Your AI Research Platform — From Papers to Publications, Powered by Claude Code
From paper ingestion to publication — your research knowledge compounds, never decays.
ΩmegaWiki is built by DAIR Lab at Peking University — a fully agentic platform that automates the complete research pipeline, from knowledge ingestion to paper submission.
Weitong Qian PKU Undergraduate · 2023 |
Beicheng Xu PKU Ph.D. · 2023 |
Zhongao Xie PKU Undergraduate · 2025 |
Bowen Fan PKU Undergraduate · 2024 |
Guozheng Tang PKU Undergraduate · 2024 |
Xinzhe Wu PKU Undergraduate · 2024 |
Jiale Chen PKU Undergraduate · 2024 |
Mingtian Yang PKU Undergraduate · 2024 |
A possible usage process：/ideate [research-direction-or-topic](You can use --skip-pilot to decide whether to conduct preliminary experiments) -> /exp-design <idea-slug>-> For each experimental block,recommended flow: /exp-run <slug> [--env local|remote] to deploy → /exp-status to monitor → /exp-run <slug> --collect to collect.->/exp-eval <experiment-slug>
✨ : New Skills
/exp-pilot-run — Pilot experiment execution: write code, deploy, monitor, collect raw results.
/exp-pilot-eval — Pilot result evaluation: read results, apply lenient verdict logic
These two skills are built into Phase5 of /ideate
🛠️ : Modified Skills
/ideate
5 structured generation paths (A-E) for both Claude and Review LLM.
Phase restructuring: Filter & Validation merged into Phase 3, Write Wiki moved to Phase 4.
Phase 5: Finish pilot design and workflow invocation
Your ideas will follow a clearer path, and a more reasonable screening mechanism will be established through pilot experiments.
/exp-design
A brand-new experimental design process:method candidate generation + 5 experiment block types + iterative ablation loop
/exp-run
Add the code decision gate, code optimization and config check
Run /poster after /paper-draft + /paper-compile to turn your finished draft into a self-contained 1400×900 HTML poster and a print-quality PNG. Figures, booktabs tables, and math macros are extracted automatically from your LaTeX source; Claude walks you through picking which figures land in which sections and customizing the header (venue, affiliation logo). Export to PDF from your browser's print dialog. Pipeline adapted from PaperX (arXiv:2602.03866).
Title, author, venue, and prose are placeholder nonsense to avoid exposing real research; tables and figures preserve the pipeline demo.
Run /discover --venue iclr --year 2024 (or any conference/year) and get a personalized shortlist of papers from that venue, ranked by relevance to what's already in your wiki. Instead of scrolling a 7000-paper proceedings, you see the dozen that actually matter for your research direction, each with a rationale tied to topics and methods you already track. No new API keys, no ingest side-effects on your wiki — just a ranked reading list. Supports NeurIPS, ICLR, ICML, and other venues covered by Paper Copilot.
Run /daily-arxiv for a one-off pass, or /daily-arxiv setup to schedule the same pipeline in GitHub Actions. The skill builds an evidence packet from arXiv + Semantic Scholar + DeepXiv, lets the LLM rank candidates against your wiki interests, and delivers a digest by e-mail. Explicit --mode auto-ingest calls /ingest for high-confidence picks; inform mode just notifies.
Your research graph now has two ways to explore:
- Web UI — run
python3 tools/serve.py, openhttp://localhost:8765/#/graph. Click any node to highlight its neighborhood via BFS, filter by entity type or edge category, double-click to open the full page in the Reader. - Obsidian — run
/visualize --obsidianto generate a color-coded graph config, or/visualize --canvasto produce a force-layout Canvas with labeled semantic edges.
Architectures, training recipes, evaluation protocols, and other reusable techniques now live in wiki/methods/ as proper wiki entities — with their own pages, source-paper links, and parent/child method chains.
Andrej Karpathy proposed LLM-Wiki: an LLM that builds and maintains a persistent, structured wiki from your sources — not a throwaway RAG answer, but compounding knowledge that grows smarter with every paper you feed it.
ΩmegaWiki takes that idea and runs the full distance. It's not just a wiki builder — it's a complete research lifecycle platform: from paper ingestion → knowledge graph → gap detection → idea generation → experiment design → paper writing → peer review response. All driven by 24 Claude Code skills, all centered on one wiki as the single source of truth.
