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 Liberty91LTD/cti-skills --skill carding-financial-fraudgit clone --depth 1 https://github.com/Liberty91LTD/cti-skillsWrote 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/liberty91ltd/cti-skills/carding-financial-fraud)<a href="https://agentmods.dev/skills/liberty91ltd/cti-skills/carding-financial-fraud"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/carding-financial-fraud/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/liberty91ltd/cti-skills/carding-financial-fraud"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/carding-financial-fraud.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00067 | $0.06192 |
| Opus 5.5 | $0.00027 | $0.02477 |
| Sonnet 5.5 | $0.00013 | $0.01238 |
| Haiku 4.5 | $0.00007 | $0.00619 |
Grade A, and why
carding-financial-fraud scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Carding & Financial Fraud
Executive Summary
Carding and financial fraud represent one of the oldest and most mature cybercriminal ecosystems, encompassing the theft, trade, and monetization of payment card data and financial credentials. The ecosystem spans from initial data theft (via digital skimming, phishing, POS malware, and database breaches) through underground marketplace trading to monetization via card-not-present (CNP) fraud, mobile wallet fraud, money mule networks, and reshipping schemes. CNP data still dominates the underground card market, but card-present fraud has returned in a new form: stolen cards provisioned into Apple and Google wallets and used or relayed over NFC [11][13].
The card shop landscape changed materially in 2025. BidenCash, previously the most visible shop, was seized on June 4, 2025 by the US Secret Service and FBI with Dutch police support (about 145 domains; no arrests announced) [9]. In September 2025 the Manhattan District Attorney seized 12 domains belonging to five further vendors, including B1ack's Stash, but Recorded Future assesses that most of those shops kept posting card data on related domains [10][11]. B1ack's Stash took over BidenCash's free-dump marketing tactic and released a further 4.6 million records in May 2026 [12]. Recorded Future counted about 142 million card records posted for sale on dark web marketplaces in 2025, down 19% from 2024, while freely exposed records on Telegram and other sources rose 26% to a comparable volume, and 82% of for-sale CNP records came with victim contact details [11].
The most significant shift in technique is the Chinese-language fraud ecosystem. Smishing kits such as Lighthouse, Darcula and Lucid harvest card data and one-time codes, enroll the cards in mobile wallets on attacker-controlled phones, and cash out in stores or through NFC relay ("ghost tap") tools sold as a service on Telegram [13][14][17]. Google sued the Lighthouse operators in November 2025 and the service reported its servers blocked within days, though researchers expected the activity to continue under other names [15][16]. NFC relay malware has since spread beyond Chinese-speaking vendors to independently developed families in Europe and Latin America [18][20].
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.
- 1mo ago Changed · +56 lines 330c51ffcc89
- 2mo ago First seen · 123 lines · 67 tokens per session scan A 566e2b48a119
carding-financial-fraud is a skill published in the GitHub repository Liberty91LTD/cti-skills (36 stars, last pushed 4d ago), licensed MIT. It adds 67 tokens to every session and 6,192 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-campaign-attribution-evidence
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use…
analyzing-threat-actor-ttps-with-mitre-attack
MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor beh.
analyzing-ransomware-payment-wallets
Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs. Identifies wallet clusters, tracks fund movement through mixers and exchanges, and supports law enforcement attribution. Activates for requests involving ransomware…
analyzing-threat-landscape-with-misp
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat…
analyzing-campaign-attribution-evidence
Use when campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
cfo-advisor
Financial leadership guidance for fiscal strategy, financial modeling, analysis, and capital management. Includes DCF modeling, Monte Carlo simulation, financial ratio analysis, sensitivity testing, and board financial presentations. USE WHEN analyzing financials, building models, preparing board financial packages…