Data & AI skills

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.

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Skill for data-sources tasks.

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Elite content marketing strategist specializing in AI-powered content creation, omnichannel distribution, SEO optimization, and data-driven performance marketing. Masters modern content tools, social media automation, and conversion optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive content marketing.

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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Integrates AI/LLM via Semantic Kernel. Plugins, prompt templates, memory stores, agents.

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Renders rich console output. Spectre.Console tables, trees, progress, prompts, live displays.

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Serializes data. System.Text.Json source generators, Protobuf, MessagePack, AOT-safe patterns.

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Designs EF Core data layer architecture. Read/write split, aggregate boundaries, N+1 governance.

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Integrates AI/LLM via Microsoft Agent Framework. Agents, workflows, tools, MCP servers, multi-agent orchestration.

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Chooses a data access approach. EF Core vs Dapper vs ADO.NET decision matrix, tradeoffs.

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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations.

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Produce and maintain repository documentation and release artifacts, including README files, PR descriptions, changelog entries, prompt templates, and instruction docs. Use when the user asks to create or update docs/process files under .github or project root.

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Produce and maintain repository documentation and release artifacts including README files, PR descriptions, changelog entries, prompt templates, and instruction docs. Use when the user asks to create or update docs/process files under .github or project root.

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Display project statistics — phases, plans, requirements, git metrics, and timeline

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Identifies and exploits insecure local data storage vulnerabilities in Android and iOS mobile applications including unencrypted databases, world-readable files, insecure SharedPreferences, plaintext credential storage, and improper keychain/keystore usage. Use when performing mobile penetration testing focused on OWASP M9 (Insecure Data Storage) or assessing compliance with MASVS-STORAGE requirements. Activates for requests involving mobile data storage security, local storage exploitation, SharedPreferences analysis, or mobile data leakage assessment.

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Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data exposure, response filtering bypass, or API over-fetching.

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Detecting and exploiting SQL injection vulnerabilities using sqlmap to extract database contents during authorized penetration tests.

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

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Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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