Incident Response
alirezarezvani/claude-skills
A skill your agent uses when a security incident has been detected or declared and needs classification, triage, escalation path determination, and forensic evidence collection.
Agent skill
Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-credentials-from-memory-dump --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/extracting-credentials-from-memory-dump .claude/skills/extracting-credentials-from-memory-dump && 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 "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .claude/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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/extracting-credentials-from-memory-dumpType 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 extracting-credentials-from-memory-dump -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-credentials-from-memory-dump --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/extracting-credentials-from-memory-dump .agents/skills/extracting-credentials-from-memory-dump && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .agents/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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 extracting-credentials-from-memory-dump -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-credentials-from-memory-dump --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/extracting-credentials-from-memory-dump .cursor/skills/extracting-credentials-from-memory-dump && 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 "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .cursor/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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/extracting-credentials-from-memory-dump--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 extracting-credentials-from-memory-dump -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-credentials-from-memory-dump --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/extracting-credentials-from-memory-dump .gemini/skills/extracting-credentials-from-memory-dump && 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 "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .gemini/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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 extracting-credentials-from-memory-dumpInstalls 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 extracting-credentials-from-memory-dump -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/extracting-credentials-from-memory-dump .github/skills/extracting-credentials-from-memory-dump && 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 "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .github/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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 extracting-credentials-from-memory-dump -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 extracting-credentials-from-memory-dump --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/extracting-credentials-from-memory-dump .opencode/skills/extracting-credentials-from-memory-dump && 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 "extracting-credentials-from-memory-dump" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-credentials-from-memory-dump into .opencode/skills/extracting-credentials-from-memory-dump/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-credentials-from-memory-dump", 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.
extracting-credentials-from-memory-dumpExtracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.
Extracting Credentials From Memory Dump is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz. Use when performing memory forensics or incident response on an LSASS or full memory dump and you need to recover credentials or Kerberos material for investigation.
Its SKILL.md is about 3.4k 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 DevOps & Cloud, covering Incident response and 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.
6 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:
python3pipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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.
Extracting Credentials From Memory Dump loads about 3.4k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 466 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 found no risky patterns in SKILL.md.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 466 words, ~3,359 tokens.
.claude/skills/extracting-credentials-from-memory-dump/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.# Install analysis tools
pip install volatility3 pypykatz
# Verify memory dump integrity
sha256sum /cases/case-2024-001/memory/memory.raw
# Identify the OS version
vol -f /cases/case-2024-001/memory/memory.raw windows.info
# Verify LSASS process exists in memory
vol -f /cases/case-2024-001/memory/memory.raw windows.pslist | grep -i lsass
# Output:
# PID PPID ImageFileName Offset(V) Threads Handles SessionId
# 684 564 lsass.exe 0xffffe00123456 35 1234 0# Dump SAM database hashes from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.hashdump \
| tee /cases/case-2024-001/analysis/hashdump.txt
# Output format:
# User RID LM Hash NTLM Hash
# Administrator 500 aad3b435b51404eeaad3b435b51404ee fc525c9683e8fe067095ba2ddc971889
# Guest 501 aad3b435b51404eeaad3b435b51404ee 31d6cfe0d16ae931b73c59d7e0c089c0
# DefaultAccount 503 aad3b435b51404eeaad3b435b51404ee 31d6cfe0d16ae931b73c59d7e0c089c0
# svcbackup 1001 aad3b435b51404eeaad3b435b51404ee 2b576acbe6bcfda7294d6bd18041b8fe
# Extract LSA secrets
vol -f /cases/case-2024-001/memory/memory.raw windows.lsadump \
| tee /cases/case-2024-001/analysis/lsadump.txt
# Extract cached domain credentials
vol -f /cases/case-2024-001/memory/memory.raw windows.cachedump \
| tee /cases/case-2024-001/analysis/cachedump.txt# Dump LSASS process memory (PID from Step 1)
vol -f /cases/case-2024-001/memory/memory.raw windows.memmap --pid 684 --dump \
-o /cases/case-2024-001/analysis/lsass_dump/
# Alternative: Dump all files associated with LSASS
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles --pid 684 \
-o /cases/case-2024-001/analysis/lsass_files/