Drop your .tex / .pdf files in a folder. Run one command. Get a fully cross-referenced knowledge base — and then use it to generate novel research ideas, design experiments, write papers, and respond to reviewers.
| RAG | ΩmegaWiki | |
|---|---|---|
| Knowledge persistence | Rediscovered on every query | Compiled once, maintained forever |
| Structure | Flat chunk store | 9 typed entities with relationships |
| Cross-references | None — chunks are isolated | Bidirectional wikilinks + typed graph |
| Knowledge gaps | Invisible | Explicitly tracked, drive research |
| Failed experiments | Lost | First-class anti-repetition memory |
| Output | Chat answers | Papers, surveys, experiment plans, rebuttals |
| Compounding | No — same cost every query | Yes — each paper enriches the whole graph |
Every skill reads from and writes back to the wiki. Knowledge compounds — each new paper enriches the whole graph. Failed experiments aren't discarded; they become anti-repetition memory that prevents re-exploring dead ends.
Prerequisites: Python 3.9+, Node.js 18+
# 1. Clone
git clone https://github.com/skyllwt/OmegaWiki.git
cd OmegaWiki
# 2. Install Claude Code
npm install -g @anthropic-ai/claude-code
claude login
# 3. One-click setup
chmod +x setup.sh && ./setup.sh # Linux / macOS
# Windows (PowerShell):
# powershell -ExecutionPolicy Bypass -File .\setup.ps1
# setup creates .venv for OmegaWiki
# the script does not keep your shell activated, but /init will use .venv automatically
# 4. Put your own papers in raw/papers/ (.tex or .pdf)
# Optional: add intent notes to raw/notes/ and saved pages to raw/web/
# /init and direct local /ingest will manage generated inputs under raw/discovered/ and raw/tmp/
# 5. Build your wiki
claude
# Then type: /init [your-research-topic]Manual setup (Linux / macOS)
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # Edit to add API keys
cp config/settings.local.json.example .claude/settings.local.jsonManual setup (Windows / PowerShell)
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
Copy-Item .env.example .env # Edit to add API keys
Copy-Item config\settings.local.json.example .claude\settings.local.jsonNote: native Windows is supported for the local pipeline. Remote-GPU
experiments via /exp-run --env remote rely on ssh/rsync/screen
and are best run from WSL2 or Linux/macOS.
| Key | Required? | How to get | What it enables |
|---|---|---|---|
ANTHROPIC_API_KEY |
Yes | claude login (automatic) |
Powers all Claude Code skills |
CLAUDE_CODE_OAUTH_TOKEN |
Optional | claude setup-token |
GitHub Actions Claude Code auth for Pro/Max users |
SEMANTIC_SCHOLAR_API_KEY |
Optional | semanticscholar.org/product/api (free) | Citation graph, paper search |
DEEPXIV_TOKEN |
Optional | setup.sh auto-registers |
Semantic search, TLDR, trending |
LLM_API_KEY + LLM_BASE_URL + LLM_MODEL |
Optional | Any OpenAI-compatible API | Cross-model review; /daily-arxiv inform recommendations |
Cross-model review: ΩmegaWiki uses a second LLM as an independent reviewer for ideas, experiments, and paper drafts. Works with any OpenAI-compatible API — DeepSeek, OpenAI, Qwen, OpenRouter, SiliconFlow, etc. If not configured, skills still work in Claude-only mode.
/daily-arxiv runs a one-off fresh-paper recommendation pass even before
automation is configured. To schedule the same pipeline in GitHub Actions, copy
config/daily-arxiv.yml.example to config/daily-arxiv.yml, then run
/daily-arxiv setup. The config stores non-secret preferences such as mode,
categories, caps, and schedule; SMTP/API credentials stay in .env or GitHub
Actions secrets. In CI inform mode, recommendations can use Claude Code auth
(ANTHROPIC_API_KEY or CLAUDE_CODE_OAUTH_TOKEN) or the OpenAI-compatible
LLM_* review model; auto-ingest still requires Claude Code.
See
docs/daily-arxiv-deployment.mdfor the GitHub Actions setup checklist and symptom-keyed troubleshooting.
