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Agent Skill

Ara Rigor Reviewer skill

ara-rigor-reviewerOrchestra-Research/AI-Research-SKILLs22-agent-native-research-artifact/rigor-reviewer/SKILL.md

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 producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.

14kMIT

always on

90 tokname + description

once used

4,013 tokSKILL.md body

bundled files

18 KB

repo stars

14kGitHub

Install the Ara Rigor Reviewer skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill ara-rigor-reviewer

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git /tmp/AI-Research-SKILLs
mkdir -p ~/.claude/skills
cp -R /tmp/AI-Research-SKILLs/22-agent-native-research-artifact/rigor-reviewer ~/.claude/skills/ara-rigor-reviewer

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description90 tok
When the agent uses itThe SKILL.md instructions4,013 tok
Only if opened1 bundled files (8 KB)varies

Until it is used, ara-rigor-reviewer costs about 15× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 98 skills from Orchestra-Research/AI-Research-SKILLs would load 6,174 tokens of descriptions. Skills vs MCP study

Checks

  • The name doesn't match its folder name, as the specification requires.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • ARA Seal Level 2: Semantic Epistemic Review
  • Six Review Dimensions
  • Procedure
  • Step 1: Read the ARA
  • Step 2: Parse Entities
  • Step 3: Build Working Maps
  • Step 4: Evaluate Each Dimension
  • Step 5: Compile Findings
  • Step 6: Compute Overall Grade
  • Step 7: Write Report
  • Critical Rules
  • Reference

More skills from Orchestra-Research/AI-Research-SKILLs

All skills in this repo
  1. academic-plottingOrchestra-Research/AI-Research-SKILLs

    Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

    14k67 tok idle · 5,308 in use
  2. ara-compilerOrchestra-Research/AI-Research-SKILLs

    Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

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  3. ara-research-managerOrchestra-Research/AI-Research-SKILLs

    Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.

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  4. audiocraft-audio-generationOrchestra-Research/AI-Research-SKILLs

    PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

    14k54 tok idle · 3,633 in use
  5. autogpt-agentsOrchestra-Research/AI-Research-SKILLs

    Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

    14k38 tok idle · 2,137 in use
  6. autoresearchOrchestra-Research/AI-Research-SKILLs

    Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

    14k98 tok idle · 5,151 in use
  7. awq-quantizationOrchestra-Research/AI-Research-SKILLs

    Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

    14k79 tok idle · 2,318 in use
  8. axolotlOrchestra-Research/AI-Research-SKILLs

    Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

    14k47 tok idle · 1,003 in use