data-analysis
10.9K
hypothesis-formulation
Structured scientific hypothesis generation from observations. Use when formulating testable hypotheses, competing explanations, or experimental predictions.
aiming-lab
data-ai
sorting
stars
current ordering strategy
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data-analysis
10.9K
scientific-visualization
Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.
aiming-lab
data-ai
data-analysis
10.9K
meta-analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
aiming-lab
data-ai
data-engineering
10.9K
data-loading
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
aiming-lab
data-ai
llm-ai
10.9K
a-evolve
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.
aiming-lab
data-ai
machine-learning
10.9K
nlp-alignment
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
aiming-lab
data-ai
machine-learning
10.9K
nlp-pretraining
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
aiming-lab
data-ai
machine-learning
10.9K
rl-policy-optimization
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
aiming-lab
data-ai
machine-learning
10.9K
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
aiming-lab
data-ai
data-analysis
10.4K
campaign-analytics
Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.
alirezarezvani
data-ai
data-engineering
10.4K
database-designer
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
alirezarezvani
data-ai
data-engineering
10.4K
senior-data-engineer
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
alirezarezvani
data-ai
data-engineering
10.4K
snowflake-development
Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.
alirezarezvani
data-ai
llm-ai
10.4K
agent-protocol
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
alirezarezvani
data-ai
llm-ai
10.4K
behuman
Use when the user wants more human-like AI responses — less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', 'less AI', 'talk like a person', 'mirror mode', 'stop being so AI', or when conversations are emotionally charged (grief, job loss, relationship advice, fear). NOT for technical questions, code generation, or factual lookups.
alirezarezvani
data-ai
llm-ai
10.4K
llm-cost-optimizer
Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt caching, or build cost observability for AI features. Triggers: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching'. NOT for RAG pipeline design (use rag-architect). NOT for prompt writing quality (use senior-prompt-engineer).
alirezarezvani
data-ai
llm-ai
10.4K
prompt-governance
Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production AI features. Triggers: 'manage prompts in production', 'prompt versioning', 'prompt regression', 'prompt A/B test', 'prompt registry', 'eval pipeline'. NOT for writing or improving individual prompts (use senior-prompt-engineer). NOT for RAG pipeline design (use rag-architect). NOT for LLM cost reduction (use llm-cost-optimizer).
alirezarezvani
data-ai