Results for “experiment”

Migrate Android Jetpack Compose UI components to use the experimental Styles API for centralized theming and state-based styling.

Design rigorous A/B tests with clear hypotheses, controlled variants, appropriate metrics, and sample size calculations.

Coordinates sprint planning, delivery tracking, experiment management, knowledge management, and changelog governance across project management workflows.

Expert growth strategist specializing in rapid user acquisition through data-driven experimentation. Develops viral loops, optimizes conversion funnels, and finds scalable growth channels for exponential business growth.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Run file-based C# apps with the .NET CLI for quick experiments, prototypes, and small utilities without creating a project.

Manage Arize datasets and examples using the ax CLI: create, list, get, export, and append datasets for evaluation and experimentation.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Compiles research inputs—PDFs, code, logs, notes—into structured Agent-Native Research Artifacts with cognitive and physical layers.

Manages a persistent hypothesis tree in markdown, tracking falsifiable claims with linked evidence, timestamps, and next experiments for multi-step research investigations.

Design, run, and learn from safe chaos engineering experiments with structured plans, blast radius calculation, and postmortem generation.

Train and experiment with a minimal GPT implementation in ~300 lines of PyTorch, from character-level Shakespeare to GPT-2 scale.

Run interpretability experiments on neural network internals using nnsight, with optional NDIF remote execution for massive models.

Use to lock the single narrative arc before drafting — converts a set of correct results into one Cell-shaped story that runs hypothesis → mechanism → significance, not a list of experiments.

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

Audit paid-ad creative assets for quality, platform compliance, fatigue, and performance, producing prioritized refresh or experiment briefs.

Design, simulate, and run quantum circuits on Google Quantum AI and partner hardware using Cirq, including noise modeling and characterization experiments.

Generate a structured paper outline from review conclusions and experiment results. Use when user says "写大纲", "paper outline", "plan the paper", "论文规划", or wants to create a paper plan before writing.

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

Design pricing strategies grounded in value delivery, competitive positioning, and willingness to pay. Recommends pricing models, tiers, and experiments.

Builds reliable browser automation skills through iterative experimentation, running an inner agent to browse sites and improving navigation instructions until tasks pass consistently.

Design, simulate, and run quantum circuits on Google Quantum AI hardware and other providers using Cirq, including noise modeling and characterization experiments.

Interrogates research plans before evals or published claims, forcing explicit null hypotheses, confound checks, and data verification across four review waves.

Injects intentional failures to test system resilience using Litmus and Chaos Monkey, with a prerequisite to validate detection before running experiments.

Guide through a structured data analysis workflow: define the question, validate data quality, select the appropriate analytical method, and produce decision-ready findings with caveats.

Automates scientific research workflows from data analysis to publication, orchestrating multiple agents for hypothesis generation, methodology development, computational experiments, and LaTeX paper writing.

Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop.

Use this skill for feature flags, gradual rollout, kill switches, metrics, experiment gates, rollback. Trigger when the task involves programming work related to Feature Flag Rollout, implementation, audits, debugging, strategy, or validation.

Use this skill for growth funnels, landing pages, analytics, experiments, activation and conversion. Trigger when the task involves agent profile work related to Growth Engineer Agent, implementation, audits, debugging, strategy, or validation.

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

Submit protein sequences to the Adaptyv Bio Foundry for experimental characterization (binding, thermostability, expression, fluorescence) and retrieve results using the REST API or Python SDK.