Agent skill · research science · zechenzhangagi
ara-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, 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.
Why this skill is useful
Adds a comprehensive executable workflow for compiling diverse research inputs into structured knowledge artifacts, which the AI cannot generate on its own.
What it needs
About 8k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
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: 1. Identify what you have. Glob, read, and explore the provided paths. Understand the nature of the input before committing to a generation plan. 2. 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. 3. Ask when stuck. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/compiler