Repo Intake And Plan
Scan an ML research repository, extract README commands, and recommend the smallest trustworthy inference or evaluation reproduction target.
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill --skill repo-intake-and-plan| Installs | 140k |
|---|---|
| GitHub stars | ★ 412 |
| Security audit | 3 / 3 scanners passed |
| Last updated | May 27, 2026 |
| Repository | lllllllama/ai-paper-reproduction-skill ↗ |
Related skills
Paper Context ResolverResolve narrow paper-backed gaps (splits, preprocessing, eval protocol, checkpoints, runtime) when README and repo files are insufficient for faithful ML paper reproduction.140k412Minimal Run And AuditRun documented inference, evaluation, or smoke commands and normalize evidence into standardized repro_outputs with patch notes.140k412Env And Assets BootstrapBootstrap conda-first environments plus checkpoints, datasets, and cache paths before a documented reproduction run.140k412Analyze ProjectMap a deep learning repo read-only—training, inference, and eval entrypoints, configs, and suspicious patterns—before you let an agent patch model code.52.4k412Ai Research ReproductionOrchestrate README-first, minimal-trustworthy reproduction of a deep learning repository with auditable repro_outputs evidence.52.3k412Run TrainRun a already-chosen deep-learning training command with conservative startup, short-run, full kickoff, or resume checks while writing command, config, seed, logs, checkpoints, status, and metrics int52.3k412FAQ
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SKILL.md
display_name: Rigor Intake short_description: Rigor Intake helper for scanning a repo and recommending the smallest trustworthy reproduction target. default_prompt: Scan this repository, read the README and common project files, extract documented commands, classify inference evaluation and training paths, and recommend the smallest trustworthy reproduction target. # Repo Scan Rules ## Primary files Always check these first when present: - `README.md` - `README` - `requirements.txt` - `environment.yml` - `environment.yaml` - `pyproject.toml` - `setup.py` - `setup.cfg` - `Dockerfile` ## High-signal directories Inspect for command or configuration clues: - `configs/` - `config/` - `scripts/` - `tools/` - `examples/` - `notebooks/` - `checkpoints/` ## Extraction priorities 1. explicit README commands 2. setup instructions 3. documented inference or demo entrypoints 4. documented evaluation entrypoints 5. documented training entrypoints 6. config references and asset path hints ## Classification guidance - `inference`: demo, predict, generate, sample, infer, test-time forward use - `evaluation`: eval, validate, benchmark, score, reproduce metrics - `training`: train, finetune, pretrain, launch long-running experiments - `other`: install, download, preprocess, export, convert, utility ## Conservative behavior - prefer explicit README evidence over filename guesses - mark guessed classifications as inferred - record ambiguity instead of overcommitting #!/usr/bin/env python3 """Extract shell-like commands from README content and classify them.""" from __future__ import annotations import argparse import json import re from pathlib import Path from typing import Dict, List, Optional CODE_BLOCK_RE = re.compile(r"```(?P<lang>[^\n`]*)\n(?P<body>.*?)```", re.DOTALL | re.IGNORECASE) INLINE_CMD_RE = re.compile(r"^\s*(?:\$|>|PS> )\s*(.+)$") HEADING_RE = re.compile(r"^(?P<marks>#{1,6})\s+(?P<title>.+?)\s*$") COMMAND_PREFIXES = ( "python ", "python3 ", "pip ", "pip3 ", "conda ", "bash ", "sh ", "chmod ", "export ", "set ", "CUDA_VISIBLE_DEVICES=", "./", "accelerate ", "torchrun ", "deepspeed ", "make ", "docker ", ) def collect_headings(readme_text: str) -> List[Dict[str, object]]: headings: List[Dict[str, object]] = [] offset = 0 for line in readme_text.splitlines(keepends=True): matched = HEADING_RE.match(line.strip()) if matched: headings.append( { "offset": offset, "level": len(matched.group("marks")), "title": matched.group("title").strip(), } ) offset += len(line) return headings def nearest_heading(headings: List[Dict[str, object]], offset: int) -> Optional[str]: current: Optional[str] = None for heading in headings: if int(heading["offset"]) > offset: break current = str(heading["title"]) return current def infer_section_category(section: Optional[str]) -> Optional[str]: if not section: return None lowered = section.lower() if any(word in lowered for word in ["inference", "usage", "demo", "example", "text-to-image", "image-to-image", "transcribe"]): return "inference" if any(word in lowered for word in ["evaluation", "evaluate", "benchmark", "metrics", "validation"]): return "evaluation" if any(word in lowered for word in ["training", "train", "finetune", "fine-tune", "pretrain"]): return "training" return None def infer_section_kind(section: Optional[str]) -> Optional[str]: if not section: return None lowered = section.lower() if any(word in lowered for word in ["install", "installation", "setup", "environment", "requirements"]): return "setup" if any(word in lowered for word in ["download", "checkpoint", "weights", "dataset", "data preparation"]): retur