Skill UI

Helps product and program teams plan, assess, and steer enterprise software, SaaS, and digital transformation portfolios using quantitative risk analytics, resource optimization, health scoring, and executive-level reporting.

This utility integrates status updates and notifications with Feishu/Lark. It is designed as an internal, zero-impact tool that other complex skills (e.g., experiment runners, review loops) call to report key events like test completion, scoring results, or reaching a checkpoint. It supports both 'push' (fire-and-forget status updates) and 'interactive' (bidirectional approval/rejection) modes, ensuring seamless workflow visibility and robust operation even if configuration is missing.

Captures coding session learnings by extracting corrections, successes, and anti-patterns with confidence scoring so teams can track, reinforce, and reuse technical knowledge across projects.

Applies two-axis scoring, mandatory devil’s advocate reasoning, and persistent history to keep post-task self-reviews honest, resistant to inflation, and suitable for Claude Code sessions.

Automatically scans any codebase, detects its stack, and generates a persistent project.faf AI context plus readiness scoring so every AI tool can instantly understand and contribute to your project.

This guide details advanced techniques for searching cricket matches by leveraging precise numerical scoring data. Instead of broad terms, users should utilize specific details—such as team totals (e.g., 168/9), runs off balls, wickets taken, or over details—combined with the match type and year to create highly targeted search queries. This method significantly improves search accuracy and efficiency when researching sports statistics.

Utilize the ScholarEval framework for comprehensive and structured evaluation of academic works, including research papers, proposals, and literature reviews. Assess quality across critical dimensions like problem formulation, methodology rigor, data analysis, and scholarly writing. Provides both quantitative scoring and actionable feedback to enhance publication readiness.

This skill analyzes your opencode prompt history to identify repeating multi-step workflows, recurring unmet needs, and candidates for new reusable skills. By scoring patterns based on frequency, complexity, and tool usage, it helps users save time and avoid repeating complex manual tasks.