Results for “experiment”

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling.

ML Pipeline Specialist IA — Expert en pipelines ML (feature stores, data versioning, experiment tracking, model registry)

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling.

Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.

Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", "outcome over output", "design studio method", "assumption mapping", or "lightweight research". Also trigger when reducing design documentation overhead, getting cross-functional teams to co-design, or running fast usability experiments. Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.

Run metric-driven iterative optimization loops -- define a measurable goal, run parallel experiments, measure each against hard gates or LLM-as-judge scores, keep improvements, and converge on the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation.

Angular forms: Signal Forms (experimental) and Reactive Forms. Trigger: When working with forms, validation, or form state in Angular.

Use when working from the local Evo 2 repository for DNA-sequence scoring, embeddings, generation, or phage-genome design experiments.

Submits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a.

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

Access the RCSB Protein Data Bank (PDB) for EXPERIMENTALLY determined 3D structures (X-ray, cryo-EM, NMR) of proteins and nucleic acids — searching by text, sequence, or structure similarity and downloading coordinates in PDB/mmCIF format with metadata. Use when retrieving a structure by PDB ID, running sequence or structure similarity searches, or obtaining experimental coordinates for structural biology and drug discovery; for AI-PREDICTED structures of proteins lacking experimental data prefer alterlab-alphafold-db, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

TL;DR:23 marketing playbooks (CRO, SEO, copy, analytics, experiments, pricing, launches, ads, social). Use to get checklists + copy/paste deliverables fast.

Marketing analytics and measurement infrastructure. Use when the user requests 'GA4', 'GTM', 'analytics', 'attribution', 'measurement', 'tracking setup', 'UTM', or 'experiment infrastructure'.

[BETA] Execute work with external delegate support. Same as ce-work but includes experimental Codex delegation mode for token-conserving code implementation.

Use when you need to characterize score distributions of a profile HMM on random sequences, such as calibration checks, benchmarking, or filter-behavior experiments.

Data pipeline specialist for ML experiments. Use when the user asks to talk to Gekko, requests the data engineer, or needs DataLoader optimization.

Run one bounded Plan, Implement, and fresh Validate experiment, then report and stop. Triggers: "run rpi", "feed this through the loop", "research-plan-implement".

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

Designs growth experiments, tests acquisition channels, builds viral loops, and analyzes metrics to optimize conversion funnels.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Generate publication-quality figures and tables from experiment results. Use when user says "画图", "作图", "generate figures", "paper figures", or needs plots for a paper.

Design statistically valid A/B tests with hypothesis frameworks, sample size calculations, and analysis checklists.

Guides setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard.

Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.

Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance using TensorBoard.

Provides 20 standard operating procedures for growth teams, covering A/B testing, analytics tracking, and related growth workflows.

Use when designing or rewriting a high-converting SaaS pricing page (structure, plan design, copywriting, SEO/AEO, FAQs, layout patterns, experiments). Includes checklists, templates, and common pitfalls.

When the user wants help with pricing decisions, packaging, or monetization strategy. Use when the user mentions 'pricing,' 'pricing model,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'pricing strategy,' 'pricing experiments,' 'price anchoring,' 'enterprise pricing,' 'GTM pricing,' 'price setting,' 'value-based pricing,' 'revenue optimization,' or 'pricing psychology.' This skill covers pricing research, tier structure, packaging strategy, discount frameworks, upsell mechanics, and pricing experiments.