Osint Methodology
elementalsouls/Claude-OSINT
Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments.
Agent skill
Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-ransomware-precursors-in-network -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-ransomware-precursors-in-network --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/detecting-ransomware-precursors-in-network .claude/skills/detecting-ransomware-precursors-in-network && 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 "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .claude/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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/detecting-ransomware-precursors-in-networkType 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 detecting-ransomware-precursors-in-network -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-ransomware-precursors-in-network --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/detecting-ransomware-precursors-in-network .agents/skills/detecting-ransomware-precursors-in-network && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .agents/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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 detecting-ransomware-precursors-in-network -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-ransomware-precursors-in-network --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/detecting-ransomware-precursors-in-network .cursor/skills/detecting-ransomware-precursors-in-network && 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 "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .cursor/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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/detecting-ransomware-precursors-in-network--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 detecting-ransomware-precursors-in-network -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-ransomware-precursors-in-network --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/detecting-ransomware-precursors-in-network .gemini/skills/detecting-ransomware-precursors-in-network && 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 "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .gemini/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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 detecting-ransomware-precursors-in-networkInstalls 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 detecting-ransomware-precursors-in-network -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/detecting-ransomware-precursors-in-network .github/skills/detecting-ransomware-precursors-in-network && 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 "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .github/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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 detecting-ransomware-precursors-in-network -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 detecting-ransomware-precursors-in-network --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/detecting-ransomware-precursors-in-network .opencode/skills/detecting-ransomware-precursors-in-network && 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 "detecting-ransomware-precursors-in-network" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-ransomware-precursors-in-network into .opencode/skills/detecting-ransomware-precursors-in-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-ransomware-precursors-in-network", 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.
detecting-ransomware-precursors-in-networkDetects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance…
Detecting Ransomware Precursors In Network is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware…
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).
It sits in Security, covering Security operations, OSINT and Red teaming and adversary simulation. 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 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
curlpython3From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
feodotracker.abuse.churlhaus.abuse.chthreatfox.abuse.chcisa.govFrom 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.
Detecting Ransomware Precursors In Network loads about 3.7k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 162 tokens; SKILL.md has 802 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). 802 words, ~3,723 tokens.
