llm-ai
841
prompt-analyzer
ๆ็คบ่ฏๅๆไธๆดๅฏ - ๆฅ็Prompt่ฏฆๆ ใๅฏนๆฏๅทฎๅผใๆจ่็ธไผผๆ็คบ่ฏใๅ ็ด ๅบ็ป่ฎก
huangserva
data-ai
sorting
์คํ
current ordering strategy
query
all entries
refine the visible subset
llm-ai
841
prompt-master
ๆ็คบ่ฏไธปๆง - ๆบ่ฝ้ๆฉๅ้็้ขๅskillๅนถ็ๆๆ็คบ่ฏ๏ผๆฏๆ่ชๅจ้ขๅๅ็ฑปๅ่ฐๅบฆ
huangserva
data-ai
llm-ai
841
domain-classifier
AI้ขๅๅ็ฑปๅจ - ๆบ่ฝๅๆๆ็คบ่ฏๅ ๅฎน๏ผๅ็กฎๅคๆญๆๅฑ้ขๅ๏ผไบบๅ/่บๆฏ/่ฎพ่ฎก/ไบงๅ/่ง้ข๏ผ
huangserva
data-ai
llm-ai
841
universal-learner
้็จๅญฆไน ๅจ - ไปไปปไฝ้ขๅ็Promptไธญ่ชๅจๆๅๅฏๅค็จๅ ็ด ๏ผๆ็ปญๅญฆไน ๅ็งฏ็ดฏ็ฅ่ฏ
huangserva
data-ai
llm-ai
841
prompt-extractor
่ชๅจๅๆๅAI็ป็ปๆ็คบ่ฏ็ๆจกๅๅ็ปๆ๏ผไปๆตท้ๆ็คบ่ฏไธญๆ็ผๅฏๅค็จ็ๆจกๅ็ปไปถ
huangserva
data-ai
llm-ai
841
intelligent-prompt-generator
ๆบ่ฝๆ็คบ่ฏ็ๆๅจ v2.0 - ๆฏๆไบบๅ/่ทจdomain/่ฎพ่ฎกไธ็งๆจกๅผ๏ผ่ฏญไน็่งฃใๅธธ่ฏๆจ็ใไธ่ดๆงๆฃๆฅ
huangserva
data-ai
llm-ai
785
common-skills
Best practices for the Common utilities package in LlamaFarm. Covers HuggingFace Hub integration, GGUF model management, and shared utilities.
llama-farm
data-ai
llm-ai
785
python-skills
Shared Python best practices for LlamaFarm. Covers patterns, async, typing, testing, error handling, and security.
llama-farm
data-ai
llm-ai
719
ccs-delegation
Auto-activate CCS CLI delegation for deterministic tasks. Parses user input, auto-selects optimal profile (glm/kimi/custom) from ~/.ccs/config.json, enhances prompts with context, executes via `ccs {profile} -p "task"` or `ccs {profile}:continue`, and reports results. Triggers on "use ccs [task]" patterns, typo/test/refactor keywords. Excludes complex architecture, security-critical code, performance optimization, breaking changes.
kaitranntt
data-ai
llm-ai
711
voice-ai-development
Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.
sickn33
data-ai
llm-ai
711
langgraph
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
sickn33
data-ai
llm-ai
711
ai-agents-architect
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
sickn33
data-ai
llm-ai
711
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
sickn33
data-ai
llm-ai
711
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
sickn33
data-ai
llm-ai
711
agent-evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoringโwhere even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
sickn33
data-ai
llm-ai
711
voice-agents
Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STTโLLMโTTS, more control, easier to debug). Key insight: latency is the constraint. Hu
sickn33
data-ai
llm-ai
711
autonomous-agent-patterns
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.
sickn33
data-ai
llm-ai
711
prompt-caching
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
sickn33
data-ai
llm-ai
711
autonomous-agents
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
sickn33
data-ai
llm-ai
711
context-window-management
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
sickn33
data-ai
llm-ai
711
rag-engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
sickn33
data-ai
