Oss Forensics
Tommy-yw/RunbookHermes
Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.
Agent skill
Recovers files from disk images and unallocated space using Foremost's header-footer signature carving, extracting evidence independent of the file system's state.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-file-carving-with-foremost -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-file-carving-with-foremost --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-file-carving-with-foremost .claude/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .claude/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremostType 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-file-carving-with-foremost -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-file-carving-with-foremost --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-file-carving-with-foremost .agents/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .agents/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremost -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-file-carving-with-foremost --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-file-carving-with-foremost .cursor/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .cursor/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremost--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-file-carving-with-foremost -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-file-carving-with-foremost --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-file-carving-with-foremost .gemini/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .gemini/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremostInstalls 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-file-carving-with-foremost -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-file-carving-with-foremost .github/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .github/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremost -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-file-carving-with-foremost --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-file-carving-with-foremost .opencode/skills/performing-file-carving-with-foremost && 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-file-carving-with-foremost" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/performing-file-carving-with-foremost into .opencode/skills/performing-file-carving-with-foremost/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "performing-file-carving-with-foremost", 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-file-carving-with-foremostRecovers files from disk images and unallocated space using Foremost's header-footer signature carving, extracting evidence independent of the file system's state.
Performing File Carving With Foremost is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Recovers files from disk images and unallocated space using Foremost's header-footer signature carving, extracting evidence independent of the file system's state. Use during digital forensics investigations to carve deleted or fragmented files, such as documents, images, and archives, from raw disk images or unallocated space.
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, 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.
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:
apt-getpython3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
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 File Carving With Foremost loads about 3.1k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 480 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 foremostsudo apt-get install scalpelAutomated 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). 480 words, ~3,140 tokens.
.claude/skills/performing-file-carving-with-foremost/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.# Install Foremost
sudo apt-get install foremost
# Verify installation
foremost -V
# Review default configuration
cat /etc/foremost.conf
# The default foremost.conf supports:
# jpg, gif, png, bmp - Image formats
# avi, exe, mpg, wav - Media and executables
# riff, wmv, mov, pdf - Documents and video
# ole (doc/xls/ppt), zip, rar - Office and archives
# htm, cpp, java - Text/code files
# Create custom configuration for additional file types
cp /etc/foremost.conf /cases/case-2024-001/custom_foremost.conf
