Agent skill · research science · aiskillstore
ai-research-reproduction
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 training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.
Why this skill is useful
Adds a comprehensive workflow for reproducible deep learning experiments, including specific commands and structured outputs that are not commonly found in public documentation.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
ai-research-reproduction Purpose Use this as the Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. The installed slug remains ai-research-reproduction for compatibility. The skill guides the agent toward a minimal trustworthy run with auditable evidence; it should not micromanage implementation details that the model can infer from the repository. Reproduction is not "make it run by changing anything"; it means faithfully reading the README, environment, weights, datasets, and documented commands, then recording results and deviations. Start from the shared operating principles in ../../references/agent-operating-principles.md, then load ../../references/research-rigor-principles.md and ../../references/deep-learning-experiment-principles.md when scientific meaning, comparability, or experiment details are at stake. Fit Use this skill when all are true: The target is an AI code repository with a README, scripts, configs, or documented commands. The request spans multiple trusted phases such as intake, setup, execution, training verification, analysis, paper-gap resolution, and reporting. The desired result is a small reproducible target, not broad experimentation. Do not use this skill for paper summaries, generic environment setup, isolated repo scanning, standalone command execution, open-ended research design, or explicit candidate-only exploration. Trusted Target Selection Choose the smallest target that can honestly demonstrate repository-grounded reproduction: 1. documented inference 2. documented evaluation 3. documented training startup or partial verification 4. full training only after explicit user confirmation Treat README guidance as the primary reproduction intent. Use repository files to clarify the README, not to silently replace it. When the README and paper conflict, record the conflict and use paper-context-resolver only for the narrow reproduction-critical gap. Workflow 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-research-reproduction