - For examining file system metadata, deleted files, and embedded artifacts - When building a comprehensive case from multiple disk images ## Prerequisites - Autopsy 4.x installed (Windows) or Autopsy 4.x with The Sleuth Kit (Linux) - Forensic disk image in raw (dd), E01 (EnCase), or AFF format - Minimum 8GB RAM (16GB recommended for large images) - Java Runtime Environment (JRE) 8+ for Autopsy - Sufficient disk space for the Autopsy case database (2-3x image size) - Hash databases (NSRL, known-bad hashes) for file identification ## Workflow ### Step 1: Install Autopsy and Configure Environment ```bash # On Linux, install Sleuth Kit and Autopsy sudo apt-get install autopsy sleuthkit # Download Autopsy 4.x (GUI version) from official source wget https://github.com/sleuthkit/autopsy/releases/download/autopsy-4.21.0/autopsy-4.21.0.zip unzip autopsy-4.21.0.zip -d /opt/autopsy # On Windows, run the MSI installer from sleuthkit.org # Launch Autopsy /opt/autopsy/bin/autopsy --nosplash # For Sleuth Kit command-line analysis alongside Autopsy sudo apt-get install sleuthkit ``` ### Step 2: Create a New Case and Add the Disk Image ``` 1. Launch Autopsy > "New Case" 2. Enter Case Name: "CASE-2024-001-Workstation" 3. Set Base Directory: /cases/case-2024-001/autopsy/ 4. Enter Case Number, Examiner Name 5. Click "Add Data Source" 6. Select "Disk Image or VM File" 7. Browse to: /cases/case-2024-001/images/evidence.dd 8. Select Time Zone of the original system 9. Configure Ingest Modules (see Step 3) ``` ```bash # Alternatively, use Sleuth Kit CLI to verify the image first img_stat /cases/case-2024-001/images/evidence.dd # List partitions in the image mmls /cases/case-2024-001/images/evidence.dd # Output example: # DOS Partition Table # Offset Sector: 0 # Units are in 512-byte sectors # Slot Start End Length Description # 00: ----- 0000000000 0000002047 0000002048 Primary Table (#0) # 01: 00:00 0000002048 0001026047 0001024000 NTFS (0x07) # 02: 00:01 0001026048 0976771071 0975745024 NTFS (0x07) # List files in a partition (offset 2048 sectors) fls -o 2048 /cases/case-2024-001/images/evidence.dd ``` ### Step 3: Configure and Run Ingest Modules ``` Enable the following Autopsy Ingest Modules: - Recent Activity: Extracts browser history, downloads, cookies, bookmarks - Hash Lookup: Compares files against NSRL and known-bad hash sets - File Type Identification: Identifies files by signature, not extension - Keyword Search: Indexes content for full-text searching - Email Parser: Extracts emails from PST, MBOX, EML files - Extension Mismatch Detector: Finds files with wrong extensions - Exif Parser: Extracts metadata from images (GPS, camera, timestamps) - Encryption Detection: Identifies encrypted files and containers - Interesting Files Identifier: Flags files matching custom rule sets - Embedded File Extractor: Extracts files from ZIP, Office docs, PDFs - Picture Analyzer: Categorizes images using PhotoDNA or hash matching - Data Source Integrity: Verifies image hash during ingest ``` ```bash # Configure NSRL hash set for known-good filtering # Download NSRL from https://www.nist.gov/itl/ssd/software-quality-group/national-software-reference-library-nsrl wget https://s3.amazonaws.com/rds.nsrl.nist.gov/RDS/current/rds_modernm.zip unzip rds_modernm.zip -d /opt/autopsy/hashsets/ # Import into Autopsy: # Tools > Options > Hash Sets > Import > Select NSRLFile.txt # Mark as "Known" (to filter out known-good files) ``` ### Step 4: Analyze File System and Recover Deleted Files ```bash # In Autopsy GUI: Navigate tree structure # - Data Sources > evidence.dd > vol2 (NTFS) # - Examine directory tree, note deleted files (marked with X) # Using Sleuth Kit CLI for targeted recovery # List deleted files fls -rd -o 2048 /cases/case-2024-001/images/evidence.dd # Recover a specific deleted file by inode icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523 > /cases/case-2024-001/recovered/deleted_document.docx # Extract all files from a directory tsk_recover -o 2048 -d /Users/suspect/Documents \ /cases/case-2024-001/images/evidence.dd \ /cases/case-2024-001/recovered/documents/
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Signals
- Stars
- 34,941
- Forks
- 4,256
- Open issues
- 24
- Last commit (this folder)
- Aug 2, 2026 (2 months ago)
- Last release
- Jun 22, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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analyzing-disk-image-with-autopsy skill. Agent Mart. https://agentmart.dev/skills/mukul975-analyzing-disk-image-with-autopsy (last updated 2026-10-24)