Sample digest
A real /daily-arxiv run: ranked recommendations with scores, rationales, wiki connections, and an auto-ingest section.
26 slash commands spanning the full research lifecycle:
| Command | What it does |
|---|---|
/setup |
First-time configuration (API keys, language, dependencies) |
/reset <scope> |
Destructive cleanup: wiki | raw | log | checkpoints | all |
| Command | What it does |
|---|---|
/prefill <domain> |
Optionally seed foundations/ with background knowledge |
/init [topic] |
Bootstrap a full wiki from user raw sources plus optional discovery |
/ingest <source> |
Parse a paper → wiki pages + cross-references |
/discover |
Recommend ranked next-read papers from anchors, a topic, the current wiki, or a venue/year |
/edit <request> |
Add/remove sources or update wiki content |
/ask <question> |
Query the wiki, crystallize answers back |
/check |
Health scan: broken links, missing cross-refs, consistency |
| Command | What it does |
|---|---|
/daily-arxiv |
Run/manage a daily arXiv recommendation feed (+ optional GitHub Actions scheduler) |
/ideate [research-direction-or-topic] |
Multi-phase idea generation from cross-topic connections |
/exp-pilot-run <idea-slug> |
Pilot experiment execution: write code, deploy, monitor, collect raw results. |
/exp-pilot-eval <idea-slug> |
Pilot result evaluation: read results, apply lenient verdict logic |
/novelty <idea> |
Multi-source novelty verification (web + S2 + wiki + review LLM) |
/review <artifact> |
Cross-model adversarial review for any research artifact |
/exp-design <idea> |
Idea-driven experiment + ablation design |
/exp-run <experiment> |
Implement + deploy + monitor (local or remote GPU) |
/exp-status |
Dashboard for running experiments; auto-collect results |
/exp-eval <experiment> |
Verdict gate → auto-update the linked idea + graph |
/refine <artifact> |
Multi-round: produce → review → fix → re-review |
| Command | What it does |
|---|---|
/survey |
Generate Related Work from wiki knowledge |
/paper-plan <ideas> |
Outline from validated-idea graph + evidence matrix |
/paper-draft <plan> |
Draft LaTeX + figures, section by section |
/paper-compile <dir> |
Compile → PDF, auto-fix, verify page/anonymity |
/research <direction> |
End-to-end orchestrator with human gates |
/rebuttal <reviews> |
Parse reviewer comments → draft point-by-point responses |
| Type | Directory | Purpose |
|---|---|---|
| Paper | papers/ |
Structured summary: problem/key idea/method/experiment+results/limitations + tldr/contribution_type/datasets |
| Concept | concepts/ |
Cross-paper technical concept with variants, comparisons, definition, linked ideas |
| Topic | topics/ |
Research direction map with SOTA tracker, key benchmarks, and open problems (split into known + methodological gaps) |
| Person | people/ |
Researcher profile with research areas, recent work, and a researcher/team/organization type |
| Idea | ideas/ |
Research idea with lifecycle, novelty argument & score, target venue |
| Experiment | experiments/ |
Full record: hypothesis → setup → results → updates to the linked idea |
| Method | methods/ |
Reusable, citable technique entity (cross-paper); links to source papers and parent/child methods |
| Summary | Summary/ |
Domain-wide survey across topics |
| Foundation | foundations/ |
Background knowledge (terminal: receives inward links, writes none) |
Semantic relationships are stored in graph/edges.jsonl; bibliographic paper citations are stored separately in graph/citations.jsonl.
Paper-paper semantic edges include same_problem_as, similar_method_to, complementary_to, builds_on, compares_against, improves_on, challenges, and surveys. Paper-concept edges use introduces_concept, uses_concept, extends_concept, and critiques_concept. Workflow edges (supports, contradicts, tested_by, invalidates, addresses_gap, inspired_by, derived_from) span experiments, ideas, methods, and concepts.
All pages use Obsidian [[wikilink]] format — open wiki/ in Obsidian for visual graph exploration.