# Use procdump plugin for cleaner process dump
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles \
--pid 684 -o /cases/case-2024-001/analysis/
# Rename the dump file for pypykatz/mimikatz
mv /cases/case-2024-001/analysis/lsass_dump/pid.684.dmp \
/cases/case-2024-001/analysis/lsass.dmp# Run pypykatz against the full memory dump
pypykatz lsa minidump /cases/case-2024-001/analysis/lsass.dmp \
> /cases/case-2024-001/analysis/pypykatz_results.txt 2>&1
# Run pypykatz against the raw memory dump directly
pypykatz rekall /cases/case-2024-001/memory/memory.raw \
> /cases/case-2024-001/analysis/pypykatz_full.txt 2>&1
# Parse pypykatz output for structured analysis
python3 << 'PYEOF'
import json
# pypykatz can also output JSON
import subprocess
result = subprocess.run(
['pypykatz', 'lsa', 'minidump', '/cases/case-2024-001/analysis/lsass.dmp', '-j'],
capture_output=True, text=True
)
if result.stdout:
data = json.loads(result.stdout)
print("=== EXTRACTED CREDENTIALS ===\n")
for session_key, session in data.get('logon_sessions', {}).items():
username = session.get('username', 'Unknown')
domain = session.get('domainname', '')
logon_server = session.get('logon_server', '')
logon_time = session.get('logon_time', '')
sid = session.get('sid', '')
if username and username != '(null)':
print(f"Session: {domain}\\{username}")
print(f" SID: {sid}")
print(f" Logon Server: {logon_server}")
print(f" Logon Time: {logon_time}")
# NTLM hashes
msv = session.get('msv_creds', [])
for cred in msv:
nt = cred.get('NThash', '')
lm = cred.get('LMHash', '')
if nt:
print(f" NTLM Hash: {nt}")
if lm:
print(f" LM Hash: {lm}")
# Kerberos tickets
kerb = session.get('kerberos_creds', [])
for cred in kerb:
password = cred.get('password', '')
if password:
print(f" Kerberos Password: {password}")
tickets = cred.get('tickets', [])
for ticket in tickets:
print(f" Kerberos Ticket: {ticket.get('server', '')} (type: {ticket.get('enc_type', '')})")
# WDigest (plaintext on older systems)
wdigest = session.get('wdigest_creds', [])
for cred in wdigest:
pwd = cred.get('password', '')
if pwd:
print(f" WDigest Password: {pwd}")
# DPAPI master keys
dpapi = session.get('dpapi_creds', [])
for cred in dpapi:
mk = cred.get('masterkey', '')
if mk:
print(f" DPAPI Master Key: {mk[:40]}...")
print()
PYEOF# Extract Kerberos tickets from memory
python3 << 'PYEOF'
import subprocess, json
result = subprocess.run(
['pypykatz', 'lsa', 'minidump', '/cases/case-2024-001/analysis/lsass.dmp', '-j', '-k', '/cases/case-2024-001/analysis/kerberos/'],
capture_output=True, text=True
)
# pypykatz exports .kirbi files to the specified directory
import os
kirbi_dir = '/cases/case-2024-001/analysis/kerberos/'
if os.path.exists(kirbi_dir):
for f in os.listdir(kirbi_dir):
if f.endswith('.kirbi'):
filepath = os.path.join(kirbi_dir, f)
size = os.path.getsize(filepath)
print(f" Kerberos ticket: {f} ({size} bytes)")
PYEOF
# Search process memory for authentication tokens and API keys
vol -f /cases/case-2024-001/memory/memory.raw windows.strings --pid 684 | \
grep -iE '(bearer |authorization:|api[_-]key|token=|password=|secret=)' \
> /cases/case-2024-001/analysis/auth_strings.txt
# Search for cloud credentials in memory
vol -f /cases/case-2024-001/memory/memory.raw windows.strings | \
grep -iE '(AKIA[A-Z0-9]{16}|ASIA[A-Z0-9]{16}|aws_secret_access_key)' \
> /cases/case-2024-001/analysis/aws_credentials.txt
# Search for browser session tokens
vol -f /cases/case-2024-001/memory/memory.raw windows.strings | \
grep -iE '(session_id=|PHPSESSID=|JSESSIONID=|_ga=|sid=)' \
> /cases/case-2024-001/analysis/session_tokens.txt# Generate credential compromise assessment
python3 << 'PYEOF'
print("""
CREDENTIAL EXTRACTION REPORT
==============================
Case: 2024-001
Source: memory.raw (16 GB Windows 10 memory dump)
Analysis Date: 2024-01-20
COMPROMISED ACCOUNTS:
=====================
1. Local Accounts (SAM):
- Administrator (RID 500): NTLM hash extracted
- svcbackup (RID 1001): NTLM hash extracted
- SQLService (RID 1002): NTLM hash extracted
2. Domain Accounts (LSASS):
- CORP\\admin.user: NTLM hash + Kerberos TGT
- CORP\\svc.backup: NTLM hash + plaintext password (WDigest)
- CORP\\domain.admin: Kerberos TGS tickets for 3 services
3. Cached Domain Credentials:
- CORP\\helpdesk.user: DCC2 hash
- CORP\\it.manager: DCC2 hash
4. Cloud Credentials:
- AWS Access Key: AKIA... found in process memory (PID 3456)
- Azure AD token found in browser process memory
IMMEDIATE ACTIONS REQUIRED:
- Reset passwords for all listed accounts
- Revoke and rotate AWS access keys
- Invalidate all active Kerberos tickets (krbtgt reset)
- Review DPAPI-protected data for additional exposure
""")
PYEOF| Concept | Description |
|---|---|
| LSASS (Local Security Authority) | Windows process managing authentication, storing credentials in memory |
| NTLM hash | NT LAN Manager hash of user password used for authentication |
| Kerberos TGT | Ticket Granting Ticket allowing request of service tickets |
| WDigest | Legacy authentication protocol storing plaintext passwords in memory (pre-Win8.1) |
| DPAPI | Data Protection API using master keys derived from user credentials |
| DCC2 (Domain Cached Credentials) | Cached domain password hashes for offline logon |
| LSA Secrets | Encrypted service account passwords and other secrets stored by LSA |
| Pass-the-Hash | Attack technique using extracted NTLM hashes without knowing the plaintext password |
| Tool | Purpose |
|---|---|
| Volatility 3 | Memory forensics framework with hashdump, lsadump, cachedump plugins |
| pypykatz | Python implementation of Mimikatz for cross-platform LSASS analysis |
| Mimikatz | Windows credential extraction tool (used offline against dumps) |
| secretsdump.py | Impacket tool for extracting secrets from SAM/SYSTEM/SECURITY |
| hashcat | Password hash cracking for recovered NTLM and DCC2 hashes |
| John the Ripper | Alternative password cracking tool |
| Rubeus | Kerberos ticket manipulation and extraction tool |
| Impacket | Python toolkit for working with Windows network protocols and credentials |
Scenario 1: Post-Breach Credential Assessment Extract all cached credentials from LSASS memory to determine which accounts were exposed, prioritize password resets based on privilege level, check for golden ticket material (krbtgt hash), assess if cloud credentials were accessible.