# Add custom file signatures
cat << 'EOF' >> /cases/case-2024-001/custom_foremost.conf
# Custom additions for investigation
# Format: extension case_sensitive max_size header footer
docx y 10000000 \x50\x4b\x03\x04 \x50\x4b\x05\x06
xlsx y 10000000 \x50\x4b\x03\x04 \x50\x4b\x05\x06
pptx y 10000000 \x50\x4b\x03\x04 \x50\x4b\x05\x06
sqlite y 50000000 \x53\x51\x4c\x69\x74\x65\x20\x66\x6f\x72\x6d\x61\x74
pst y 500000000 \x21\x42\x44\x4e
eml y 1000000 \x46\x72\x6f\x6d\x3a \x0d\x0a\x0d\x0a
evtx y 50000000 \x45\x6c\x66\x46\x69\x6c\x65
EOF# Basic carving of all supported file types
foremost -t all \
-i /cases/case-2024-001/images/evidence.dd \
-o /cases/case-2024-001/carved/foremost_all/
# Carve only specific file types
foremost -t jpg,png,pdf,doc,xls,zip \
-i /cases/case-2024-001/images/evidence.dd \
-o /cases/case-2024-001/carved/foremost_targeted/
# Use custom configuration
foremost -c /cases/case-2024-001/custom_foremost.conf \
-i /cases/case-2024-001/images/evidence.dd \
-o /cases/case-2024-001/carved/foremost_custom/
# Carve from a specific partition offset
# First, find partitions
mmls /cases/case-2024-001/images/evidence.dd
# Then carve from unallocated space only
# Extract unallocated space with blkls
blkls -o 2048 /cases/case-2024-001/images/evidence.dd \
> /cases/case-2024-001/unallocated.dd
foremost -t all \
-i /cases/case-2024-001/unallocated.dd \
-o /cases/case-2024-001/carved/foremost_unalloc/
# Verbose mode for detailed progress
foremost -v -t all \
-i /cases/case-2024-001/images/evidence.dd \
-o /cases/case-2024-001/carved/foremost_verbose/ 2>&1 | \
tee /cases/case-2024-001/carved/foremost_log.txt
# Indirect mode (process standard input)
dd if=/cases/case-2024-001/images/evidence.dd bs=512 skip=2048 | \
foremost -t jpg,pdf -o /cases/case-2024-001/carved/foremost_pipe/# Install Scalpel (faster alternative based on Foremost)
sudo apt-get install scalpel
# Edit Scalpel configuration (uncomment desired file types)
cp /etc/scalpel/scalpel.conf /cases/case-2024-001/scalpel.conf
# Uncomment lines for target file types in the config
# Run Scalpel
scalpel -c /cases/case-2024-001/scalpel.conf \
-o /cases/case-2024-001/carved/scalpel/ \
/cases/case-2024-001/images/evidence.dd
# Scalpel with file size limits
# Edit scalpel.conf to set appropriate max sizes:
# jpg y 5000000 \xff\xd8\xff \xff\xd9
# pdf y 20000000 %PDF %%EOF# Review Foremost audit report
cat /cases/case-2024-001/carved/foremost_all/audit.txt
# The audit.txt contains:
# - Number of files found per type
# - Start and end offsets
# - File sizes
# Validate carved files
python3 << 'PYEOF'
import os
import subprocess
from collections import defaultdict
carved_dir = '/cases/case-2024-001/carved/foremost_all/'
stats = defaultdict(lambda: {'total': 0, 'valid': 0, 'invalid': 0, 'size': 0})
for subdir in os.listdir(carved_dir):
subdir_path = os.path.join(carved_dir, subdir)
if not os.path.isdir(subdir_path) or subdir == 'audit.txt':
continue
for filename in os.listdir(subdir_path):
filepath = os.path.join(subdir_path, filename)
if not os.path.isfile(filepath):
continue
ext = subdir
filesize = os.path.getsize(filepath)
stats[ext]['total'] += 1
stats[ext]['size'] += filesize
# Validate file using 'file' command
result = subprocess.run(['file', '--brief', filepath], capture_output=True, text=True)
file_type = result.stdout.strip()
if 'data' in file_type.lower() or 'empty' in file_type.lower():
stats[ext]['invalid'] += 1
else:
stats[ext]['valid'] += 1
print("=== CARVED FILE VALIDATION ===\n")
print(f"{'Type':<10} {'Total':<8} {'Valid':<8} {'Invalid':<10} {'Total Size':<15}")
print("-" * 55)
for ext in sorted(stats.keys()):
s = stats[ext]
size_mb = s['size'] / (1024*1024)
print(f"{ext:<10} {s['total']:<8} {s['valid']:<8} {s['invalid']:<10} {size_mb:>10.1f} MB")
# Remove zero-byte files
for subdir in os.listdir(carved_dir):
subdir_path = os.path.join(carved_dir, subdir)
if os.path.isdir(subdir_path):
for filename in os.listdir(subdir_path):
filepath = os.path.join(subdir_path, filename)
if os.path.isfile(filepath) and os.path.getsize(filepath) == 0:
os.remove(filepath)
PYEOF
# Hash all valid carved files
find /cases/case-2024-001/carved/foremost_all/ -type f ! -name "audit.txt" \
-exec sha256sum {} \; > /cases/case-2024-001/carved/carved_file_hashes.txt