GitHub Actions runs the /daily-arxiv recommendation pipeline at UTC 00:17 daily (08:17 Beijing time):
- Add SMTP secrets to repo Settings → Secrets when e-mail delivery is enabled:
SMTP_HOST,SMTP_PORT,SMTP_USER,SMTP_PASSWORD,SMTP_FROM,DAILY_ARXIV_EMAIL_TO - Optional inform-mode LLM recommendation: add
ANTHROPIC_API_KEYorCLAUDE_CODE_OAUTH_TOKENfor Claude Code, orLLM_API_KEY,LLM_BASE_URL, andLLM_MODELfor any OpenAI-compatible provider .github/workflows/daily-arxiv.ymlfetches arXiv, deduplicates against the wiki, builds a recommendation context, uploads artifacts, and sends the digest by SMTP
auto-ingest mode is explicit and requires Claude Code in CI, because plain API LLMs cannot invoke slash skills such as /ingest. Use manual dispatch with send_email=false for a dry run without SMTP secrets.
OmegaWiki/
├── CLAUDE.md # Runtime schema & rules
├── wiki/ # Knowledge base (LLM-maintained)
│ ├── papers/ # Structured paper summaries
│ ├── concepts/ # Cross-paper technical concepts
│ ├── topics/ # Research direction maps
│ ├── people/ # Researcher profiles
│ ├── ideas/ # Research ideas (with lifecycle)
│ ├── experiments/ # Experiment records
│ ├── methods/ # Reusable cross-paper method entities
│ ├── Summary/ # Domain-wide surveys
│ ├── foundations/ # Background knowledge (terminal pages)
│ ├── outputs/ # Generated artifacts
│ ├── graph/ # Auto-generated: edges, context, gaps
│ ├── index.md # Content catalog
│ └── log.md # Chronological log
├── raw/ # Source materials
│ ├── papers/ # User-owned .tex / .pdf files
│ ├── discovered/ # external papers from /init and explicit /daily-arxiv auto-ingest
│ ├── tmp/ # generated prepared local sidecars for /init and direct local /ingest
│ ├── notes/ # User-owned .md notes
│ └── web/ # User-owned HTML / Markdown
├── tools/ # Deterministic Python helpers
│ ├── research_wiki.py # Wiki engine (20 CLI commands)
│ ├── init_discovery.py # /init prepare + plan + fetch helper
│ ├── discover.py # /discover candidate gathering, dedup, ranking
│ ├── lint.py # Structural validation (10 checks)
│ ├── reset_wiki.py # Scoped destructive cleanup helper
│ ├── fetch_arxiv.py # arXiv RSS fetcher
│ ├── fetch_s2.py # Semantic Scholar API
│ ├── fetch_deepxiv.py # DeepXiv semantic search
│ ├── fetch_wikipedia.py # Wikipedia fetcher (used by /prefill)
│ └── remote.py # SSH ops for remote experiments
├── .claude/skills/ # 24 Claude Code skill definitions
├── i18n/ # Bilingual: en/ (canonical) + zh/
├── config/ # Configuration templates
├── mcp-servers/ # Cross-model review server
└── .github/workflows/ # Daily arXiv cron
ΩmegaWiki ships in English and Chinese:
./setup.sh --lang en # English (default)
./setup.sh --lang zh # 中文We welcome contributions! See CONTRIBUTING.md for guidelines.
ΩmegaWiki runs on Claude Code, which speaks the Anthropic API protocol. You can use Claude directly, or route Claude Code to any third-party provider that exposes an Anthropic-compatible endpoint by overriding a few environment variables.
ΩmegaWiki 基于 Claude Code,Claude Code 使用 Anthropic API 协议通信。你既可以直接使用 Claude,也可以通过覆盖几个环境变量,把 Claude Code 指向任意支持 Anthropic 协议的第三方供应商。
claude login # OAuth, no manual config / OAuth 登录,无需手动配置Pick a provider below, paste the snippet into ~/.claude/settings.json (or the project's .claude/settings.json), and replace the <...> placeholder with your own API key. Model names and extra options are taken from each provider's official Claude Code docs — if anything stops working (e.g. a model is renamed), check the provider's website.