.claude/skills/detecting-ransomware-precursors-in-network/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.Do not use for post-encryption response (see recovering-from-ransomware-attack). This skill focuses on the pre-encryption detection window where containment can prevent data loss.
Map network-observable indicators to each pre-encryption phase:
| Kill Chain Phase | Network Indicators | Detection Source |
|---|---|---|
| Initial Access | RDP brute force, VPN credential stuffing, phishing callback | Firewall logs, IDS, proxy logs |
| C2 Establishment | Cobalt Strike beacons (HTTPS/DNS), Sliver/Brute Ratel callbacks | Zeek SSL/HTTP logs, DNS logs |
| Credential Harvesting | NTLM relay, Kerberoasting, DCSync traffic | Zeek Kerberos/NTLM logs, DC logs |
| Reconnaissance | Internal port scanning, AD enumeration (LDAP/SMB) | Zeek conn.log, flow data |
| Lateral Movement | PsExec/WMI/WinRM traffic, RDP pivoting, SMB file copies | Zeek SMB/DCE-RPC logs |
| Staging | Data aggregation, archive creation, cloud upload prep | Proxy logs, DNS logs, DLP |
Suricata rules for common ransomware precursors:
# Cobalt Strike default HTTPS beacon profile detection
alert tls $HOME_NET any -> $EXTERNAL_NET any (msg:"RANSOMWARE PRECURSOR - Cobalt Strike Default TLS Certificate"; tls.cert_subject; content:"Major Cobalt Strike"; sid:3000001; rev:1;)
# Cobalt Strike DNS beacon
alert dns $HOME_NET any -> any 53 (msg:"RANSOMWARE PRECURSOR - Cobalt Strike DNS Beacon Pattern"; dns.query; pcre:"/^[a-z0-9]{3}\.[a-z]{4,8}\./"; threshold:type both, track by_src, count 50, seconds 60; sid:3000002; rev:1;)
# Mimikatz network signature (DCSync - DRS GetNCChanges)
alert tcp $HOME_NET any -> $HOME_NET 135 (msg:"RANSOMWARE PRECURSOR - Possible DCSync/Mimikatz"; content:"|05 00 0b|"; offset:0; depth:3; content:"|e3 51 4d 2b 4b 47 15 d2|"; sid:3000003; rev:1;)
# Internal network scanning (many connections, few bytes)
alert tcp $HOME_NET any -> $HOME_NET any (msg:"RANSOMWARE PRECURSOR - Internal Port Scan"; flags:S; threshold:type both, track by_src, count 100, seconds 10; sid:3000004; rev:1;)
# PsExec service installation over SMB
alert tcp $HOME_NET any -> $HOME_NET 445 (msg:"RANSOMWARE PRECURSOR - PsExec Service Install"; content:"|ff|SMB"; content:"PSEXESVC"; nocase; sid:3000005; rev:1;)
# RDP brute force from internal host (lateral movement)
alert tcp $HOME_NET any -> $HOME_NET 3389 (msg:"RANSOMWARE PRECURSOR - Internal RDP Brute Force"; flow:to_server,established; threshold:type both, track by_src, count 20, seconds 60; sid:3000006; rev:1;)
# Large SMB file transfer (data staging)
alert tcp $HOME_NET any -> $HOME_NET 445 (msg:"RANSOMWARE PRECURSOR - Large SMB Transfer Possible Staging"; flow:to_server,established; dsize:>60000; threshold:type both, track by_src, count 100, seconds 300; sid:3000007; rev:1;)Zeek scripts for behavioral detection:
# detect_ransomware_precursors.zeek
# Detect high volume of failed SMB connections (credential testing)
@load base/protocols/smb
module RansomwarePrecursor;
export {
redef enum Notice::Type += {
SMB_Brute_Force,
Suspicious_Internal_Scan,
Excessive_DNS_Queries,
SMB_Admin_Share_Access,
};
const smb_fail_threshold = 10 &redef;
const scan_threshold = 50 &redef;
const dns_query_threshold = 200 &redef;
}
global smb_fail_count: table[addr] of count &default=0 &create_expire=5min;
global conn_count: table[addr] of set[addr] &create_expire=1min;
event smb2_message(c: connection, hdr: SMB2::Header, is_orig: bool) {
if (hdr$status != 0) {
++smb_fail_count[c$id$orig_h];
if (smb_fail_count[c$id$orig_h] >= smb_fail_threshold) {
NOTICE([$note=SMB_Brute_Force,
$msg=fmt("Host %s has %d failed SMB attempts", c$id$orig_h, smb_fail_count[c$id$orig_h]),
$src=c$id$orig_h,
$identifier=cat(c$id$orig_h)]);
}
}
}
event new_connection(c: connection) {
if (c$id$orig_h in Site::local_nets && c$id$resp_h in Site::local_nets) {
if (c$id$orig_h !in conn_count)
conn_count[c$id$orig_h] = set();
add conn_count[c$id$orig_h][c$id$resp_h];
if (|conn_count[c$id$orig_h]| >= scan_threshold) {
NOTICE([$note=Suspicious_Internal_Scan,
$msg=fmt("Host %s connected to %d internal hosts in 1 min", c$id$orig_h, |conn_count[c$id$orig_h]|),
$src=c$id$orig_h,
$identifier=cat(c$id$orig_h)]);
}
}
}Splunk correlation for ransomware precursor chain:
| tstats count FROM datamodel=Network_Traffic
