Run a verified multi-agent goal with Bernstein. Use when a task is too large for a single agent session: Bernstein decomposes the goal into tasks, spawns CLI coding agents in parallel git worktrees, verifies their output, and merges results. Also use to check run status, costs, and to verify a finished run against its…
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted…
Scan a repository that already has Claude Code configuration (hooks, skills, CLAUDE.md, settings, MCP servers) and package it into a valid MCS techpack.yaml for distribution via mcs. Use this skill whenever the user wants to create a tech pack from an existing repo, convert a Claude Code setup into a shareable MCS…
Plan and run a protein design campaign within a user-approved stage and resource boundary. Branches: de novo binder design (epitope choice, generation, co-folding ensemble ranking, ranked order sheet); structure and complex prediction with calibrated confidence; protein engineering (stability, enzyme, interface).…
ArXivist is a multi-agent orchestration skill that converts scientific papers (arXiv PDFs or URLs) into fully executable, reproducible Git repositories through a structured pipeline. Use this skill whenever a user mentions: converting a paper to code, reproducing a research paper, implementing an arXiv paper, turning…
CRUCIBLE: a Claude-only adversarial expert workshop that stress-tests a scientific paper and then, opt-in, rebuilds it. ACT I (TRIBUNAL): from the paper PDF (plus a few public cited works it fetches), a topic-adapted fleet of expert referee subagents (every contested claim argued from at least two COMPETING…
A set of rules for running reproducible bioinformatics analyses, which study biological data with software. It uses small self-contained scripts, recorded inputs and outputs, environment details, and version history so results can be repeated.
Conduct AI/ML research as a science - first-principles reasoning, mechanism-level hypotheses, falsifiable predictions, discriminative experiments, honest evaluation, and claims that survive hostile review. Use when starting or steering an AI/ML research project, deciding what to investigate next, choosing datasets…
Use rrdoctor as the deterministic, offline definition of done for preparing a research repository for Artifact Evaluation or public release.
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