从下方任选一个供应商,把对应配置粘贴到 ~/.claude/settings.json(或项目的 .claude/settings.json),并把 <...> 占位符替换为你自己的 API key。模型名与额外选项均来自各供应商官方 Claude Code 文档;若出现问题(例如模型改名),请查询对应官网。
{
"env": {
"ANTHROPIC_BASE_URL": "https://api.xiaomimimo.com/anthropic",
"ANTHROPIC_AUTH_TOKEN": "<your-mimo-key>",
"ANTHROPIC_MODEL": "mimo-v2.5",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "mimo-v2.5",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "mimo-v2.5-pro",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "mimo-v2.5"
}
}{
"env": {
"ANTHROPIC_BASE_URL": "https://api.deepseek.com/anthropic",
"ANTHROPIC_AUTH_TOKEN": "<your-deepseek-key>",
"ANTHROPIC_MODEL": "deepseek-v4-pro[1m]",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "deepseek-v4-pro[1m]",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "deepseek-v4-pro[1m]",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "deepseek-v4-flash",
"CLAUDE_CODE_SUBAGENT_MODEL": "deepseek-v4-flash",
"CLAUDE_CODE_EFFORT_LEVEL": "max"
}
}{
"env": {
"ANTHROPIC_BASE_URL": "https://api.moonshot.ai/anthropic",
"ANTHROPIC_AUTH_TOKEN": "<your-moonshot-key>",
"ANTHROPIC_MODEL": "kimi-k2.5",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "kimi-k2.5",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "kimi-k2.5",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "kimi-k2.5",
"CLAUDE_CODE_SUBAGENT_MODEL": "kimi-k2.5",
"ENABLE_TOOL_SEARCH": "false"
}
}{
"env": {
"ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
"ANTHROPIC_AUTH_TOKEN": "<your-zai-key>",
"API_TIMEOUT_MS": "3000000"
}
}Z.AI applies a default server-side model mapping, so no explicit
ANTHROPIC_MODELis needed. Z.AI 默认在服务端做模型映射,无需显式设置ANTHROPIC_MODEL。
Skip the Claude Code onboarding / 跳过 Claude Code 初始引导
When using a third-party key (instead of claude login), Claude Code's first-run onboarding won't complete automatically. Create or edit .claude.json and mark it done:
使用第三方 key 时不会走 claude login,Claude Code 首次启动的引导不会自动完成。创建或编辑 .claude.json,手动标记引导已完成:
- macOS / Linux:
~/.claude.json - Windows:
<user-home>\.claude.json
{
"hasCompletedOnboarding": true
}Then run claude as usual. / 保存后正常运行 claude 即可。
Scan to join the ΩmegaWiki WeChat group / 扫码加入微信交流群
- Andrej Karpathy — for the LLM-Wiki concept that inspired this project
- Claude Code — the AI agent runtime that powers ΩmegaWiki
MIT — use it, fork it, build on it.
Andrej Karpathy 提出了 LLM-Wiki 概念：让 LLM 构建并维护一个持久的、结构化的 wiki，而不是一次性的 RAG 回答。知识持续积累，每一篇新论文都让整个知识图谱更强。
ΩmegaWiki 将这个理念完整实现。 它不仅是 wiki 构建器，更是完整的研究全流程平台：从论文摄入 → 知识图谱 → 缺口检测 → 想法生成 → 实验设计 → 论文写作 → 同行评审回复。24 个 Claude Code Skills 驱动，一个 wiki 作为唯一的知识中枢。
| RAG | ΩmegaWiki | |
|---|---|---|
| 知识持久性 | 每次查询都重新发现 | 编译一次，持续维护 |
| 结构 | 扁平的 chunk 存储 | 9 种实体类型 + 关系图 |
| 交叉引用 | 无 — chunk 彼此孤立 | 双向 wikilink + 类型化边 |
