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published by aitmpl

50 listings on this page, in order of arrival. Each one has its own page with README, repository facts and source links.

  1. agent-management ★ 30,576
    Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent",
  2. agent-messaging ★ 30,576
    Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "se
  3. docs-search ★ 30,576
    Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when the user asks
  4. graph-query ★ 30,576
    Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find caller
  5. memory-search ★ 30,576
    Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search me
  6. planning ★ 30,576
    Create and manage persistent markdown planning files for structured task execution. Use when the user asks to "create a plan", "track progre
  7. agent-evaluation ★ 30,576
    "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—whe
  8. agent-manager-skill ★ 30,576
    Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
  9. agent-memory-mcp ★ 30,576
    A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
  10. agent-memory-systems ★ 30,576
    "Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agen
  11. agent-tool-builder ★ 30,576
    "Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinat
  12. agents-autogpt ★ 30,576
    Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent au
  13. agents-crewai ★ 30,576
    Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on compl
  14. agents-langchain ★ 30,576
    Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+
  15. agents-llamaindex ★ 30,576
    Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features
  16. ai-agents-architect ★ 30,576
    "Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration
  17. autonomous-agent-patterns ★ 30,576
    "Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-lo
  18. autonomous-agents ★ 30,576
    "Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant hum
  19. behavioral-modes ★ 30,576
    AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
  20. claude-code-guide ★ 30,576
    Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techni
  21. computer-use-agents ★ 30,576
    "Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers An
  22. context-window-management ★ 30,576
    "Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window,
  23. context7-auto-research ★ 30,576
    Automatically fetch latest library/framework documentation for Claude Code via Context7 API
  24. conversation-memory ★ 30,576
    "Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, rem
  25. crewai ★ 30,576
    "Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and go
  26. data-engineer ★ 30,576
    Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and clo
  27. data-processing-nemo-curator ★ 30,576
    GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filterin
  28. data-processing-ray-data ★ 30,576
    Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train,
  29. data-scientist ★ 30,576
    Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling
  30. datadog-cli ★ 30,576
    Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or wo
  31. deep-research ★ 30,576
    "Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports."
  32. deep-research-notebooklm ★ 30,576
    "Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysi
  33. dispatching-parallel-agents ★ 30,576
    Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
  34. distributed-training-accelerate ★ 30,576
    Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Au
  35. distributed-training-deepspeed ★ 30,576
    Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse
  36. distributed-training-megatron-core ★ 30,576
    Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models
  37. distributed-training-pytorch-fsdp ★ 30,576
    Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
  38. distributed-training-pytorch-lightning ★ 30,576
    High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerpl
  39. distributed-training-ray-train ★ 30,576
    Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparam
  40. emerging-techniques-knowledge-distillation ★ 30,576
    Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained
  41. emerging-techniques-long-context ★ 30,576
    Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long docume
  42. emerging-techniques-model-merging ★ 30,576
    Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending
  43. emerging-techniques-model-pruning ★ 30,576
    Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining,
  44. emerging-techniques-moe-training ★ 30,576
    Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost red
  45. emerging-techniques-speculative-decoding ★ 30,576
    Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference
  46. evaluation-bigcode-evaluation-harness ★ 30,576
    Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code model
  47. evaluation-lm-evaluation-harness ★ 30,576
    Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparin
  48. evaluation-nemo-evaluator ★ 30,576
    Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needin
  49. fine-tuning-axolotl ★ 30,576
    Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
  50. fine-tuning-llama-factory ★ 30,576
    Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support