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Found 23 Skills
Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
Build the detection rule set an affiliate program manager uses to flag fraudulent affiliate activity, plus the investigation and escalation path behind it - cookie stuffing, fake lead and form-fill fraud, self-referral rings, traffic-quality baselines, commission holds and clawbacks, and separating real fraud from legitimate low-incrementality coupon or cashback partners. Covers B2B SaaS lead-gen and B2C ecommerce programs. Use whenever the user mentions affiliate fraud, suspicious affiliate traffic, invalid clicks or leads, bot traffic, or an affiliate whose numbers look too good, even if they never say fraud. Do NOT use for consumer refer-a-friend gaming - use mbfinotti/partnerships-skills@referral-abuse-guardrails instead.
Use to monitor, investigate, and prevent abuse within referral programs.
Detection and prevention of illegal tax avoidance, Part IVA anti-avoidance triggers, circular arrangements, and sham transactions. Ensures all recommendations are legally defensible and do not constitute tax fraud.
Audit an affiliate or partner commission payout run for accuracy before money is disbursed - per-line rate and tier recomputation, double-counted conversions, clawback and validation windows, period cutoff, currency, rounding, and tax-form status, delivered as an audit report with severities and recommended holds. Covers B2B SaaS recurring revenue-share and B2C ecommerce order-level CPA programs. Use whenever the user mentions an affiliate payout audit, commission accuracy, payout reconciliation, overpaid or underpaid affiliates, or a pre-disbursement review, even if they never say audit. Do NOT use for spotting fraudulent affiliates - use mbfinotti/partnerships-skills@affiliate-fraud-detection instead.
Define the operational guardrails that stop end customers from gaming a refer-a-friend program - reward caps, velocity limits and cooldowns, reward-hold windows tied to the refund window, eligibility rules, identity-verification gates, a published enforcement ladder, and abuse-monitoring signals. Produces a guardrail spec for B2C and B2B SaaS referral program managers. Use whenever the user mentions referral abuse, self-referral, fake accounts claiming referral rewards, referral rings, or a referral code leaking to coupon forums, even if they never say abuse. Do NOT use for professional-affiliate traffic fraud - use mbfinotti/partnerships-skills@affiliate-fraud-detection instead.
Business logic vulnerability testing for web/mobile/API engagements. Covers workflow bypass, state machine violations, multi-step process abuse, price/quantity/discount manipulation, currency confusion, coupon stacking, refund/chargeback abuse, race conditions on logic boundaries, parameter tampering for hidden flows, role/tenant boundary violations, time-of-check vs use, anti-automation defeat, fraud-detection evasion, and subscription/quota abuse. Use when scoping an application after surface-level OWASP Top 10 has been covered, or when the asset is a transactional/marketplace/fintech/e-commerce/SaaS app where logic flaws produce direct financial impact.
Expert-level financial systems, FinTech, banking, payments, and financial technology
Pipeline skill for healthcare claims data parsing, analysis, and fraud detection. Use when the user asks to parse X12 837 or 835 claim files, manipulate ICD-10 CPT or HCPCS codes, detect billing pattern anomalies, profile providers against specialty peers, identify outlier billing behavior, validate NCCI edits programmatically, detect duplicate claims, run Benford's law analysis on charges, build claims data pipelines, or analyze E&M code distributions. Triggers include "parse X12 837", "parse 835", "claims SQL", "ICD-10 manipulation", "CPT code analysis", "provider profiling", "billing outlier", "NCCI validation code", "duplicate claim detection", "Benford's law charges", "claims ETL", "E&M distribution analysis", "claims analytics pipeline".
Reasoning skill for healthcare claims billing rules and fraud detection logic. Use when the user asks about CMS billing rules, place of service codes, global surgery periods, modifier usage (25 59 76 77), NCCI edit logic, column 1 column 2 code pairs, mutually exclusive procedures, modifier indicators, fraud waste and abuse patterns, E&M upcoding, unbundling, phantom billing, impossible day detection, coding error versus fraud distinction, FWA investigation methodology, or claims audit logic. Triggers include "CMS billing rules", "NCCI edits", "modifier 25", "modifier 59", "global surgery period", "upcoding", "unbundling", "phantom billing", "impossible day", "FWA", "fraud waste abuse", "coding error vs fraud", "claims audit", "billing compliance", "E&M level selection".
Entry point for open-source intelligence, investigation and verification work. Routes any identifier — a name, phone number, email address, username, domain, company, photo, crypto address, tail number or IMO — to the right investigation workflow, after setting an authorised scope. Use when asked to investigate, research, verify, vet, check out, look up, background-check, trace, attribute or find someone or something; when a request involves due diligence, KYC or KYB, counterparty or vendor risk, sanctions and PEP screening, AML, fraud, business email compromise, verifying a supplier before payment, recruitment or marketplace scams, insider threat, executive protection, attack-surface review, journalism or fact-checking; or when someone asks "who is this", "who owns this", "is this real", "where did this come from" or "where do I start". Reference at useosint.com/skills.
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.