AI Research Skills Library is a collection of reusable instructions that guide AI agents through research and machine-learning engineering tasks, from finding ideas and writing papers to training, evaluation, and deployment. It is for configuring agents such as Claude Code, Codex, and Gemini to perform research workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill rigor-reviewergit clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/orchestra-research/ai-research-skills/rigor-reviewer)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/rigor-reviewer"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/rigor-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/rigor-reviewer"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/rigor-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.04161 |
| Opus 5.5 | $0.00036 | $0.01664 |
| Sonnet 5.5 | $0.00018 | $0.00832 |
| Haiku 4.5 | $0.00009 | $0.00416 |
Grade A, and why
ara-rigor-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- rigor-reviewer — 94% identical, 54 lines differ
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARA Seal Level 2: Semantic Epistemic Review
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.
Prerequisite: Level 1 (structural validation) has already passed. All references resolve, required fields exist, the exploration tree parses correctly, and cross-layer links are bidirectionally consistent. Level 2 does NOT re-check any of this. Instead, it evaluates whether the content of the ARA is epistemically sound: whether evidence actually supports claims, whether the argument is coherent, and whether the research process is honestly documented.
Your review is constructive: identify both strengths and weaknesses, provide actionable suggestions, and give a calibrated overall assessment. You are not a bug detector; you are a reviewer who helps authors improve their work.
Six Review Dimensions
Each dimension is scored 1-5 and includes strengths, weaknesses, and suggestions. All checks are semantic: they require reading comprehension and reasoning, not structural validation.
| Dimension | What it evaluates |
|---|---|
| D1. Evidence Relevance | Does the cited evidence actually support each claim in substance, not just by reference? |
| D2. Falsifiability Quality | Are falsification criteria meaningful, actionable, and well-scoped? |
| D3. Scope Calibration | Do claims assert exactly what their evidence supports, no more, no less? |
| D4. Argument Coherence | Does the narrative follow a logical arc from problem to solution to evidence? |
| D5. Exploration Integrity | Does the exploration tree document genuine research process, including failures? |
| D6. Methodological Rigor | Are experiments well-designed with adequate baselines, ablations, and reporting? |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 323 lines · 91 tokens per session scan A 5a8169a5d069
ara-rigor-reviewer is a skill published in the GitHub repository Orchestra-Research/AI-Research-SKILLs (14,012 stars, last pushed 17d ago), licensed MIT. It adds 91 tokens to every session and 4,161 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
sr-screener
Protocol-driven study screening for systematic, scoping and rapid reviews. Turns a proposal or PROSPERO protocol into confirmed eligibility rules, then screens titles/abstracts and full texts with two blinded AI reviewers and a third-reviewer adjudicator: ordered exclusion codes, no silent defaults, resumable batch…
proof-checker
A mathematical proof review and repair workflow for LaTeX documents. It checks whether a proof has valid reasoning, addresses identified gaps, reviews the fixes, and produces an audit report.
auto-paper-improvement-loop
An automated editing cycle for a compiled research paper: an external language model reviews it, fixes are applied, and the paper is compiled again.
paper-illustration
A workflow for generating academic illustrations, such as architecture diagrams and method visuals, with image generation and repeated review. Claude plans and checks the figure during the process.
citation-crosscheck
Double-blind bibliographic cross-check: one agent extracts each cited entry verbatim from the local .bib, a second independently re-fetches the same paper's record from the web while blind to the .bib, and a third extractor emits a mechanical field-by-field delta to catch dropped/re-ordered authors, wrong years, wrong…
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…