Skill UI

Analyzes local git branches and worktrees, groups related prefixes, verifies merges or squash history, and safely prompts before deleting merged, superseded, or gone branches. Use when stale local branches and worktrees need cleanup without affecting remote.

HR-Pro offers compliance-first workflows for hiring, onboarding, PTO, performance, and employee relations, delivering templates, checklists, and playbooks tailored to professional HR partners needing structured guidance and verification prompts.

Orchestrates Kling AI batch video jobs with async Python to submit prompts in parallel, track progress, stay within rate limits, and retrieve results automatically.

Applies artistic styles and visual effects to Kling AI video generation, letting creators match brand aesthetics or explore experimental looks by describing target aesthetics, supplying reference images, and iterating prompts for refined stylized videos.

Generate high-quality videos from text prompts via Kling AI, tuning duration, resolution, and aspect ratio while monitoring progress and downloading results—ideal for creators or learners honing prompt engineering.

Track and improve production LLMs with Langfuse observability: trace API calls, manage prompts, score evaluations, integrate with LangChain/LlamaIndex/OpenAI, and keep dashboards on cost, latency, and quality.

Use Langfuse Core Workflow B to collect manual feedback, run automated evaluation functions, operate LLM-as-judge scoring, and compare prompts for AI quality scoring.

Monitor Langfuse telemetry to record LLM token usage, aggregate spend by model and time, alert on budget breaches, and steer requests toward cheaper models while caching responses and trimming prompts.

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

LlamaGuard is Meta’s 7–8B safety-specialized LLM that filters both prompts and responses by classifying six threat categories, enabling fast inference via vLLM/SageMaker and integration into NeMo Guardrails for end-to-end moderation.

This skill guides users through advanced prompt optimization techniques for Large Language Models (LLMs). It functions as an expert prompt engineer coach, applying methodologies like Constitutional AI and Chain-of-Thought reasoning. Use it to transform basic, raw instructions into highly effective, production-ready prompts, significantly improving accuracy, reducing hallucinations, and optimizing token usage for cost efficiency.

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.