Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill investigating-ransomware-attack-artifactsgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/investigating-ransomware-attack-artifacts)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/investigating-ransomware-attack-artifacts"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/investigating-ransomware-attack-artifacts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/investigating-ransomware-attack-artifacts"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/investigating-ransomware-attack-artifacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 400 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Privilege Escalation · line 85 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.04329 |
| Opus 5 | $0.00018 | $0.02165 |
| Sonnet 5 | $0.00007 | $0.00866 |
| Haiku 4.5 | $0.00004 | $0.00433 |
Grade B, and why
investigating-ransomware-attack-artifacts scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
# Linux: sudo insmod lime.ko "path=/evidence/memory.lime format=lime" How it starts
The opening of the file, as written. The whole thing — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigating Ransomware Attack Artifacts
When to Use
- Immediately after discovering ransomware encryption on systems
- When performing forensic analysis to understand the full scope of a ransomware incident
- For identifying the ransomware variant and determining if decryption is possible
- When tracing the attack chain from initial access to encryption
- For documenting evidence to support law enforcement and insurance claims
Prerequisites
- Forensic images of affected systems (preserve before remediation)
- Memory dumps captured before system shutdown (if available)
- Ransom notes and encrypted file samples
- Network traffic captures from the attack period
- Windows Event Logs, Prefetch files, and registry hives
- Access to ransomware identification tools (ID Ransomware, No More Ransom)
- Isolated sandbox environment for malware analysis
Workflow
Step 1: Preserve Evidence and Identify the Ransomware Variant
# CRITICAL: Do NOT restart systems. Preserve memory first if possible.
# Encryption keys may still be in memory.
# Capture memory from running systems
# Windows: DumpIt.exe (generates memory.raw)
# Linux: sudo insmod lime.ko "path=/evidence/memory.lime format=lime"
# Collect ransom note
cp /mnt/evidence/Users/*/Desktop/README*.txt /cases/case-2024-001/ransomware/ransom_notes/
cp /mnt/evidence/Users/*/Desktop/DECRYPT*.txt /cases/case-2024-001/ransomware/ransom_notes/
cp /mnt/evidence/Users/*/Desktop/HOW_TO*.txt /cases/case-2024-001/ransomware/ransom_notes/
find /mnt/evidence/ -name "*.hta" -o -name "*DECRYPT*" -o -name "*RANSOM*" -o -name "*README*" \
2>/dev/null | head -20 > /cases/case-2024-001/ransomware/note_locations.txt
# Collect sample encrypted files (for identification)
find /mnt/evidence/Users/ -name "*.encrypted" -o -name "*.locked" -o -name "*.crypted" \
-o -name "*.crypt" -o -name "*.enc" | head -10 > /cases/case-2024-001/ransomware/encrypted_samples.txt
# Copy sample encrypted files
mkdir -p /cases/case-2024-001/ransomware/samples/
head -5 /cases/case-2024-001/ransomware/encrypted_samples.txt | while read f; do
cp "$f" /cases/case-2024-001/ransomware/samples/
done
# Identify ransomware variant using file extension and ransom note
python3 << 'PYEOF'
import os, hashlib, json
ransomware_indicators = {
'.lockbit': 'LockBit',
'.blackcat': 'BlackCat/ALPHV',
'.royal': 'Royal',
'.akira': 'Akira',
'.clop': 'Cl0p',
'.conti': 'Conti',
'.ryuk': 'Ryuk',
'.revil': 'REvil/Sodinokibi',
'.maze': 'Maze',
'.phobos': 'Phobos',
'.dharma': 'Dharma/CrySIS',
'.stop': 'STOP/Djvu',
'.hive': 'Hive',
'.blackbasta': 'Black Basta',
'.play': 'Play',
}
# Check encrypted file extensions
samples_dir = '/cases/case-2024-001/ransomware/samples/'
for f in os.listdir(samples_dir):
ext = os.path.splitext(f)[1].lower()
variant = ransomware_indicators.get(ext, 'Unknown')
sha256 = hashlib.sha256(open(os.path.join(samples_dir, f), 'rb').read()).hexdigest()
print(f"File: {f}")
print(f" Extension: {ext}")
print(f" Suspected Variant: {variant}")
print(f" SHA-256: {sha256}")
print()
# Parse ransom note for IoCs
note_dir = '/cases/case-2024-001/ransomware/ransom_notes/'
for note in os.listdir(note_dir):
with open(os.path.join(note_dir, note), 'r', errors='ignore') as f:
content = f.read()
print(f"\n=== Ransom Note: {note} ===")
# Extract bitcoin addresses
import re
btc = re.findall(r'[13][a-km-zA-HJ-NP-Z1-9]{25,34}|bc1[a-zA-HJ-NP-Z0-9]{25,39}', content)
tor = re.findall(r'[a-z2-7]{56}\.onion', content)
emails = re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', content)
if btc: print(f" Bitcoin addresses: {btc}")
if tor: print(f" Tor addresses: {tor}")
if emails: print(f" Contact emails: {emails}")
PYEOF
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 418 lines · 36 tokens per session scan B 69645e50cba7
investigating-ransomware-attack-artifacts is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 36 tokens to every session and 4,329 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
reverse-engineering-workbench
Coordinate EVOKORE reverse-engineering work across Ghidra-style static analysis, semantic recovery, and debugger-guided triage. Use when opening an unfamiliar binary, planning a decompilation workflow, or choosing between static and dynamic analysis paths.
malware-triage-workflow
Triage suspicious binaries by combining static indicators, control-flow hints, YARA or pattern matches, and debugger confirmation. Use when the sample may be malicious, packed, evasive, or operationally risky.
analyzing-windows-prefetch-with-python
Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
analyzing-linux-kernel-rootkits
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (checksyscall, lsmod, hiddenmodules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
tool-calling-tutor
Use when a tool-calling agent does not call a tool, sends wrong arguments, loops without stopping, or needs a function schema. Guides a four-branch diagnosis and five-step schema repair. Do not use for framework-specific, MCP-server, or production-observability questions.