GitHub Repository Detail
Ar9av/PaperOrchestra
Ar9av/PaperOrchestra · generated-local-file
An automated AI research-paper writer based off Google's PaperOrchestra paper's implementation through a skills - benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs — a GitHub Skill repository verified at commit 798f03a with 9 SKILL.md files for research workflows.
Skill Overview
An automated AI research-paper writer based off Google's PaperOrchestra paper's implementation through a skills - benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs — a GitHub Skill repository verified at commit 798f03a with 9 SKILL.md files for research workflows.
What task this skill solves
This is a repository-level resource. The directory does not split its multiple Skills into duplicate entries. Review the representative paths and sampled commit, then use the upstream README for installation and runtime requirements.
- Check the default branch, sampled commit, and last push.
- Use representative SKILL.md paths to judge task coverage.
- Independently review upstream code, scripts, license, and permissions before installation.
Resource Type
GitHub Hot Skill Repositories
Categories
Research
Tags
Research, agentic-ai, ai-research, anthropic, antigravity, GitHub Skills
The download is an EasyGlobe-authored bilingual repository brief with verification evidence, not a redistributed repository or directly installable single Skill.
Detailed Skill Introduction
What Ar9av/PaperOrchestra is for
Ar9av/PaperOrchestra is an AI agent skill resource for Research workflows. Based on the archived project material, its core value is: An automated AI research-paper writer based off Google's PaperOrchestra paper's implementation through a skills - benchmark + autoraters using any coding agent (Claude Code, Cursor, Antigravity, Cline, Aider). No API keys, no LLM SDKs — a GitHub Skill repository verified at commit 798f03a with 9 SKILL.md files for research workflows.. This detail page focuses on the practical questions to answer before adopting the skill: what task it solves, how setup works, which README notes matter, where the local Markdown copy is available, and whether the workflow can be adapted for Claude, Codex, Gemini, Kimi, GLM, ChatGPT, or an internal AI agent.
README summary
> This is an EasyGlobe-authored bilingual repository brief, not a redistributed third-party Skill and not an installable copy of the repository. Review the upstream repository before use.
README capability notes
| Path | Frontmatter name | Frontmatter description | SHA-256 |
| --- | --- | --- | --- |
| skills/agent-research-aggregator/SKILL.md | agent-research-aggregator | Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md)....
Workflow fit and practical value
The current taxonomy places Ar9av/PaperOrchestra under Research, with tags such as Research, agentic-ai, ai-research, anthropic, antigravity, GitHub Skills. That means the skill should be evaluated through the lens of repeatable work: what task it helps an AI agent perform, what context the agent needs, what output a user should expect, and whether the workflow can be reused as a team SOP. The archived README is used as the first source of truth whenever it includes feature lists, examples, setup notes, or usage guidance. The local Markdown download is useful for review, internal documentation, and adapting the instructions into your own agent skills repository.
Installation, usage, and platform notes
No explicit installation section was found in the archived README. Open the original source for the official setup path; the download button provides the local Markdown copy saved by EasyGlobe. Use this skill when the task involves Research, agentic-ai, ai-research, anthropic, antigravity, GitHub Skills. It can be adapted into Claude Skills, Codex Skills, Gemini Skills, Kimi Skills, GLM Skills, or a team SOP. If the README does not include a complete command-by-command setup path, the Skill URL remains the official source for the latest installation instructions. Platform support should be confirmed from the original project, but the information on this page is organized so it can be reused as a reference for Claude, Codex, Gemini, Kimi, GLM, ChatGPT, or internal AI agent workflows.
Capabilities
| Path | Frontmatter name | Frontmatter description | SHA-256 |
| --- | --- | --- | --- |
| skills/agent-research-aggregator/SKILL.md | agent-research-aggregator | Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing. | c543b867f8473ca090679c0c21ed1b2776374bf70ba98169b71479da4756e4ce |
| skills/content-refinement-agent/SKILL.md | content-refinement-agent | Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate concession-threshold guard that blocks acceptance on unresolved critical findings. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper". | 808643c5364c36ef57217c4147e1f863cd335d197dabe3def30321f7fb6ec469 |
| skills/literature-review-agent/SKILL.md | literature-review-agent | Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018)....
Supported Platforms
Use this skill as a reference template for these AI agents, model workflows, or team SOPs.
Configuration & Updates
No standalone configuration section was detected in the archive. If the source project requires API keys, account auth, CLI setup, or plugin config, follow the Skill URL.
README Command Examples
No displayable command code block was detected in the README.
Tags
FAQ
Is Ar9av/PaperOrchestra a single Skill?
No. This page represents one GitHub repository. It may contain multiple Skills, and at least one sampled SKILL.md passed the name and description frontmatter gate.
What is in the repository brief download?
It contains an EasyGlobe-authored bilingual summary, repository metadata, representative paths, and frontmatter content hashes. It does not contain the third-party repository or original Skill text.
How is the hot rank calculated?
The score combines weekly/monthly GitHub trend signals, log-normalized stars, age-adjusted stars per day, recent push/release activity, and Skill packaging, documentation, license, and static safety clarity.
Is verification a security audit?
No. Verification confirms repository eligibility, valid frontmatter, and a limited set of static risk patterns. Independently review upstream code, scripts, and permissions before installation.