Loading...
Loading...
Found 1,655 Skills
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.
Use this skill to build, run, deploy, evaluate, and troubleshoot Go agents with Google's Agent Development Kit (`google.golang.org/adk`), including llmagent config, tools/integrations, callbacks/plugins, sessions/state/memory, workflows, streaming, MCP/A2A, and runtime/deployment patterns.
Verifies implementation against specifications by checking requirement fulfillment, task completion, and contract implementation. Generates a fulfillment report with coverage metrics. Always run after /speckit.implement completes.
Bridge web search capability for LLM workflows. Use when user asks for latest info, external facts, or source links and the active model/toolchain lacks direct search ability.
💰 Save Token | Token 节省器 TRIGGERS: Use when token cost is high, conversation is long, files read multiple times, or before complex tasks. Guiding skill that helps agents identify and avoid sending duplicate context to LLM APIs. Teaches agents to recognize repeated content and summarize instead of re-sending. 触发条件：Token 成本高、对话长、文件多次读取、复杂任务前。 指导 Agent 识别重复内容，避免重复发送，从而节省 Token。
Adds Wasp knowledge, LLM-friendly documentation fetching instructions, and best practices to your project's CLAUDE.md or AGENTS.md file
Prompt design patterns for LLMs including few-shot, chain-of-thought, structured output, and injection defense. Use when crafting prompts, optimizing LLM outputs, or building prompt-based features.
Analyze text content using both traditional NLP and LLM-enhanced methods. Extract sentiment, topics, keywords, and insights from various content types including social media posts, articles, reviews, and video content. Use when working with text analysis, sentiment detection, topic modeling, or content optimization.
Autonomous LLM training optimization with GPU support. Runs 5-minute training experiments, measures val_bpb, keeps improvements or reverts — repeat forever. Use this skill when the user asks to "train a model autonomously", "optimize LLM training", "run ML experiments", "autoresearch with GPU", "optimize val_bpb", "autonomous ML training", "LLM pretraining loop", "setup ML autoresearch", "GPU training experiments", "pretrain from scratch", "speed up training", "lower my loss", "GPU optimization", "CUDA training", or mentions "train.py", "prepare.py", "bits per byte", "val_bpb", "NVIDIA GPU training", "RTX training", "H100 training", "autonomous model training", "consumer GPU training", "low VRAM training". Always use this skill when the user wants to autonomously optimize any ML training metric.
Orchestrates durable multi-step workflow pipelines on the iii engine. Use when building order fulfillment, data pipelines, task orchestration, or any sequential process requiring retries, backoff, step tracking, scheduled cleanup, or dead letter queue (DLQ) handling.
Vine review program strategy — enrollment, product selection, timing, review quality maximization
Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop