メニュー
Autonomous research agent: literature, ideas, experiments, writing, review, rebuttal, presentation, theory. Triggers: 'research', 'write paper', 'survey', 'polish', 'translate', 'review', 'rebuttal', 'slides', 'poster', '写论文', '文献调研', '润色', '翻译', '去AI味', '审稿'.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Run GPTZero AI detection on one or more review files via the app.gptzero.me dashboard. Requires Chrome running with --remote-debugging-port=9222 and the user logged into GPTZero. Use when user says "check ai rate", "gptzero", "检测AI率", "run gptzero", or wants to verify that review prose passes AI detection.
Write a venue-form peer review of someone else's paper. Humanization is automatic. Thin shim over the `research-review` CLI. The CLI runs three steps internally for every paper: (1) codex writes a long detailed free-form draft, no template constraint; (2) extract_judgment compresses the draft into a structured judgment dict (numerics + per-field bullets); (3) prose is regenerated under real-human sentence templates, with numerics preserved from step 1. No draft input, no draft option. Every paper goes through the same three-step pipeline. For self-critique with no AI-detection concern, use /self-review.
Critically review your own paper to find real weaknesses before submission. Output is meant to be read by you (the author) and fed into a revision loop, not submitted to a venue. So: no AI-rate constraint, no corpus-template humanization, no GPTZero verification. The skill optimizes for harsh, specific, paper-grounded critique instead of polite peer-review-ese. Sibling skill: - peer-review: write a venue-form review with prose humanized via corpus templates. Use when you are reviewing someone else's paper and need GPTZero <=cap. Also handles "humanize an existing review draft" via the --draft option.
Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
| name | agentic-research |
| description | Autonomous research agent: literature, ideas, experiments, writing, review, rebuttal, presentation, theory. Triggers: 'research', 'write paper', 'survey', 'polish', 'translate', 'review', 'rebuttal', 'slides', 'poster', '写论文', '文献调研', '润色', '翻译', '去AI味', '审稿'. |
Autonomous research agent built with OpenProgram (Agentic Programming paradigm).
Two-level autonomous loop: Level 1 picks a research stage (literature, writing, review, ...), Level 2 dispatches to functions within that stage. Cross-model review uses GPT (via Codex) as reviewer and Claude as author.
/agentic-research "<your task>"
Single entry point — the agent reads your task and autonomously decides which functions to call.
/agentic-research "Survey recent work on LLM uncertainty"
/agentic-research "Polish this paragraph for NeurIPS: <text>"
/agentic-research "翻译这段中文为英文LaTeX: <中文草稿>"
/agentic-research "Review paper/ as a NeurIPS reviewer"
/agentic-research "Run the full pipeline for topic 'LLM Uncertainty', venue NeurIPS"
# Basic usage (--work-dir is required to run a task)
research-harness --work-dir /abs/path "your task"
research-harness --list # list all functions
# Cross-model review: Claude writes, GPT reviews (ARIS design)
research-harness --work-dir /abs/path "Review the paper at ./project/" \
--provider claude-code \
--review-provider openai-codex
# Custom models
research-harness --work-dir /abs/path "Survey LLM uncertainty" \
--provider openai --model gpt-4o \
--review-provider openai-codex
# Operation log is written automatically to <work-dir>/OPERATION_LOG.md
from research_harness.main import research_agent
from openprogram.providers import create_runtime
# Single model
rt = create_runtime(provider="claude-code")
rt.set_workdir("/abs/path/to/work-dir")
result = research_agent(task="Survey LLM uncertainty", runtime=rt)
# Cross-model review
exec_rt = create_runtime(provider="claude-code")
exec_rt.set_workdir("/abs/path/to/work-dir")
review_rt = create_runtime(provider="openai-codex")
review_rt.set_workdir("/abs/path/to/work-dir")
result = research_agent(
task="Review the paper at ./project/ as EMNLP reviewer",
runtime=exec_rt,
review_runtime=review_rt,
)