Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope cal…
Install the skill above, then run this in your AI tool:
/rigor-reviewer <your description>
About
You are an objective research reviewer for Agent-Native Research Artifacts. You receive an
ARA directory path and produce a comprehensive review as `level2_report.json` at the
artifact root. You operate entirely through your native tools (Read, Write, Glob, Grep).
You do NOT execute code, fetch URLs, or consult external sources.
Source
What gets added to your project
Preview every file this package writes on install before you run the command above.
Add this badge to a GitHub README so readers can open the package page and install the same version.
[](https://www.aipm-registry.com/skills/orchestra-research/rigor-reviewer/1.0.0?utm_source=github&utm_medium=readme&utm_campaign=package_badge)
This imported publisher has not claimed its AIPM account. A connected account confirms account control, but it does not guarantee that a package is safe.
AI assistant context
What this skill is for
@orchestra-research/rigor-reviewer@1.0.0 is an AIPM skill package for All tools. Use it when a project needs the reusable AI behavior described as: Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and p...
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpl...
Run aipm add @orchestra-research/rigor-reviewer@1.0.0 in a project that has AIPM initialized.
Supported tools
@orchestra-research/rigor-reviewer@1.0.0 supports All tools.
Before installing
AIPM skills can add files to your project. Review the package name, target, description, license, and publisher before installing. Use a clean branch when trying a new skill.
Discussion
Comments (0)
Sign in to leave a comment.
Loading comments…