# Check against known-bad hash database
# Check against NSRL known-good database to filter# Extract metadata from carved images (EXIF data including GPS)
exiftool -r -csv /cases/case-2024-001/carved/foremost_all/jpg/ \
> /cases/case-2024-001/analysis/carved_image_metadata.csv
# Search carved documents for keywords
find /cases/case-2024-001/carved/foremost_all/pdf/ -name "*.pdf" -exec pdftotext {} - \; 2>/dev/null | \
grep -iE '(confidential|secret|password|account|ssn|credit.card)' \
> /cases/case-2024-001/analysis/keyword_hits_pdf.txt
# Generate thumbnails for image review
mkdir -p /cases/case-2024-001/carved/thumbnails/
find /cases/case-2024-001/carved/foremost_all/jpg/ -name "*.jpg" -exec \
convert {} -thumbnail 200x200 /cases/case-2024-001/carved/thumbnails/{} \; 2>/dev/null
# Create evidence catalog
python3 << 'PYEOF'
import os, hashlib, csv, subprocess
catalog = []
carved_dir = '/cases/case-2024-001/carved/foremost_all/'
for subdir in sorted(os.listdir(carved_dir)):
subdir_path = os.path.join(carved_dir, subdir)
if not os.path.isdir(subdir_path):
continue
for filename in sorted(os.listdir(subdir_path)):
filepath = os.path.join(subdir_path, filename)
if not os.path.isfile(filepath):
continue
size = os.path.getsize(filepath)
sha256 = hashlib.sha256(open(filepath, 'rb').read()).hexdigest()
file_type = subprocess.run(['file', '--brief', filepath], capture_output=True, text=True).stdout.strip()
catalog.append({
'filename': filename,
'type': subdir,
'size': size,
'sha256': sha256,
'file_description': file_type[:100]
})
with open('/cases/case-2024-001/analysis/carved_file_catalog.csv', 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=['filename', 'type', 'size', 'sha256', 'file_description'])
writer.writeheader()
writer.writerows(catalog)
print(f"Catalog created with {len(catalog)} files")
PYEOF| Concept | Description |
|---|---|
| File carving | Recovering files by searching for known header/footer byte sequences in raw data |
| File signature | Unique byte pattern at the start (header) or end (footer) identifying a file type |
| Unallocated space | Disk sectors not assigned to any file; primary target for carving |
| Fragmentation | When file data is stored in non-contiguous sectors, complicating carving |
| Header-footer carving | Extracting data between known file start and end signatures |
| False positives | Carved data matching file signatures but containing corrupt or unrelated content |
| Slack space | Unused bytes at the end of a file's last allocated cluster |
| Sector alignment | Files typically start at sector boundaries, improving carving accuracy |
| Tool | Purpose |
|---|---|
| Foremost | Original header-footer file carving tool developed for US Air Force OSI |
| Scalpel | High-performance file carver with configurable signatures |
| PhotoRec | Signature-based file recovery supporting 300+ formats |
| bulk_extractor | Extracts features (emails, URLs, credit cards) from raw data |
| blkls | Sleuth Kit tool extracting unallocated space from disk images |
| mmls | Partition table display for identifying carving targets |
| ExifTool | Metadata extraction from carved image and document files |
| hashdeep | Recursive hash computation for carved file cataloging |
Scenario 1: Recovering Deleted Evidence Documents Run Foremost targeting doc, pdf, xlsx formats against the unallocated space extracted with blkls, validate carved documents, search content for case-relevant keywords, catalog and hash all recoverable documents, present as evidence.
Scenario 2: Image Recovery from Formatted Media Carve JPEG, PNG, GIF, BMP from a formatted USB drive image, extract EXIF metadata including GPS coordinates and camera information, generate thumbnails for rapid visual review, identify evidence-relevant images, document recovery chain.
Scenario 3: Email Recovery from Damaged PST Use custom foremost.conf with PST and EML signatures, carve email artifacts from damaged Outlook data file, attempt to open carved PST fragments in a viewer, extract individual EML messages, search for relevant communications.
Scenario 4: Database Recovery for Financial Investigation Configure Foremost to carve SQLite databases from unallocated space, recover application databases that were deleted, query recovered databases for financial records, cross-reference with known transaction data, document findings for prosecution.
