Agent skill

Analyzing Disk Image With Autopsy

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

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…

Apache-2.0Auto-check: notesSecurity

Install Analyzing Disk Image With Autopsy

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-disk-image-with-autopsy -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-disk-image-with-autopsy --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/analyzing-disk-image-with-autopsy .claude/skills/analyzing-disk-image-with-autopsy && 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
analyzing-disk-image-with-autopsy
GitHub stars
34k
Token cost
~2.7k tokens
SKILL.md length
531 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

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…

  • Works in 6 steps: Install Autopsy and Configure Environment → Create a New Case and Add the Disk Image → Configure and Run Ingest Modules → …
  • Structured analysis of a forensic disk image
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls apt-get and wget; reaches github.com and nist.gov

What it does

Analyzing Disk Image With Autopsy is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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.

Its SKILL.md is about 2.7k 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 Digital forensics. 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

  • Structured analysis of a forensic disk image
  • Stakeholders need visual reports from evidence

Example prompts

  • “/analyzing-disk-image-with-autopsy”

Requirements

  • Python 3

Workflow steps

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

  1. Install Autopsy and Configure Environment
  2. Create a New Case and Add the Disk Image
  3. Configure and Run Ingest Modules
  4. Analyze File System and Recover Deleted Files
  5. Perform Keyword Searches and Tag Evidence
  6. Build Timeline and Generate Reports

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:

    • apt-get
    • wget

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • nist.gov
    • s3.amazonaws.com

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Analyzing Disk Image With Autopsy loads about 2.7k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 531 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.4k

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:50
    sudo apt-get install autopsy sleuthkit
  • NoteRuns commands with sudoSKILL.md:61
    sudo apt-get install sleuthkit

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). 531 words, ~2,690 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-disk-image-with-autopsy/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
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.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, autopsy, disk-analysis, sleuth-kit, file-recovery, artifact-analysis
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1005, T1074.001, T1070.004, T1083

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/

# Get detailed file metadata
istat -o 2048 /cases/case-2024-001/images/evidence.dd 14523
# Shows: creation, modification, access, MFT change timestamps, size, data runs
Step 5: Perform Keyword Searches and Tag Evidence
In Autopsy:
1. Keyword Search panel > "Ad Hoc Keyword Search"
2. Search terms: credit card patterns, SSN regex, email addresses
3. Example regex for credit cards: \b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14})\b
4. Example regex for SSN: \b\d{3}-\d{2}-\d{4}\b
5. Review results > Right-click items > "Add Tag"
6. Create tags: "Evidence-Critical", "Evidence-Supporting", "Requires-Review"
7. Add comments to tagged items documenting relevance
bash
# Using Sleuth Kit for CLI keyword search
srch_strings -a -o 2048 /cases/case-2024-001/images/evidence.dd | \
   grep -iE '(password|secret|confidential)' > /cases/case-2024-001/keyword_hits.txt

# Search for specific file signatures
sigfind -o 2048 /cases/case-2024-001/images/evidence.dd 25504446
# 25504446 = %PDF header signature
Step 6: Build Timeline and Generate Reports
In Autopsy:
1. Timeline viewer: Tools > Timeline
2. Select date range of interest (incident window)
3. Filter by event type: File Created, Modified, Accessed, Web Activity
4. Zoom into suspicious time periods
5. Export timeline events as CSV for external analysis

Generate Report:
1. Generate Report > HTML Report
2. Select tagged items and data sources to include
3. Configure report sections: file listings, keyword hits, timeline
4. Export to /cases/case-2024-001/reports/
bash
# Using Sleuth Kit mactime for CLI timeline
fls -r -m "/" -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/bodyfile.txt

# Generate timeline from bodyfile
mactime -b /cases/case-2024-001/bodyfile.txt -d > /cases/case-2024-001/timeline.csv

# Filter timeline to specific date range
mactime -b /cases/case-2024-001/bodyfile.txt \
   -d 2024-01-15..2024-01-20 > /cases/case-2024-001/incident_timeline.csv

Key Concepts

ConceptDescription
Ingest ModulesAutomated analysis plugins that process data sources upon import
MFT (Master File Table)NTFS metadata structure recording all file entries and attributes
File carvingRecovering files from unallocated space using file signatures
Hash filteringUsing NSRL or custom hash sets to exclude known-good or flag known-bad files
Timeline analysisChronological reconstruction of file system and user activity events
Deleted file recoveryRestoring files whose directory entries are removed but data remains
Keyword indexingFull-text search index built from all file content including slack space
Artifact extractionAutomated parsing of browser, email, registry, and OS-specific artifacts

Tools & Systems

ToolPurpose
AutopsyOpen-source GUI forensic platform for disk image analysis
The Sleuth Kit (TSK)Command-line forensic toolkit underlying Autopsy
flsList files and directories in a disk image including deleted entries
icatExtract file content by inode number from a disk image
mactimeGenerate timeline from TSK bodyfile format
mmlsDisplay partition layout of a disk image
NSRLNIST hash database for identifying known software files
sigfindSearch for file signatures at the sector level
Show full SKILL.md (184 more words)Show less

Common Scenarios

Scenario 1: Employee Data Theft Investigation Import the employee workstation image, run all ingest modules, search for company-confidential file names and keywords, examine USB connection artifacts in Recent Activity, check for cloud storage client artifacts, review deleted files for evidence of data staging, generate HTML report for legal team.

