Skill UI

An AI-driven utility that guides users through selecting the most suitable Apify Actor for complex web scraping tasks. It provides comprehensive coverage across major social media platforms and websites, including Instagram, Facebook, TikTok, YouTube, and Google Maps, ensuring a unified workflow for efficient data extraction.

This skill provides comprehensive access to X (Twitter) data via the Xquik platform. It enables advanced data tasks such as searching tweets, retrieving detailed user profiles and engagement metrics (likes, retweets), performing bulk follower extraction, running giveaway draws, and setting up real-time account monitoring via webhooks. It supports over 19 extraction tools and API integration, making it a powerful social media intelligence tool.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This skill provides comprehensive read-only access to explore, query, and analyze datasets hosted on Hugging Face. It utilizes the Dataset Viewer API to list splits, preview rows, perform text searches, filter records, and retrieve crucial metadata and Parquet links. Furthermore, it supports advanced data extraction by enabling complex SQL querying (via parquetlens) directly against dataset shards, allowing export into CSV, JSON, or Parquet formats.

Xquik unlocks Twitter data via REST APIs, MCP tools, and webhooks, covering tweet/user lookups, follower extraction, giveaway draws, trending topics, and accurate account monitoring for real-time automation.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Examines packet captures, Zeek logs, and NetFlow data to uncover C2 channels, lateral movement, and exfiltration during security incidents, guiding analysts through capture, filtering, and IOC extraction workflows.

Step-by-step workflow for pentesters to detect and exploit NoSQL injection in MongoDB, CouchDB, and similar stacks, covering operator abuse, authentication bypass, blind data extraction, JavaScript injection, and automation with NoSQLMap.

Use LaZagne during red-team engagements to automate credential harvesting from browsers, email clients, databases, system stores, Wi-Fi profiles, and applications, enabling lateral movement and privilege escalation after initial access.

Guide for acquiring and analyzing smartphone data with Cellebrite UFED plus ALEAPP/iLEAPP and libimobiledevice, covering device prep, logical/physical dumps, and messaging extraction for investigations.

Use steganalysis and metadata tools to detect concealed payloads inside images, audio, and video, guiding investigators through metadata review, automated scans, LSB statistics, and extraction attempts for covert channel detection.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.