File Carving Summary:
Tool: Foremost 1.5.7
Source: evidence.dd (500 GB)
Target: Unallocated space (234 GB)
Duration: 1h 45m
Files Carved:
jpg: 2,345 files (1.8 GB) - Valid: 2,100 / Invalid: 245
png: 234 files (456 MB) - Valid: 210 / Invalid: 24
pdf: 156 files (890 MB) - Valid: 134 / Invalid: 22
doc: 89 files (234 MB) - Valid: 67 / Invalid: 22
xls: 45 files (123 MB) - Valid: 38 / Invalid: 7
zip: 67 files (567 MB) - Valid: 52 / Invalid: 15
exe: 34 files (234 MB) - Valid: 30 / Invalid: 4
sqlite: 12 files (89 MB) - Valid: 10 / Invalid: 2
Total Files: 2,982 (3.4 GB recovered)
Evidence-Relevant: 45 files flagged for review
Audit Log: /cases/case-2024-001/carved/foremost_all/audit.txt
File Catalog: /cases/case-2024-001/analysis/carved_file_catalog.csv© 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-file-carving-with-foremost of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Performing File Carving With Foremost 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 File Carving With Foremost this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| Oss ForensicsTommy-yw/RunbookHermes | 546 | 3 repos | ~5k | Automated safety check: Pass | MIT | |
| Ctf Malwareljagiello/ctf-skills | 3.4k | — | ~2.1k | Automated safety check: Notes | MIT | |
| Dfirtransilienceai/communitytools | 563 | — | ~1.5k | Automated safety check: Pass | MIT | |
| TShark Traffic AnalysisAgentSecOps/SecOpsAgentKit | 220 | 1 repos | ~4.8k | Automated safety check: Notes | Custom licence | |
| Runtime Memory Sample Acquisitiondslsdzc/rev-skills | 135 | — | ~2k | Automated safety check: Pass | Apache-2.0 |
Tommy-yw/RunbookHermes
Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.
ljagiello/ctf-skills
Provides malware analysis and network traffic techniques for CTF challenges.
transilienceai/communitytools
Digital forensics and incident response - Windows event log analysis, PCAP forensics, filesystem artifact analysis, AD attack detection, and timeline correlation.
AgentSecOps/SecOpsAgentKit
Guides authorized packet capture and analysis with TShark, Wireshark's command-line tool, for security investigations, malware detection and forensic examination of network traffic.
dslsdzc/rev-skills
Captures an analyzable sample from a live system when the target leaves no file on disk, by finding abnormal executable memory and the execution context that reached it.
zhaoxuya520/reverse-skill
A skill your agent uses for authorized digital forensics including memory dumps, disk timelines, PCAP investigation, artifact triage, and IR evidence preservation.
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
Recovers files from disk images and unallocated space using Foremost's header-footer signature carving, extracting evidence independent of the file system's state. Performing File Carving With Foremost is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Recovers files from disk images and unallocated space using Foremost's header-footer signature carving, extracting evidence independent of the file system's state.
Performing File Carving With Foremost fits situations like: tasks that involve Digital forensics.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-file-carving-with-foremost -a claude-code`. Or copy the skill folder (skills/performing-file-carving-with-foremost in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-file-carving-with-foremost in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-file-carving-with-foremost -a codex`. Or copy the skill folder (skills/performing-file-carving-with-foremost in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-file-carving-with-foremost 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-file-carving-with-foremost -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-file-carving-with-foremost, .gemini/skills/performing-file-carving-with-foremost, .github/skills/performing-file-carving-with-foremost and .opencode/skills/performing-file-carving-with-foremost in your project.
Going by SKILL.md and its folder, Performing File Carving With Foremost needs Python for the scripts in its folder and the command-line tools its instructions call (apt-get and python3). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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 File Carving With Foremost 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 385 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Performing File Carving With Foremost: 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.
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.