Scenario 2: Malware Infection Forensics Add the compromised system image, enable Extension Mismatch and Encryption Detection modules, examine the prefetch directory for execution evidence, search for known malware hashes, build timeline around the infection window, extract suspicious executables for further analysis in a sandbox.

Scenario 3: Child Exploitation Material (CSAM) Investigation Import image with PhotoDNA and Project VIC hash sets enabled, run Picture Analyzer module, hash all image files against known-bad databases, tag and categorize matches by severity, generate law enforcement report with chain of custody documentation.

Scenario 4: Intellectual Property Dispute Import multiple employee disk images as separate data sources in one case, perform keyword searches for proprietary terms and project names, compare file hashes between sources, build timeline showing file access and transfer patterns, export evidence for legal review.

Output Format

Autopsy Case Analysis Summary:
  Case:           CASE-2024-001-Workstation
  Image:          evidence.dd (500GB NTFS)
  Partitions:     2 (System Reserved + Primary)
  Total Files:    245,832
  Deleted Files:  12,456 (recoverable: 8,234)

  Ingest Results:
    Hash Matches (Known Bad):  3 files
    Extension Mismatches:      17 files
    Keyword Hits:              234 across 45 files
    Encrypted Files:           5 containers detected
    EXIF Data Extracted:       1,245 images with metadata

  Tagged Evidence:
    Critical:     12 items
    Supporting:   34 items
    Review:       67 items

  Timeline Events:  1,234,567 entries (filtered to incident window: 892)
  Report:          /cases/case-2024-001/reports/autopsy_report.html

© 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/analyzing-disk-image-with-autopsy 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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TShark Traffic AnalysisAgentSecOps/SecOpsAgentKit2201 repos~4.8kAutomated safety check: NotesCustom licence
Runtime Memory Sample Acquisitiondslsdzc/rev-skills135—~2kAutomated safety check: PassApache-2.0

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Categories

Questions about Analyzing Disk Image With Autopsy

What does Analyzing Disk Image With Autopsy do?

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…. Analyzing Disk Image With Autopsy is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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.

When should I use Analyzing Disk Image With Autopsy?

Analyzing Disk Image With Autopsy fits situations like: structured analysis of a forensic disk image; stakeholders need visual reports from evidence.

How do I install Analyzing Disk Image With Autopsy in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-disk-image-with-autopsy -a claude-code`. Or copy the skill folder (skills/analyzing-disk-image-with-autopsy in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-disk-image-with-autopsy in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Disk Image With Autopsy in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-disk-image-with-autopsy -a codex`. Or copy the skill folder (skills/analyzing-disk-image-with-autopsy in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-disk-image-with-autopsy in your project. Codex loads it when a task matches its description.

Can I use Analyzing Disk Image With Autopsy 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 analyzing-disk-image-with-autopsy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-disk-image-with-autopsy, .gemini/skills/analyzing-disk-image-with-autopsy, .github/skills/analyzing-disk-image-with-autopsy and .opencode/skills/analyzing-disk-image-with-autopsy in your project.

What does Analyzing Disk Image With Autopsy need to run?

Going by SKILL.md and its folder, Analyzing Disk Image With Autopsy needs Python for the scripts in its folder and the command-line tools its instructions call (apt-get and wget). Our summary lists: Python 3.

Does Analyzing Disk Image With Autopsy access the network?

SKILL.md names 3 domains. In commands or code: github.com, nist.gov and s3.amazonaws.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Analyzing Disk Image With Autopsy 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 Analyzing Disk Image With Autopsy use?

Analyzing Disk Image With Autopsy 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 Analyzing Disk Image With Autopsy use?

About 2.7k tokens (SKILL.md is roughly 11k 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 727 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Disk Image With Autopsy?

Skills that share tags, products or a category with Analyzing Disk Image With Autopsy: Oss Forensics (Tommy-yw/RunbookHermes, 546 stars), Ctf Malware (ljagiello/ctf-skills, 3.4k stars), Dfir (transilienceai/communitytools, 563 stars) and TShark Traffic Analysis (AgentSecOps/SecOpsAgentKit, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Disk Image With Autopsy?

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.