WHERE earliest=-24h All_Traffic.dest_port IN (445, 135, 139, 3389, 5985, 5986)
AND All_Traffic.src_ip IN 10.0.0.0/8
AND All_Traffic.dest_ip IN 10.0.0.0/8
BY All_Traffic.src_ip, All_Traffic.dest_port, _time span=1h
| stats dc(All_Traffic.dest_port) as port_count,
values(All_Traffic.dest_port) as ports,
count as total_conns
BY All_Traffic.src_ip
| where port_count >= 3 AND total_conns > 50
| rename All_Traffic.src_ip as src_ip
| lookup threat_intel_ioc ip as src_ip OUTPUT threat_type
| eval risk_score = case(
port_count >= 5 AND total_conns > 200, "CRITICAL",
port_count >= 3 AND total_conns > 50, "HIGH",
1=1, "MEDIUM")
| table src_ip, ports, port_count, total_conns, risk_score, threat_typeMicrosoft Sentinel KQL - Ransomware precursor correlation:
let timeframe = 24h;
let RDPBruteForce = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 4625
| where LogonType == 10
| summarize FailedRDP = count() by TargetAccount, IpAddress, bin(TimeGenerated, 1h)
| where FailedRDP > 10;
let SuspiciousSMB = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 5145
| where ShareName has "ADMIN$" or ShareName has "C$" or ShareName has "IPC$"
| summarize AdminShareAccess = count() by SubjectUserName, IpAddress, bin(TimeGenerated, 1h)
| where AdminShareAccess > 5;
let ServiceInstalls = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 7045
| where ServiceName has_any ("PSEXESVC", "meterpreter", "beacon");
RDPBruteForce
| join kind=inner SuspiciousSMB on IpAddress
| project TimeGenerated, IpAddress, TargetAccount, FailedRDP, SubjectUserName, AdminShareAccess
| extend AlertTitle = "Ransomware Precursor: RDP Brute Force + Admin Share Access"Configure automated IOC feeds for known ransomware infrastructure:
# Download and update ransomware C2 blocklists
# abuse.ch Feodo Tracker (Cobalt Strike, TrickBot, BazarLoader C2s)
curl -s https://feodotracker.abuse.ch/downloads/ipblocklist.csv | \
grep -v "^#" | cut -d, -f2 > /opt/threat-intel/feodo_ips.txt
# abuse.ch URLhaus (malware distribution URLs)
curl -s https://urlhaus.abuse.ch/downloads/csv_recent/ | \
grep -v "^#" | cut -d, -f3 > /opt/threat-intel/urlhaus_urls.txt
# abuse.ch ThreatFox (ransomware IOCs)
curl -s https://threatfox.abuse.ch/export/csv/recent/ | \
grep -i "ransomware" | cut -d, -f3 > /opt/threat-intel/ransomware_iocs.txt
# CISA Known Exploited Vulnerabilities (initial access vectors)
curl -s https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json | \
python3 -c "import json,sys; data=json.load(sys.stdin); [print(v['cveID'],v['vendorProject'],v['product']) for v in data['vulnerabilities'] if 'ransomware' in v.get('knownRansomwareCampaignUse','').lower()]"Define triage procedures based on precursor confidence level:
| Alert Type | Confidence | Response Time | Action |
|---|---|---|---|
| Confirmed Cobalt Strike beacon | High | 15 minutes | Isolate host immediately, trigger IR |
| DCSync/Kerberoasting from non-DC | High | 15 minutes | Disable account, isolate host, trigger IR |
| Internal port scan + admin share access | Medium-High | 30 minutes | Investigate source host, check EDR telemetry |
| RDP brute force from internal host | Medium | 1 hour | Verify if legitimate admin activity, check host |
| Unusual DNS query volume | Low-Medium | 4 hours | Check for DNS tunneling, correlate with other alerts |
| Term | Definition |
|---|---|
| Ransomware Precursor | Network activity that precedes ransomware encryption, including C2 communication, lateral movement, and data staging |
| Dwell Time | Time between initial compromise and ransomware deployment, averaging 21 days but sometimes as short as 17 minutes |
| Initial Access Broker (IAB) | Threat actors who sell compromised network access to ransomware operators on dark web markets |
| Beaconing | Periodic C2 callbacks from implants (Cobalt Strike, Sliver) that can be detected by analyzing connection timing patterns |
| Kerberoasting | Credential harvesting technique requesting Kerberos service tickets for offline cracking, detectable via unusual TGS-REQ patterns |
| DCSync | Technique using Directory Replication Service to extract password hashes from domain controllers, critical ransomware precursor |
Context: A manufacturing company's SOC receives an alert for unusual SMB traffic from a workstation (10.1.5.42) in the engineering department. The workstation connected to 47 internal hosts on port 445 within 5 minutes at 2:00 AM.
