llm-ai
34
wispr-flow
Analyze Wispr Flow voice dictation data. Stats, search, export, visualizations. Use when user says "dictation history", "word counts", "voice analytics", "how much did I dictate", "search my dictation".
ArtemXTech
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
llm-ai
34
jimeng-mcp-skill
使用jimeng-mcp-server进行AI图像和视频生成。当用户请求从文本生成图像、合成多张图片、从文本描述创建视频或为静态图像添加动画时使用此技能。支持四大核心能力：文生图、图像合成、文生视频、图生视频。需要jimeng-mcp-server在本地运行或通过SSE/HTTP访问。
wwwzhouhui
data-ai
llm-ai
34
self-learning-skills
Memory sidecar for agent work: recall before tasks, record learnings after tasks, review recommendations, optional backport bundles.
scottfalconer
data-ai
llm-ai
34
siliconflow-api-skills
硅基流动（SiliconFlow）云服务平台文档。用于大语言模型 API 调用、图片生成、向量模型、在 Claude Code 中使用硅基流动、Chat Completions API、Stream 模式等。
wwwzhouhui
data-ai
llm-ai
33
anthropic-expert
Expert on Anthropic Claude API, models, prompt engineering, function calling, vision, and best practices. Triggers on anthropic, claude, api, prompt, function calling, vision, messages api, embeddings
raintree-technology
data-ai
llm-ai
33
ralph
Autonomous feature development - setup and execution. Triggers on: ralph, set up ralph, run ralph, run the loop, implement tasks. Two phases: (1) Setup - chat through feature, create tasks with dependencies (2) Loop - pick ready tasks, implement, commit, repeat until done.
ampcode
data-ai
llm-ai
32
pine-visualizer
Breaks down trading ideas into component parts for systematic Pine Script implementation. Use when analyzing trading concepts, decomposing strategies, planning indicator features, or extracting ideas from YouTube videos. Triggers on conceptual questions, "how would I build", YouTube URLs, or video analysis requests.
TradersPost
data-ai
llm-ai
32
shorts-script-personality
Generates hyper-optimized YouTube Shorts/Instagram Reels scripts with personality-specific styles while enforcing strict anti-AI-slop writing rules
outscal
data-ai
llm-ai
32
script-writer-personality
Generates educational video scripts with personality-specific styles (GMTK, Fireship, Chilli) while enforcing strict anti-AI-slop writing rules
outscal
data-ai
llm-ai
32
prompt-engineering
LLM prompt optimization and design patterns. Use for crafting effective prompts, chain-of-thought, and AI integration.
lovedragonball
data-ai
llm-ai
31
extract-transcripts
Extract readable transcripts from Claude Code and Codex CLI session JSONL files
0xBigBoss
data-ai
llm-ai
31
mteb-retrieve
This skill provides guidance for semantic similarity retrieval tasks using embedding models (e.g., MTEB benchmarks, document ranking). It should be used when computing embeddings for documents/queries, ranking documents by similarity, or identifying top-k similar items. Covers data preprocessing, model selection, similarity computation, and result verification.
letta-ai
data-ai
llm-ai
31
learning-sdk-integration
Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK
letta-ai
data-ai
llm-ai
31
path-tracing
Guide for reverse-engineering and recreating programmatically-generated ray-traced images. This skill should be used when tasks involve analyzing a target image to determine rendering parameters, implementing path tracing or ray tracing algorithms, matching scene geometry and lighting, or achieving high similarity scores between generated and target images.
letta-ai
data-ai
llm-ai
31
letta-fleet-management
Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, and folders.
letta-ai
data-ai
llm-ai
31
langchain-orchestration
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
manutej
data-ai
llm-ai
31
model-configuration
SDK/API patterns for configuring LLM models on Letta agents. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings (reasoning, extended thinking), or setting up custom endpoints.
letta-ai
data-ai
llm-ai
31
letta-development-guide
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
letta-ai
data-ai
llm-ai
31
claude-sdk-integration-patterns
Expert integration patterns for Claude API and TypeScript SDK covering Messages API, streaming responses, tool use, error handling, token optimization, and production-ready implementations for building AI-powered applications
manutej
data-ai
llm-ai
31
count-dataset-tokens
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.
letta-ai
data-ai
llm-ai
31
sam-cell-seg
Guidance for SAM-based cell segmentation and mask conversion tasks involving MobileSAM, mask-to-polygon conversion, CSV processing, and command-line interface design. This skill applies when working with Segment Anything Model (SAM) for biological image segmentation, converting binary masks to polygon coordinates, processing microscopy data, or building CLI tools that interface with deep learning models. (project)
letta-ai
data-ai
llm-ai
31
llm-inference-batching-scheduler
Guidance for optimizing LLM inference request batching and scheduling problems. This skill applies when designing batch schedulers that minimize cost while meeting latency and padding constraints, involving trade-offs between batch count, shape selection, and padding ratios. Use when the task involves grouping requests by sequence lengths, managing shape compilation costs, or optimizing multi-objective scheduling with hard constraints.
letta-ai
data-ai