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Search Experiment , found 36 results

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Claim-Driven Experiment Planner

experiment-plan

wanshuiyin/Auto-claude-code-research-in-sleep

Transforms a refined research proposal or method idea into a concise, claim-backed experiment roadmap, covering ablations, evaluation protocols, compute budgets, and execution order so papers can defend the problem, novelty, and any LLM/VLM/Diffusion/RL contribution.

Monitor Experiment Results

monitor-experiment

wanshuiyin/Auto-claude-code-research-in-sleep

Track running experiments by inspecting remote screen sessions, gathering logs, parsing JSON outputs, and optionally pulling WandB metrics before summarizing baselines, deltas, and costs for Vast.ai or Modal runs.

Autonomous Research Pipeline

research-pipeline

wanshuiyin/Auto-claude-code-research-in-sleep

Orchestrates idea discovery, implementation, scaling experiments, and autonomous review loops into a single pipeline that builds and iterates toward submission-ready research.

Critical Research Review Workflow

research-review

wanshuiyin/Auto-claude-code-research-in-sleep

Provides a Codex-powered workflow that orchestrates multi-round, high-reasoning GPT research reviews, capturing context, criticisms, follow-ups, experiments, and documentation.

Run Experiment Deployment

wanshuiyin/Auto-claude-code-research-in-sleep

Deploys and runs ML experiments on local, remote, Vast.ai, or Modal GPUs by detecting the target environment, syncing code, ensuring W&B logging when needed, and launching training jobs safely.

Reviewer Ablation Planner

ablation-planner

wanshuiyin/Auto-claude-code-research-in-sleep

Guides ML teams through reviewer-style ablation planning, covering context gathering, structured experiment design, feasibility checks, and compute-aware sequencing before implementation.

Experiment Bridge Workflow

experiment-bridge

wanshuiyin/Auto-claude-code-research-in-sleep

Bridges idea discovery and auto-review by reading an experiment plan, implementing and reviewing code, deploying on GPU, and collecting initial results so experiments are ready for the next review loop.

Vast.ai GPU Rent

wanshuiyin/Auto-claude-code-research-in-sleep

Automates renting and managing vast.ai GPU instances by analyzing task requirements, selecting cost-optimized offers, and handling lifecycle steps (provision, run, destroy) so users can run experiments on demand without owning hardware.

Arize Evaluator Workflow

arize-evaluator

github/awesome-copilot

Defines and runs LLM-as-judge evaluators on Arize via ax commands, handling evaluator creation, continuous monitoring, column mapping, trigger runs, and evaluation spans or experiments with configurable templates, models, and query filters.

Phoenix CLI Debugging

github/awesome-copilot

Phoenix CLI streamlines debugging Phoenix-based LLM observability: it fetches traces, spans, sessions, datasets, experiments, prompts, and GraphQL data while piping JSON to tools like jq so you can triage errors, inspect performance, and verify deployments quickly.

Scientific Critical Thinking

scientific-critical-thinking

K-Dense-AI/scientific-agent-skills

Assess research claims and experimental rigor by evaluating methodology, statistical validity, evidence quality, and biases using GRADE and Cochrane ROB frameworks, plus supporting schematics for complex concepts.

Stable Baselines3: RL Algorithm Toolkit

stable-baselines3

K-Dense-AI/scientific-agent-skills

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

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