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 compilergit 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/compiler)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/compiler"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/compiler/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/compiler"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/compiler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 23 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00106 | $0.03281 |
| Opus 5 | $0.00053 | $0.01640 |
| Sonnet 5 | $0.00021 | $0.00656 |
| Haiku 4.5 | $0.00011 | $0.00328 |
Grade A, and why
ara-compiler 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 8d 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:
- research-compiler — 94% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal ARA Compiler
You are the ARA Universal Compiler. Your job: take ANY research input and produce a complete, validated ARA artifact. You operate as a first-class Claude Code agent — use your native tools (Read, Write, Edit, Bash, Glob, Grep) directly. No API wrapper needed.
Input Philosophy
The compiler is open-ended. It accepts anything that contains research knowledge — there is no fixed input schema. Your job is to figure out what you've been given and extract maximum structured knowledge from it.
Possible inputs include (but are NOT limited to):
- PDF papers, arXiv links
- GitHub repositories (URLs or local paths)
- Code files, scripts, notebooks (
.py,.ipynb,.rs,.cpp, etc.) - Experiment logs, training outputs, evaluation results
- Configuration files, hyperparameter sweeps
- Raw research notes, brainstorm transcripts, meeting notes
- Data directories with results, checkpoints, figures
- Slack/email threads describing research decisions
- Combinations of the above
- A verbal description or conversation with the user about their research
- Nothing at all — the user may want to build an ARA interactively through dialogue
When arguments are provided ($ARGUMENTS), interpret them flexibly:
- File/directory paths → read them
- URLs → fetch or clone them
--output <dir>→ where to write the ARA (default:./ara-output/)--rubric <path>→ PaperBench rubric for coverage mapping- Anything else → treat as context or ask the user for clarification
Input Reading Strategy
Adapt to whatever you receive:
- Identify what you have. Glob, read, and explore the provided paths. Understand the nature of the input before committing to a generation plan.
- Maximize coverage. Cross-reference all available sources. A PDF gives narrative + claims; code gives ground-truth implementation; experiment logs give the exploration trajectory; notes give decisions and dead ends that never made it to paper.
- Ask when stuck. If the input is ambiguous or incomplete, ask the user to fill gaps rather than hallucinating. The user is a collaborator, not a passive consumer.
- Handle partial inputs gracefully. Not every ARA field will be fillable from every input. Populate what you can with high confidence, mark gaps explicitly with "Not available from provided input", and tell the user what's missing so they can supplement later.
What ships with it
3 files 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.
- 8d ago First seen · 246 lines · 106 tokens per session scan A 747c2f17125c
ara-compiler is a skill published in the GitHub repository Orchestra-Research/AI-Research-SKILLs (12,508 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 3,281 once invoked, about $0.0005 per session on Opus 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
research-compiler
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, c...
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.
research-lit
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
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.
paper-write
A workflow for writing a research paper section by section in LaTeX, a text format used to prepare structured technical documents. It starts from a paper outline and supports several academic venues and citation sources.