skills.rest
Orchestra-Research
Orchestra-ResearchOFFICIAL
Explore all skills by this author →
GitHub

ara-compiler

Compiles research inputs into structured Agent-Native Research Artifacts with claims, evidence, and exploration graphs.

13.0k|930|Updated Nov 3, 2025
Search Tags:#validation#evidence#knowledge extraction#claims#research artifact#paper ingestion#exploration graph
One-click install
npx skills add https://github.com/Orchestra-Research/AI-research-SKILLs --skill ara-compiler-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ara-compiler
Source: https://github.com/Orchestra-Research/AI-research-SKILLs/tree/main/22-agent-native-research-artifact/compiler
Command: npx skills add https://github.com/Orchestra-Research/AI-research-SKILLs --skill ara-compiler-orchestra-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Research knowledge in papers, codebases, and experiment logs is unstructured and hard for AI agents to traverse, verify, or build upon. This Skill converts any research input into a validated, machine-executable Agent-Native Research Artifact (ARA) with falsifiable claims, grounded evidence, and a reconstructed research trajectory. ## Core Features & Use Cases - Universal Input Ingestion: Accepts PDF papers, GitHub repositories, code files, experiment logs, configs, raw notes, or interactive dialogue, with no fixed input schema. - Four-Stage Epistemic Compilation: Performs semantic deconstruction, cognitive mapping (claims, concepts, experiments, related work), physical stubbing (configs, typed Python code stubs), and exploration graph extraction. - Coverage and Seal Validation: Runs up to 3 coverage-check rounds against the source, then validates the artifact against Seal Level 1 structural and cross-layer binding checks, fixing failures iteratively. - Use Case: Given an arXiv PDF and its accompanying repository, generate a complete ARA containing PAPER.md, logic/ claims and experiments, src/ configs and code stubs, an exploration_tree.yaml with dead ends and decisions, and an evidence/ layer with exact result tables. ## Quick Start Compile the attached paper PDF and its GitHub repository into a complete ARA artifact in the ./ara-output/ directory.

Frequently Asked Questions about ara-compiler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert a research paper into a structured knowledge artifact?▼

Provide the PDF, arXiv link, or repository path as input and the compiler reads all sources, reasons through a four-stage epistemic protocol, and generates the full ARA directory including claims, experiments, configs, and evidence tables. It then validates the result with Seal Level 1 checks.

What inputs does the ARA compiler accept?▼

It accepts PDF papers, arXiv links, GitHub URLs or local repos, code files and notebooks, experiment logs, configuration files, raw research notes, and even verbal descriptions. Partial inputs are handled gracefully with gaps marked as not available.

What files does an Agent-Native Research Artifact contain?▼

An ARA contains PAPER.md with frontmatter and a layer index, logic/ files for problem, claims, concepts, experiments, solution and related work, src/ configs and Python code stubs, trace/exploration_tree.yaml, and an evidence/ directory with exact result tables and figures.

Does the compiler work without a paper or repository?▼

Yes, it can build an ARA interactively through dialogue with the user. It asks clarifying questions when input is ambiguous or incomplete rather than hallucinating missing content.

How does the compiler avoid hallucinating research results?▼

It enforces exact-number copying from sources, separates direct evidence from interpretation, marks inferred exploration nodes distinctly from explicit ones, and writes "Not specified in paper" when information is unavailable. A coverage loop and Seal validation catch gaps and inconsistencies.

What are the limitations of automated paper ingestion?▼

Ambiguous PDF extraction may require manual page inspection, and not every ARA field is fillable from every input type. Inferred exploration graph nodes reconstruct plausible logic but are not literal session records, so they are marked as inferred.

Related Skills

Explore More Education & Research Skills→
MarsPainMarsPain

paper-research-assistant

Resolve academic paper sources and generate Markdown research notes.

Community
Advanced
skyllwtskyllwt
1.7k

visualize

Generates Obsidian graph configs and Canvas knowledge maps from wiki graph data.

Community
Intermediate
accionlabsaccionlabs
2

validate-functional-graph

Validate functional graphs against source documents for coverage and quality metrics.

Official
Advanced
tronghieutronghieu
36

lumi-research-survey

Generate grounded survey narratives from wiki sources and graph edges.

Community
Intermediate