Results for “experiment-lifecycle”

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

当意识到自己处于长期迷茫、缺乏动力阶段，需要系统性方法走出困境时

Guide product managers through a complete discovery cycle from problem hypothesis to validated solution using problem framing, customer interviews, synthesis, and experimentation.

Turn assumptions, funnel opportunities, and product questions into a prioritised, feasibility-checked experiment backlog. Filters by traffic reality, metric latency, and method feasibility — not just ICE/RICE scoring. Maintains a living portfolio with status (idea → designed → running → readout → archived). Load when the user says "what should we test next", "build an experiment backlog", "prioritise our tests", "where should we experiment", "what's worth testing", or when the experimentation orchestrator routes here.

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

Full dependency lifecycle composite — audit known vulnerabilities, triage by severity and breaking-change risk, upgrade targeted packages, verify tests pass, ship PR. Use monthly, before releases, or when dependency alerts are stacking. Never do a blanket upgrade; always triage first.

Orchestrator for the experimentation skill suite — turn assumptions and product questions into rigorous, well-instrumented experiments and decision-grade readouts. Routes through backlog → spec → runbook → readout based on user need and existing artefacts. Platform-agnostic with PostHog as the primary binding. Load when the user asks to design an experiment, A/B test something, set up an experiment, run a holdout, test a hypothesis, decide what to test next, read out experiment results, analyse a test, or says "should we A/B test this", "experiment on the landing page", "is this lift real", "ship or kill this test", "what should we test next", "build an experiment backlog", "test the pricing page", "validate this with an experiment".

Orchestrate a full theory-first idea discovery pipeline. Use when the user wants the analysis counterpart of idea-discovery: literature context, proof-oriented idea generation, novelty checking, DeepSeek critique, and an analysis plan instead of an experiment-first workflow.

当感到生活迷茫、停滞不前，需要理解当前所处阶段并规划下一步行动时

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.

查看所有运行中实验的状态，可选自动收集已完成实验并推进流水线

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment.

Use when a researcher asks where to start, which project skill fits the current stage of a study, or in what order literature scoping, source access, vault upkeep, drafting, citation verification, disclosure, and review response should run.

Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.

Behavior-driven development: Discovery → Formulation → Automation. Use when the user wants to build features or fix bugs via Gherkin scenarios and the BDD lifecycle, or mentions BDD/Gherkin/Executable Specifications. Delegates test implementation to /mattpocock:tdd.

Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.

Design lean validation experiments — hypothesis, method, success criteria, sample size, timeline, and decision rules

Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.

Design low-effort experiments to test product assumptions for an existing product, including prototypes, A/B tests, spikes, and other validation methods.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

UAT Sign-off

Runs focused experiments to validate feasibility of an idea, saving artifacts to .planning/spikes/ and supporting both idea and frontier modes.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:loop or asks to run an autoresearch experiment continuously on a schedule.

Autonomously runs iterative experiment loops to optimize code against a measurable metric. Use when the user wants to improve execution time, memory usage, test pass rate, or any numeric performance goal across repeated experiments — NOT for one-shot bug fixes or simple code review.

Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop). Goes from a broad research direction all the way to a submission-ready paper. Use when user says "全流程", "full pipeline", "从找idea到投稿", "end-to-end research", or wants the complete autonomous research lifecycle.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

Guides the end-to-end cyber threat intelligence lifecycle from planning and direction through collection, processing, analysis, dissemination, and feedback to establish or mature a CTI program.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Design product experiments around hypotheses, audience, metrics, guardrails, and decisions.

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.