aiming-lab

aiming-lab/AutoResearchClaw

Agent

Statistical result synthesis agent. Writes the final research narrative, combining formulation, proposed method, theory, experiments, comparisons, and limitations into a coherent report.

aiming-lab/AutoResearchClaw

Agent

Statistical theory analysis agent. Derives or sketches theoretical properties of the proposed methods, including identifiability, bias, variance, consistency, asymptotic behavior, coverage, error bounds, robustness, and limitations under stated assumptions.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigensolver (VQE) for chemistry, matrix-product-state circuits, and noise model integration. Use when writing Python code that imports qiskit…

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

aiming-lab/AutoResearchClaw

Skill Claude CodeCodex

Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

aiming-lab/SimpleMem

Skill Claude CodeCodex

Store and retrieve conversation memories across sessions. Use when asked to 'remember this', 'save conversation', 'add to memory', 'what did we discuss about...', 'query memories', or 'import chat history'. Also use proactively to preserve important dialogue context and decisions.

aiming-lab/MetaClaw

Skill Claude CodeCodex

Use this skill when delegating a subtask to a sub-agent, spawning a parallel worker, or handing off work across sessions. Write a self-contained task description so the receiving agent needs no prior context.

aiming-lab/MetaClaw

Skill Claude CodeCodex

Use this skill when writing messages in async channels (Slack, GitHub issues, email threads) where the reader may not have context and cannot ask follow-up questions immediately.