Skill UI

Python CLI trio for multi-touch attribution, funnel conversion diagnostics, and ROI/ROAS/CPA benchmarking to optimize marketing campaigns without external dependencies.

A sophisticated writing editor designed to remove tell-tale signs of AI-generated text. This tool helps transform overly polished, robotic, or academic prose into natural, human-sounding writing. It detects and corrects patterns such as inflated symbolism, passive voice, vague attributions, and repetitive LLM structures, while also calibrating the output to match an author's specific voice and tone. Ideal for content review, editing, and improving natural communication flow.

This skill provides comprehensive governance for multi-user API deployments using OpenRouter. It covers key provisioning, including creating, listing, and revoking individual user keys. Users can set per-user budget limits and track usage costs programmatically. It is designed for corporate or team environments that require strict cost control, usage attribution, and detailed usage reporting.

Designs content-led go-to-market systems that reverse-engineer distribution, repurpose pillar content, and track attribution so every post, video, or newsletter feeds measurable revenue and pipeline growth.

Guides GTM measurement for AI products by outlining revenue, efficiency, pipeline, retention, and funnel metrics, aligning dashboards with attribution models and weekly reviews so founders and revenue leaders monitor ARR, CAC, TTFV, NRR, and cost signals without implementation detail.

This skill provides a systematic framework for analyzing cyber campaign attribution. It involves collecting, weighting, and correlating diverse indicators—such as infrastructure overlaps, TTP consistency, malware code similarities, and operational patterns—to determine the responsible threat actor. Utilizing structured methodologies like Analysis of Competing Hypotheses (ACH) and the Diamond Model, users can build confidence-weighted assessments to enhance threat intelligence accuracy.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

This skill provides a systematic methodology for building comprehensive threat actor profiles using Open-Source Intelligence (OSINT). It involves gathering, correlating, and analyzing publicly available data from diverse sources (e.g., vendor reports, dark web forums, code repositories). Users can map adversary TTPs to MITRE ATT&CK, assess motivations, and build structured dossiers for proactive defensive strategies and attribution assessments.

Structures completed intrusion investigations with the Diamond Model by documenting adversaries, capabilities, infrastructure, and victims plus their relationships, metadata, and pivots to cluster incidents and support campaign attribution, confidence scoring, and analytic write-ups.

Develops comprehensive profiles for Advanced Persistent Threat (APT) groups, criminal organizations, and hacktivist collectivities. This tool aggregates TTP documentation, campaign data, and attribution indicators from major intelligence sources (MITRE ATT&CK, Mandiant, CrowdStrike) to help organizations update threat models, prioritize defensive controls, and prepare executive briefings on sector-specific risks.

This skill provides comprehensive forensic analysis of cryptocurrency payment flows stemming from ransomware attacks. It leverages blockchain APIs and advanced techniques to identify wallet clusters, track fund movement through obfuscation methods like mixers and peel chains, and map funds to exchanges. It is crucial for law enforcement, incident responders, and threat intelligence analysts conducting ransomware attribution.

SHAP (SHapley Additive exPlanations) is a powerful technique used to interpret and audit machine learning model predictions. It calculates feature attributions (SHAP values) to explain how input features map to model outputs. This skill guides users through selecting appropriate explainers, handling multi-output models, validating feature attributions, and generating visualizations for both local and global model understanding.