Skills

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Experiment Tracking

Skills tagged with #Experiment Tracking

@fmind

MLOps Automation

Guide to refine MLOps projects with task automation, containerization, CI/CD pipelines, and robust experiment tracking.

fmind/mlops-python-package+5 more
18d ago
1.4K0
@Agent-Analytics
MCP

Agent Analytics

Analytics your AI agent can actually use. Track, experiment, and optimize via MCP.

mcpgithubai
Agent-Analytics/agent-analytics-mcp
19d ago
0
@Affitor

ab-test-generator

Generate A/B test variants for affiliate content. Triggers on: "create A/B test", "test my headline", "optimize my CTA", "generate variants", "split test ideas", "improve click-through rate", "test my landing page copy", "headline alternatives", "CTA variations", "which version is better", "optimize conversions", "test my email subject line", "compare approaches".

affiliate-marketinganalyticsoptimizationtrackingab-testingexperiments
Affitor/affiliate-skills+31 more
18d ago
2320
@scispot-repo
MCP

Mcp

Turn any LLM into your lab assistant: search samples, track experiments, analyze data with AI.

mcpaisearchllm
scispot-repo/scispot-mcp-server
19d ago
0
@K-Dense-AI

adaptyv

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

K-Dense-AI/claude-scientific-skills+153 more
18d ago
15.6K0
@allenai

monitor-experiment

Monitor Beaker experiments until completion. Use when the user asks to monitor, check, or track a Beaker experiment.

allenai/open-instruct+1 more
18d ago
3.6K0
@chrisvoncsefalvay

a* (autostar)

A generalised autonomous optimisation loop — soft RLVR for the masses. The user defines a goal; the system runs structured experiments, evaluates progress across independent tracks, reflects at strategic checkpoints, and learns from every attempt — including learning how to learn better the next

chrisvoncsefalvay/autostar+1 more
18d ago
80