Scenario 2: Lateral Movement Investigation Extract NTLM hashes and Kerberos tickets to understand how the attacker moved between systems, identify pass-the-hash/pass-the-ticket artifacts, correlate extracted credentials with network logon events in event logs.
Scenario 3: Ransomware Operator Credential Theft Analyze pre-encryption memory dump for Mimikatz execution evidence, extract all available credential types, determine if domain admin credentials were obtained, assess if krbtgt was compromised (golden ticket), plan credential rotation strategy.
Scenario 4: Cloud Credential Theft from Endpoint Search endpoint memory for AWS access keys, Azure tokens, and GCP service account keys stored by CLI tools and browsers, identify exposed cloud permissions, immediately rotate discovered credentials, audit cloud audit logs for unauthorized access.
Credential Extraction Summary:
Source: memory.raw (16 GB, Windows 10 Build 19041)
LSASS PID: 684
Credentials Recovered:
Local NTLM Hashes: 4 accounts
Domain NTLM Hashes: 3 accounts
Kerberos TGTs: 2 tickets
Kerberos TGS: 5 service tickets
Plaintext Passwords: 1 (WDigest - svc.backup)
Cached Domain Creds: 2 DCC2 hashes
LSA Secrets: 3 service account passwords
DPAPI Master Keys: 4 keys recovered
Cloud Credentials: 1 AWS access key, 1 Azure token
Highest Privilege Compromised: Domain Admin (CORP\domain.admin)
Recommended Actions:
- Immediate: Reset all extracted account passwords
- Immediate: Rotate AWS access key AKIA...
- Urgent: Double krbtgt password reset (golden ticket mitigation)
- High: Revoke all Kerberos tickets via krbtgt rotation
- Medium: Audit DPAPI-protected data exposure© 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/extracting-credentials-from-memory-dump of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Extracting Credentials From Memory Dump 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 |
|---|---|---|---|---|---|---|
| Extracting Credentials From Memory Dump this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| Incident Responsealirezarezvani/claude-skills | 28k | — | ~3.8k | Automated safety check: Pass | MIT | |
| Responding To Incidentstrilwu/secskills | 156 | — | ~3.9k | Automated safety check: Notes | MIT | |
| Incident Responsehypnguyen1209/offensive-claude | 386 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Forensics OsqueryAgentSecOps/SecOpsAgentKit | 220 | 1 repos | ~4.9k | Automated safety check: Notes | Custom licence | |
| Ir VelociraptorAgentSecOps/SecOpsAgentKit | 220 | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence |
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Categories
Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz. Extracting Credentials From Memory Dump is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.
Extracting Credentials From Memory Dump fits situations like: performing memory forensics; incident response on an LSASS; full memory dump and you need to recover credentials; kerberos material for investigation.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a claude-code`. Or copy the skill folder (skills/extracting-credentials-from-memory-dump in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/extracting-credentials-from-memory-dump in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a codex`. Or copy the skill folder (skills/extracting-credentials-from-memory-dump in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/extracting-credentials-from-memory-dump 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 extracting-credentials-from-memory-dump -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extracting-credentials-from-memory-dump, .gemini/skills/extracting-credentials-from-memory-dump, .github/skills/extracting-credentials-from-memory-dump and .opencode/skills/extracting-credentials-from-memory-dump in your project.
Going by SKILL.md and its folder, Extracting Credentials From Memory Dump needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. 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.
Extracting Credentials From Memory Dump 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 3.4k 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 664 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Extracting Credentials From Memory Dump: Incident Response (alirezarezvani/claude-skills, 28k stars), Responding To Incidents (trilwu/secskills, 156 stars), Incident Response (hypnguyen1209/offensive-claude, 386 stars) and Forensics Osquery (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 33,922 GitHub stars. The repository holds 637 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.