# Ara Compiler

Compiles any research input, PDF papers, GitHub repositories, experiment logs, code...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ara-compiler -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ara-compiler -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ara-compiler -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill ara-compiler -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/22-agent-native-research-artifact/compiler/SKILL.md