Comprehensive digital forensics and signal analysis toolkit for CTF challenges across disk, memory, network, and steganography domains.
Works with
Covers 15+ forensics categories: disk/memory imaging (Volatility, VM forensics, coredumps), Windows registry/event logs/SAM, Linux logs/Docker, network analysis (PCAP, TLS decryption, SMB, NTLMv2), and browser artifact extraction
Includes advanced steganography techniques: image LSB/bitplane extraction, PDF multi-layer stego, audio DTMF/FFT/SSTV, SVG ke
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionctf-forensicsExecute the skills CLI command in your project's root directory to begin installation:
Fetches ctf-forensics from ljagiello/ctf-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ctf-forensics. Access via /ctf-forensics in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Quick reference for forensics CTF challenges. Each technique has a one-liner here; see supporting files for full details.
Python packages (all platforms):
pip install volatility3 Pillow numpy matplotlib
Linux (apt):
apt install binwalk foremost libimage-exiftool-perl tshark sleuthkit \
ffmpeg steghide testdisk john pcapfix
macOS (Homebrew):
brew install binwalk exiftool wireshark sleuthkit ffmpeg \
testdisk john-jumbo
Ruby gems (all platforms):
gem install zsteg
/ctf-crypto./ctf-malware./ctf-web./ctf-misc./ctf-osint./ctf-reverse.# File analysis
file suspicious_file
exiftool suspicious_file # Metadata
binwalk suspicious_file # Embedded files
strings -n 8 suspicious_file
hexdump -C suspicious_file | head # Check magic bytes
# Disk forensics
sudo mount -o loop,ro image.dd /mnt/evidence
fls -r image.dd # List files
photorec image.dd # Carve deleted files
# Memory forensics (Volatility 3)
vol3 -f memory.dmp windows.info
vol3 -f memory.dmp windows.pslist
vol3 -f memory.dmp windows.filescan
See disk-and-memory.md for full Volatility plugin reference, VM forensics, and coredump analysis.
grep -iE "(flag|part|piece|fragment)" server.log # Flag fragments
grep "FLAGPART" server.log | sed 's/.*FLAGPART: //' | uniq | tr -d '\n' # Reconstruct
sort logfile.log | uniq -c | sort -rn | head # Find anomalies
See linux-forensics.md for Linux attack chain analysis and Docker image forensics.
Key Event IDs:
RDP Session IDs (TerminalServices-LocalSessionManager):
import Evtx.Evtx as evtx
with evtx.Evtx("Security.evtx") as log:
for record in log.records():
print(record.xml())
See windows.md for full event ID tables, registry analysis, SAM parsing, USN journal, and anti-forensics detection.
dir/Explorer. Detect with fls -r image.dd | grep ":", extract with icat. See windows.md.If attacker cleared event logs, use these alternative sources:
See windows.md for detailed parsing code and anti-forensics detection checklist.
steghide extract -sf image.jpg
zsteg image.png # PNG/BMP analysis
stegsolve # Visual analysis
Binary border stego: Black/white pixels in 1px image border encode bits clockwise
FFT frequency domain: Image data hidden in 2D FFT magnitude spectrum; try np.fft.fft2 visualization
DTMF audio: Phone tones encoding data; decode with multimon-ng -a DTMF
Multi-layer PDF: Check hidden comments, post-EOF data, XOR with keywords, ROT18 final layer
SSTV + LSB: SSTV signal may be red herring; check 2-bit LSB of audio samples with stegolsb
SVG keyframes: Animation keyTimes/values attributes encode binary/Morse via fill color alternation
PNG chunk reorder: Fix chunk order: IHDR → ancillary → IDAT (in order) → IEND
File overlays: Check after IEND for appended archives with overwritten magic bytes
APNG frame extraction: Animated PNG has multiple frames; extract with apngdis or parse fdAT/fcTL chunks. See steganography.md.
PNG height/CRC manipulation: Modify IHDR height field, brute-force until CRC matches to reveal hidden rows. See steganography.md.
Pixel coordinate chain stego: Linked-list traversal where R=data byte, G/B=next pixel coordinates. See stego-image.md.
AVI frame differential: XOR consecutive video frames to reveal hidden data in pixel differences. See stego-image.md.
Custom freq DTMF: Non-standard dual-tone frequencies; generate spectrogram first (ffmpeg -i audio -lavfi showspectrumpic), map custom grid to keypad digits, decode variable-length ASCII
JPEG DQT LSB: Unused quantization tables (ID 2, 3) carry LSB-encoded data; access via Image.open().quantization and extract bit 0 from each of 64 values
Multi-track audio subtraction: Two nearly-identical audio tracks in MKV/video; sox -m a0.wav "|sox a1.wav -p vol -1" diff.wav cancels shared content, flag appears in spectrogram of difference signal (5-12 kHz band)
Packet interval timing: Identical packets with two distinct interval values (e.g., 10ms/100ms) encode binary; filter by interface, compute inter-packet deltas, threshold to bits
See steganography.md, stego-advanced.md, and stego-advanced-2.md for full code examples and decoding workflows.
exiftool document.pdf # Metadata (often hides flags!)
pdftotext document.pdf - # Extract text
strings document.pdf | grep -i flag
binwalk document.pdf # Embedded files
Advanced PDF stego (Nullcon 2026 rdctd): Six techniques -- invisible text separators, URI annotations with escaped braces, Wiener deconvolution on blurred images, vector rectangle QR codes, compressed object streams (mutool clean -d), document metadata fields.
See steganography.md for full PDF steganography techniques and code.
# Disk images
sudo mount -o loop,ro image.dd /mnt/evidence
fls -r image.dd && photorec image.dd
# VM images (OVA/VMDK)
tar -xvf machine.ova
7z x disk.vmdk -oextracted "Windows/System32/config/SAM" -r
# Memory (Volatility 3)
vol3 -f memory.dmp windows.pslist
vol3 -f memory.dmp windows.cmdline
vol3 -f memory.dmp windows.netscan
vol3 -f memory.dmp windows.dumpfiles --physaddr <addr>
# String carving
strings -a -n 6 memdump.bin | grep -E "FLAG|SSH_CLIENT|SESSMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
ctf-forensics has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for ctf-forensics matched our evaluation — installs cleanly and behaves as described in the markdown.
ctf-forensics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ctf-forensics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ctf-forensics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ctf-forensics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ctf-forensics reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in ctf-forensics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend ctf-forensics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: ctf-forensics is the kind of skill you can hand to a new teammate without a long onboarding doc.
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