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 26zl/cybersec-toolkit --skill analyzing-ransomware-leak-site-intelligencegit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/analyzing-ransomware-leak-site-intelligence)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-ransomware-leak-site-intelligence"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-ransomware-leak-site-intelligence/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/26zl/cybersec-toolkit/analyzing-ransomware-leak-site-intelligence"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-ransomware-leak-site-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.03170 |
| Opus 5 | $0.00023 | $0.01585 |
| Sonnet 5 | $0.00009 | $0.00634 |
| Haiku 4.5 | $0.00005 | $0.00317 |
Grade A, and why
analyzing-ransomware-leak-site-intelligence scanned grade A 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(self.RANSOMWATCH_API, timeout=30) Copies of this mod
4 near-identical copies found in the catalogue:
- analyzing-ransomware-leak-site-intelligence — 95% identical, 12 lines differ
- analyzing-ransomware-leak-site-intelligence — 91% identical, 26 lines differ
- analyzing-ransomware-leak-site-intelligence — 91% identical, 26 lines differ
- analyzing-ransomware-leak-site-intelligence — 88% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Ransomware Leak Site Intelligence
Overview
Ransomware groups operating under double-extortion models maintain data leak sites (DLS) on Tor hidden services where they post victim names, stolen data samples, and countdown timers to pressure payment. In H1 2025, 96 unique ransomware groups were active, listing approximately 535 victims per month. Monitoring these sites provides intelligence on active threat groups, targeted sectors, geographic patterns, and emerging ransomware families. This skill covers safely collecting DLS intelligence, extracting structured data, tracking group activity trends, and producing sector-specific risk assessments.
When to Use
- When investigating security incidents that require analyzing ransomware leak site intelligence
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.9+ with
requests,beautifulsoup4,pandas,matplotliblibraries - Tor proxy (SOCKS5) for accessing .onion sites or commercial DLS monitoring feeds
- Understanding of ransomware double-extortion business model
- Familiarity with major ransomware families (Qilin, Akira, LockBit, BlackCat, Clop)
- Access to ransomware tracking feeds (Ransomwatch, RansomLook, DarkFeed)
Key Concepts
Double Extortion Model
Modern ransomware groups encrypt victim data AND exfiltrate it before encryption. Leak sites serve as public pressure: victims are listed with a countdown timer, partial data samples, and file trees. If ransom is not paid, full data is published. Some groups have moved to triple extortion, adding DDoS threats or contacting victims' customers directly.
DLS Intelligence Value
Leak sites provide: victim identification (company name, sector, country), attack timeline (when listed, deadline, data published), data volume estimates, group capability assessment (sectors targeted, attack frequency, operational tempo), and trend analysis (new groups emerging, groups rebranding, law enforcement takedowns).
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 · 345 lines · 46 tokens per session scan A 2f1695d5bb92
analyzing-ransomware-leak-site-intelligence is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 3,170 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
analyzing-ransomware-leak-site-intelligence
Safely monitor ransomware group Tor-hosted data leak sites (DLS) to collect and extract structured victim posting data, track group activity trends over time, and produce sector- and geography-specific ransomware risk assessments. Use when performing threat intelligence gathering on active ransomware groups or…
analyzing-ransomware-leak-site-intelligence
A threat-intelligence workflow for monitoring ransomware data-leak sites, where criminal groups publish victim names or stolen-data samples to pressure payment. It focuses on collecting and analyzing information about ransomware activity.
analyzing-ransomware-leak-site-intelligence
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
analyzing-ransomware-leak-site-intelligence
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
analyzing-ransomware-leak-site-intelligence
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.
analyzing-ransomware-leak-site-intelligence
Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.