web-test-report
Generate test report with clear visual indicators - ✅ for pass, ❌ for fail. Summarize results, document failures, provide recommendations.
আপনার এজেন্টের জন্য উপযুক্ত সক্ষমতা খুঁজে নিন।
Generate test report with clear visual indicators - ✅ for pass, ❌ for fail. Summarize results, document failures, provide recommendations.
Expert assistant for monitoring and optimizing performance in the KR92 Bible Voice project. Use when analyzing query performance, optimizing database indexes, reviewing React Query caching, monitoring AI call costs, or identifying N+1 queries. Helps diagnose slow operations across database, frontend, and AI systems.
Compare friction pipeline outputs across different runs, thinking levels, or seeds. Use when comparing visa research results, checking variance across thinking levels (minimal/low/medium), analyzing fee discrepancies, or reviewing route selection consistency.
Data format specialist covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation for modern applications. Use when optimizing data for LLM transmission, implementing high-performance serialization, validating data schemas, or converting between data formats.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create visualizations, or build predictive models with reproducible, code-based workflows.
Autogenerate formula implementations from formula specifications (F-*). Converts mathematical formulas, calculations, and algorithms into production code with tests. Use when F-* includes formula specifications.
A deep analysis mode for the Google AI (Gemini) to fully deconstruct the codebase and market conditions without editing code.
Compare local codebase capabilities against competitor products by researching competitor features via web search and documentation, analyzing local code patterns, and generating an interactive HTML comparison report.
Expert on external API integration, backend proxy architecture, backend functions, rate limiting, and data fetching strategies. Use when integrating with external services, designing backend functions, or understanding how data flows from external sources to the database. References docs/06_integration_spec.md.
Forces documentation of the reasoning behind every significant design decision. Creates a decision log that explains WHY choices were made, alternatives considered, and trade-offs accepted. Prevents unjustified "default" choices.
Applies Fabric AI prompt patterns for summarization, analysis, extraction, code review, and content creation. Use when asked to summarize content, analyze documents, extract insights, review code, create documentation, or transform text. Patterns are in data/patterns/.
Analyze Excel spreadsheet formulas to build dependency DAGs (Directed Acyclic Graphs) and understand calculation chains. This skill should be used when the user wants to reverse-engineer Excel formula dependencies, trace how values are calculated from inputs to outputs, validate formula logic, or create reusable calculators from spreadsheet logic.
Presentation-focused Natural Language to SQL app with PPT-style visualizations.
Performance monitoring and optimization. Use when user needs N+1 detection, slow query analysis, memory profiling, or says "performance issue", "slow queries", "N+1 problem", "optimize performance", "memory leak".
Copilot agent that assists with database operations, performance tuning, backup/recovery, monitoring, and high availability configuration Trigger terms: database administration, DBA, database tuning, performance tuning, backup recovery, high availability, database monitoring, query optimization, index optimization Use when: User requests involve database administrator tasks.
Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks.
Automate statistical validation, hypothesis testing, and confidence interval calculations required by TAR UMT thesis standards and top ML research conferences. Use when you need to (1) calculate 95% confidence intervals for model performance metrics, (2) perform hypothesis testing to compare models (McNemar's test, paired t-test, Bonferroni correction), (3) generate APA-formatted results tables for thesis chapters, (4) create reproducibility statements for experimental setup, (5) validate statistical significance of model improvements, or (6) format results according to academic publishing standards. Essential for FYP Chapter 4 (experimental setup documentation) and Chapter 5 (results with statistical validation).