llm-ai
11
ai-governance
AI governance and responsible AI planning including EU AI Act classification, NIST AI RMF, and AI ethics frameworks
melodic-software
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
llm-ai
11
custom-agent-design
Design custom agents from scratch using Claude Agent SDK patterns. Use when building domain-specific agents, designing agents with full SDK control, or creating specialized agents with custom tools and prompts.
melodic-software
data-ai
llm-ai
11
expertise-file-design
Design YAML expertise file structures for agent experts. Use when creating mental models for domain-specific agents, defining expertise schema, or structuring knowledge for Act-Learn-Reuse workflows.
melodic-software
data-ai
llm-ai
11
context-assembler
Assembles relevant context for agent spawns with prioritized ranking. Ranks packages by relevance, enforces token budgets with graduated zones, captures error patterns for learning, and supports configurable per-agent retrieval limits.
mehdic
data-ai
llm-ai
11
brainstorm-diverge-converge
Use when you need to generate many creative options before systematically narrowing to the best choices. Invoke when exploring product ideas, solving open-ended problems, generating strategic alternatives, developing research questions, designing experiments, or when you need both breadth (many ideas) and rigor (principled selection). Use when user mentions brainstorming, ideation, divergent thinking, generating options, or evaluating alternatives.
lyndonkl
data-ai
llm-ai
11
tool-design
Create custom tools using the @tool decorator for domain-specific agents. Use when building agent-specific tools, implementing MCP servers, or creating in-memory tools with the Agent SDK.
melodic-software
data-ai
llm-ai
11
self-improve-prompt-design
Write self-improve prompts that sync expertise files with codebase reality. Use when creating maintenance workflows for agent experts, designing validation logic, or implementing the LEARN step of Act-Learn-Reuse.
melodic-software
data-ai
machine-learning
11
ml-inference-optimization
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
melodic-software
data-ai
machine-learning
11
llm-serving-patterns
LLM inference infrastructure, serving frameworks (vLLM, TGI, TensorRT-LLM), quantization techniques, batching strategies, and streaming response patterns. Use when designing LLM serving infrastructure, optimizing inference latency, or scaling LLM deployments.
melodic-software
data-ai
llm-ai
11
gemini-token-optimization
Optimize token usage when delegating to Gemini CLI. Covers token caching, batch queries, model selection (Flash vs Pro), and cost tracking. Use when planning bulk Gemini operations.
melodic-software
data-ai
llm-ai
11
foundation-models
On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.
rshankras
data-ai
llm-ai
11
workflow-router
Determine next agent based on state machine rules. Use AFTER receiving any BAZINGA agent response to decide what to do next.
mehdic
data-ai
llm-ai
11
prompt-builder
Build complete agent prompts deterministically via Python script. Use BEFORE spawning any BAZINGA agent (Developer, QA, Tech Lead, PM, etc.).
mehdic
data-ai
llm-ai
11
piter-setup
Set up PITER framework elements for AFK agent systems. Use when configuring prompt input sources, triggers, environments, and review processes for autonomous agent workflows.
melodic-software
data-ai
machine-learning
11
equivariant-architecture-designer
Use when you have validated symmetry groups and need to design neural network architecture that respects those symmetries. Invoke when user mentions equivariant layers, G-CNN, e3nn, steerable networks, building symmetry into model, or needs architecture recommendations for specific symmetry groups. Provides architecture patterns and implementation guidance.
lyndonkl
data-ai
llm-ai
11
ethics-review
AI and technology ethics review including ethical impact assessment, stakeholder analysis, and responsible innovation frameworks
melodic-software
data-ai
llm-ai
11
rag-architecture
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
melodic-software
data-ai
machine-learning
11
model-selection
Choose appropriate model for custom agent tasks. Use when selecting between Haiku, Sonnet, and Opus for agents, optimizing cost vs quality tradeoffs, or matching model capability to task complexity.
melodic-software
data-ai
llm-ai
11
agent-lifecycle-management
Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.
melodic-software
data-ai
machine-learning
11
c4-documentation
C4 model architecture visualization and documentation
melodic-software
data-ai
machine-learning
11
evaluate-model
Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.
mvillmow
data-ai