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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill compiler --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 (`$A
- Input Philosophy
- Input Reading Strategy
- Workflow
- Step 1: Read Inputs
- Step 2: 4-Stage Epistemic Chain-of-Thought
- Step 3: Generate Files
- Step 4: Coverage Check Loop (max 3 rounds)
- Step 5: Validate
- Step 6: Fix & Iterate
- Step 7: Report
- Critical Rules
- Reference Files
What does the ara-compiler skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill compiler --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.