ai-paper-reproduction
ai-paper-reproduction
Use when
- The user wants the agent to reproduce an AI paper repository.
- The target is a code repository with a README, scripts, configs, or documented commands.
- The goal is a minimal trustworthy run, not unlimited experimentation.
- The user needs standardized outputs that another human or model can audit quickly.
- The task spans more than one stage, such as intake plus setup, or setup plus execution plus reporting.
Do not use when
- The task is a general literature review or paper summary.
- The task is to design a new model, benchmark suite, or training pipeline from scratch.
- The repository is not centered on AI or does not expose a documented reproduction path.
- The user primarily wants a deep code refactor rather than README-first reproduction.
- The user is explicitly asking for only one narrow phase that a sub-skill already covers cleanly.
- The user is explicitly authorizing exploratory branch-only experimentation instead of trusted reproduction.
More from lllllllama/ai-paper-reproduction-skill
paper-context-resolver
Optional narrow helper skill for README-first AI repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
99.0Kenv-and-assets-bootstrap
Environment and assets sub-skill for README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
98.9Krepo-intake-and-plan
Narrow helper skill for README-first AI repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
98.9Kminimal-run-and-audit
Trusted-lane execution and reporting skill for README-first AI repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, or end-to-end orchestration by itself.
98.9Kai-research-reproduction
Main orchestrator for README-first AI repo reproduction. Use when the user wants an end-to-end, minimal-trustworthy reproduction 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, or broad research assistance outside repository-grounded reproduction.
175explore-code
Explore-lane code adaptation skill for deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together low-risk migration ideas with summary-only records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, or default repository analysis.
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