Results for “replication”

Activate when users mention Stata commands, .do files, regressions, econometrics, stored results, graphs, dataset inspection, replication, or Stata errors. Route the task through mcp-stata tools and the specialized research skills instead of treating it as plain text coding.

Run any Replicate model (image gen, audio, video) by version ID

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

Reproduce a bug as a failing regression test, fix until green, review, and commit via a delegated pipeline.

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

Discover, compare, audit, repair, adapt, craft, run, debrief, and publish repeatable AI-agent loops for engineering workflows.

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Computes the Recall metric using torchmetrics, including configuration for binary, multiclass, and multilabel tasks.

`analysis-agent`/`task-agent`/`review-agent`: use when repeated failure needs a new hypothesis or proof path; skip an initial failure with verified cause and a different action.

Discovers, finds, audits, repairs, adapts, crafts, runs, debriefs, saves, and prepares repeatable AI-agent loops for publication, treating loops as bounded feedback systems.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

Alpaca: A Strong, Replicable Instruction-Following Model

Use for writing, reviewing, debugging, testing, or analyzing Python SimPy discrete-event simulations. Trigger on Environment, Event, Process, timeout, Resource, PriorityResource, PreemptiveResource, Container, Store, queues, interrupts, simulation clocks, replications, or SimPy monitoring. Do not use for asyncio services, wall-clock schedulers, continuous ODE solvers, or Monte Carlo code without an event-process model.

Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.