Approach:
Pitfalls:
## Ransomware Precursor Detection Alert
**Alert ID**: [SIEM-generated ID]
**Detection Time**: [Timestamp]
**Source Host**: [IP / Hostname]
**Confidence**: [High / Medium / Low]
**Kill Chain Phase**: [Initial Access / C2 / Credential Harvest / Recon / Lateral Movement / Staging]
### Indicators Detected
| Indicator | Source | Detail | MITRE ATT&CK |
|-----------|--------|--------|--------------|
| [Type] | [Zeek/Suricata/SIEM] | [Description] | [T-ID] |
### Correlation Chain
1. [Timestamp] - [Event 1]
2. [Timestamp] - [Event 2]
3. [Timestamp] - [Event 3]
### Recommended Actions
- [ ] Isolate source host from network
- [ ] Check EDR telemetry for host-based indicators
- [ ] Reset credentials for affected user accounts
- [ ] Block identified C2 infrastructure
- [ ] Escalate to incident response team© 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 7 other files (scripts, references, assets) in skills/detecting-ransomware-precursors-in-network of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Detecting Ransomware Precursors In Network 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 |
|---|---|---|---|---|---|---|
| Detecting Ransomware Precursors In Network this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| Osint Methodologyelementalsouls/Claude-OSINT | 2.8k | — | ~8.7k | Automated safety check: Notes | MIT | |
| Threat Intelligence OSINTzhaoxuya520/reverse-skill | 41k | 1 repos | ~1k | Automated safety check: Pass | MIT | |
| Councilwarpdotdev/common-skills | 610 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Enrich Iocdandye/ai-runbooks | 127 | — | ~702 | Automated safety check: Pass | Apache-2.0 | |
| Cybersecurityohmyjahh/xquads-squads | 277 | — | ~895 | Automated safety check: Pass | MIT |
elementalsouls/Claude-OSINT
Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments.
zhaoxuya520/reverse-skill
Enriches IOCs, campaigns, impersonation and scams from public sources, including bounded X search through Xquik, and checks each lead against independent evidence.
warpdotdev/common-skills
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation.
dandye/ai-runbooks
Enrich an IOC (IP, domain, hash, URL) with threat intelligence.
ohmyjahh/xquads-squads
Squad de 15 agentes de seguranca ofensiva e defensiva (Georgia Weidman, Peter Kim, Jim Manico, Chris Sanders, Omar Santos, Marcus Carey) cobrindo pentest, red team, blue team, AppSec, recon e…
digoal/blog
Answer general or cross-domain questions with a non-pleasing rational mode: adversarial red-team and blue-team expert analysis, mutually exclusive conclusions, up to five debate rounds, saved…
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
Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance…. Detecting Ransomware Precursors In Network is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior.
Detecting Ransomware Precursors In Network fits situations like: tasks that involve Security operations; tasks that involve OSINT; tasks that involve Red teaming and adversary simulation.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-ransomware-precursors-in-network -a claude-code`. Or copy the skill folder (skills/detecting-ransomware-precursors-in-network in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-ransomware-precursors-in-network in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-ransomware-precursors-in-network -a codex`. Or copy the skill folder (skills/detecting-ransomware-precursors-in-network in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-ransomware-precursors-in-network 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 detecting-ransomware-precursors-in-network -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-ransomware-precursors-in-network, .gemini/skills/detecting-ransomware-precursors-in-network, .github/skills/detecting-ransomware-precursors-in-network and .opencode/skills/detecting-ransomware-precursors-in-network in your project.
Going by SKILL.md and its folder, Detecting Ransomware Precursors In Network needs Python for the scripts in its folder and the command-line tools its instructions call (curl and python3). Our summary lists: Python 3.
SKILL.md names 4 domains. In commands or code: feodotracker.abuse.ch, urlhaus.abuse.ch, threatfox.abuse.ch and cisa.gov; the agent is likely to contact these when it follows the instructions. 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.
Detecting Ransomware Precursors In Network 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 3.7k tokens (SKILL.md is roughly 15k 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 2.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Detecting Ransomware Precursors In Network: Osint Methodology (elementalsouls/Claude-OSINT, 2.8k stars), Threat Intelligence OSINT (zhaoxuya520/reverse-skill, 41k stars), Council (warpdotdev/common-skills, 610 stars) and Enrich Ioc (dandye/ai-runbooks, 127 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.