| 知识缺口 | 不可见 | 显式追踪，驱动研究方向 |
| 失败实验 | 丢失 | 一等公民，防止重复探索 |
| 输出 | 聊天回答 | 论文、综述、实验方案、审稿回复 |
| 复利效应 | 无 — 每次查询成本相同 | 有 — 每篇论文丰富整个图谱 |
前置条件： Python 3.9+, Node.js 18+
git clone https://github.com/skyllwt/OmegaWiki.git && cd OmegaWiki
# 安装 Claude Code
npm install -g @anthropic-ai/claude-code
claude login
# 一键配置
chmod +x setup.sh && ./setup.sh --lang zh # Linux / macOS
# Windows (PowerShell):
# powershell -ExecutionPolicy Bypass -File .\setup.ps1 -Lang zh
# setup 会为 OmegaWiki 创建 .venv
# 脚本不会把你当前 shell 永久激活，但 /init 会自动使用 .venv
# 把你自己的论文放入 raw/papers/（.tex 或 .pdf）
# 可选：把意图笔记放入 raw/notes/，网页存档放入 raw/web/
# /init 与直接本地 /ingest 会自动管理 raw/discovered/ 与 raw/tmp/ 下的生成内容
# 启动 Claude Code
claude
# 输入：/init [你的研究方向]Windows 用户：本地 pipeline 已原生支持。
/exp-run --env remote远程 GPU 实验依赖ssh/rsync/screen，建议在 WSL2 或 Linux/macOS 下运行。
| Key | 必须？ | 获取方式 | 用途 |
|---|---|---|---|
ANTHROPIC_API_KEY |
是 | claude login |
驱动所有 Skill |
CLAUDE_CODE_OAUTH_TOKEN |
可选 | claude setup-token |
Pro/Max 用户的 GitHub Actions Claude Code auth |
SEMANTIC_SCHOLAR_API_KEY |
可选 | semanticscholar.org（免费） | 引用图谱、论文搜索 |
DEEPXIV_TOKEN |
可选 | setup.sh 自动注册 |
语义搜索、热门趋势 |
LLM_API_KEY + LLM_BASE_URL + LLM_MODEL |
可选 | 任意 OpenAI 兼容 API | 跨模型评审；/daily-arxiv inform 推荐 |
GitHub Actions 每天 UTC 00:17（北京时间 08:17）运行 /daily-arxiv 推荐 pipeline：拉取 arXiv、按 wiki 去重、构建 recommendation context、上传 artifacts，并可通过 SMTP 发送 digest 邮件。
启用邮件时，在 repo Settings → Secrets 添加：SMTP_HOST、SMTP_PORT、SMTP_USER、SMTP_PASSWORD、SMTP_FROM、DAILY_ARXIV_EMAIL_TO。
CI inform mode 可使用 ANTHROPIC_API_KEY 或 CLAUDE_CODE_OAUTH_TOKEN 启动 Claude Code，也可使用 LLM_API_KEY、LLM_BASE_URL、LLM_MODEL 接入任意 OpenAI-compatible provider。auto-ingest 是显式模式，并且需要 Claude Code，因为普通 API LLM 不能调用 /ingest 这类 slash skill。手动触发时可设置 send_email=false，用于无 SMTP secrets 的 dry run。
Digest 示例 / Sample digest
一次真实的 /daily-arxiv 运行结果：带分数、理由、wiki 关联以及 auto-ingest 区块的推荐 digest。
| 命令 | 功能 |
|---|---|
/setup |
首次配置（API key、语言、依赖） |
/reset |
按范围销毁性清理：wiki | raw | log | checkpoints | all |
/prefill |
可选地预填 foundations/ 背景知识 |
/init |
基于用户 raw 素材并按需做外部发现来搭建 wiki |
/ingest |
消化论文，创建页面 + 交叉引用 |
/discover |
从 anchor、topic、当前 wiki 或 venue/year 推荐排序后的下一批待读论文 |
/edit |
增删 raw 或更新 wiki |
/ask |
对 wiki 提问 |
/check |
wiki 健康检查 |
/daily-arxiv |
运行/管理每日 arXiv 推荐 feed（可选 CI 定时） |
/ideate |
跨方向构思研究 idea |
/exp-pilot-run |
预实验部署运行 |
/exp-pilot-eval |
预实验结果评估 |
/novelty |
多源新颖性验证 |
/review |
跨模型评审 |
/exp-design |
idea 驱动的实验设计 |
/exp-run |
部署 + 监控实验 |
/exp-status |
实验状态看板 |
/exp-eval |
裁决 → 自动更新关联 idea |
/refine |
多轮迭代改进 |
/survey |
生成 Related Work |
/paper-plan |
idea 图谱 + 实验证据 → 论文提纲 |
/paper-draft |
提纲 + wiki → LaTeX 草稿 |
/paper-compile |
编译 → PDF，自动修复 |
/research |
端到端研究编排器 |
/rebuttal |
解析评审意见 → 逐条回复 |
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