Skill UI

This meta-skill is designed for generating comprehensive competitive intelligence briefs for sales, business development, and strategy teams. It monitors signals across various dimensions (pricing, product, leadership, funding, etc.) for specified competitors or targets over a defined time window. It helps track changes against a baseline and provides actionable recommendations, moving beyond simple raw data dumps.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

This skill extracts precise, pixel-level data from various chart types (bar, line, scatter, pie) and graphs presented in images. It generates a comprehensive structured data table, including confidence levels, axis details, and assumptions, making the raw data ready for spreadsheets or further analysis. Ideal for digitizing charts, transcribing graph values, or turning visual data into actionable formats.

This skill monitors key competitors across various signals (Product, Pricing, Hiring, Partnerships) and translates raw data into structured, actionable intelligence briefs. It assesses the strategic implications of competitor moves relative to your current roadmap, providing high/medium/low priority signals, recommended actions, and a concise strategic summary. Ideal for weekly competitive landscape monitoring and strategic planning.

This skill analyzes comprehensive sprint delivery data, including ticket completion rates, carry-over analysis, and unplanned work. It generates a structured, data-grounded retrospective brief, transforming raw metrics into concrete discussion prompts (Start/Stop/Continue) and actionable, testable experiments for the next iteration. It helps teams focus on measurable solutions rather than subjective debates.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

A comprehensive guide on querying data stored in Fabric's OneLake using DuckDB. This skill supports two operational modes: local command line interface (CLI) for ad-hoc analysis, and in-notebook mode for seamless integration within Fabric notebooks. It can read Delta Lake tables and various raw formats (Parquet, CSV, JSON), making it ideal for data exploration, validation, and schema discovery in a read-only fashion.

Hayabusa is a specialized threat-hunting and fast-forensics tool designed to analyze Windows EVTX event logs. It applies a comprehensive library of Sigma detection rules to raw log data, generating a single, prioritized, chronological timeline. The output includes severity levels, MITRE ATT&CK mappings, and detailed metrics, enabling analysts to perform rapid, SIEM-free incident triage and export results for collaborative analysis.

A comprehensive skill for interacting with Weaviate vector databases. It enables advanced operations including semantic search, hybrid keyword querying, schema inspection, data exploration, collection creation, and bulk data import (CSV, JSON, PDF). Use this skill when you need to query data conceptually, retrieve specific raw objects, or manage the lifecycle of Weaviate collections.

This discipline establishes a portable, framework-agnostic layer for ensuring type safety at the network edge of any React or React Native application. It mandates a single, typed API client to wrap all network fetching, guaranteeing that raw wire JSON is immediately parsed into trusted, domain-specific types or standardized error objects before entering the application state. This pattern drastically reduces runtime errors and enhances code robustness.

Transform raw structured data (CSV, Excel, JSON) into professional, long-form, interactive visualization reports. This tool generates comprehensive business review pages featuring KPI dashboards, multiple dynamic charts (using Chart.js/ECharts), detailed data tables, and narrative business insights, making it ideal for weekly or monthly performance reporting.

This template guides the process of turning raw A/B test results, growth experiments, or product feature data into a clear, actionable decision report. It structures the analysis beyond mere metrics by forcing the inclusion of hypothesis, setup details, result interpretation, and a final, justified decision (Ship/Stop/Iterate). Essential for Product Managers and Growth Teams.