Auto-claude-code-research-in-sleep (ARIS ⚔️🌙)

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🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.

💡 *Use ARIS as a skill-based workflow in Claude Code / Codex CLI / Cursor / Trae / Antigravity / GitHub Copilot CLI / OpenClaw / DeepSeek Harness — enjoy any way you like!*The executor drives, an independent model (Codex MCP by default) reviews. Alternative model combinations, including ModelScope-hosted models, need no Claude or OpenAI API.

📦 Those are the skills installed directly. Want it in one command? ARIS also ships as a standalone CLI and as plugins: · · ·

❗ codex-cli 0.154.0 removed codex mcp-server — the entry point every ARIS reviewer call used. No impact on ARIS: the codex MCP server is now ARIS’s own bridge over codex exec (mcp-servers/codex-exec/), same tools, same results, all 82 skills unchanged, ultra and per-thread resume intact. One re-registration is all it takes — already installed? → Quick Start step 2b · new install → step 2 · what changed.

🧩 The ARIS family — same method, other jobs. One line each; the details live in their own repos.

— EasyAIOffer: 34 bilingual ML / LLM interview cheat sheets on one page — derivations, from-scratch PyTorch, 25 questions each. 希望大家秋招轻松一点 🌱

· — two sides of one disease. HERO: a ~550-token block for CLAUDE.md / AGENTS.md against the four shapes agents over-defend in (Hashing, Edge cases, Rubrics, Overbuild), bounding what the agent proposes, never what it looks for. Anti-Autoresearch: 61 integrity signals into a deterministic, reviewer-ready report — fabrication forensics, not an AI-text detector.

— ARIS goes multimodal: a rough story in, a movie told in still frames out, every scene checked by another model; the same loop draws clean method diagrams (/method-figure).

— a tiny always-on-top macOS widget that lights up 🔴 when a session waits for your approval; cd aris-monitor && ./run.sh. Many windows? Claude Fleet by @tianyilt is the full dashboard.

🔥 ARIS-Code CLI — 独立安装版 · English | ⬇️ Download · — v0.4.28: ARIS_REASONING_EFFORT works on the Anthropic channel, tool calls through {}-placeholder relays fixed, OS proxy honoured. v0.4.27: launching and quitting the REPL leaves no empty session file. v0.4.26: the fable alias and /model now point at Fable 5.1 (default stays Opus 5). v0.4.25: the Codex reviewer works again on codex-cli ≥ 0.154 (built-in codex exec bridge, nothing to register), /since takes you back to your last input, /resume lists sessions by index, Windows multi-line paste fixed (pending confirmation), 83 bundled skills on GPT-6-Astra.

中文版 README | English

🌙 Let Claude Code do research while you sleep. Wake up to find your paper scored, weaknesses identified, experiments run, and narrative rewritten — autonomously.

🪶 Radically lightweight — no infrastructure, zero lock-in. The entire skill layer is plain Markdown files. No framework to learn, no database to maintain, no Docker to configure, no daemon to babysit. Every skill is a single SKILL.md readable by any LLM — swap Claude Code for Codex CLI, OpenClaw, Cursor, Trae, Antigravity, Copilot CLI, Windsurf, or your own agent and the workflows still work. Fork it, rewrite it, adapt it to your stack.

💭 Why not self-play with a single model? Using Claude Code subagents or agent teams for both execution and review is technically possible, but tends to fall into local minima — the same model reviewing its own patterns creates blind spots.

Think of it like adversarial vs. stochastic bandits: a single model self-reviewing is the stochastic case (predictable reward noise), while cross-model review is adversarial (the reviewer actively probes weaknesses the executor didn’t anticipate) — and adversarial bandits are fundamentally harder to game.

💭 Why two models, not more? Two is the minimum needed to break self-play blind spots, and 2-player games converge to Nash equilibrium far more efficiently than n-player ones. Adding more reviewers increases API cost and coordination overhead with diminishing returns — the biggest gain is going from 1→2, not 2→4.

Claude Code’s strength is fast, fluid execution; Codex (GPT-6-Astra xhigh) is slower but more deliberate and rigorous in critique. These complementary styles — speed × rigor — produce better outcomes than either model talking to itself.

🧿 Want the strongest possible reviewer? Add — reviewer: oracle-pro to any skill to route reviews through GPT-5.5 Pro via Oracle MCP. Pro-level reasoning for proof verification, experiment auditing, and final stress tests. Works with API key or free browser mode. Setup →

Contents

1. More Than Just a Prompt
2. What’s New · changelog
3. Quick Start · install + first run
4. Features
5. Score Progression (Real Run)
6. Community Showcase — Papers Built with ARIS
7. Awesome Community Skills & Extensions
8. Workflows · 13 named pipelines (W1 / W1.5 / W2 / W3 / W4 / W5 / W6 / Wiki / WM + Effort / Assurance / Oracle)
9. Setup · prerequisites / install / update / usage / GPU server config
10. Customization · per-skill config knobs
11. Alternative Model Combinations · GLM / MiniMax / Kimi / etc.
12. Community
13. Citation
14. Star History
15. Acknowledgements
16. License

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1. 🎯 More Than Just a Prompt

These are full pipelines — you can also use each workflow independently. Already have an idea? Skip to Workflow 1.5. Have results? Jump to Workflow 3. Got reviews? Jump to Workflow 4. Want persistent memory? Enable Research Wiki. See Quick Start for all commands and Workflows for the full breakdown.

Basic mode — give ARIS a research direction, it handles everything:

/research-pipeline "factorized gap in discrete diffusion LMs"

🔥 Targeted mode — got a paper you want to improve? Give ARIS the paper + the code:

/research-pipeline "improve method X" — ref paper: https://arxiv.org/abs/2406.04329, base repo: https://github.com/org/project

ARIS reads the paper → finds its weaknesses → clones the codebase → generates ideas that specifically fix those weaknesses with that code → runs experiments → writes your paper. Like telling a research assistant: “read this paper, use this repo, find what’s missing, and fix it.”

Mix and match: ref paper only = “what can be improved?”, base repo only = “what can I build with this code?”, both = “improve this paper using this code.”

🛠️ Build a feature — already know what to build?

/research-implement-feature "add KV-cache reuse to the decoder" — base repo: https://github.com/org/project

Spine first, then one feature at a time, each with a runnable check. Every choice the request left open is written to an assumption ledger before the code that depends on it, and a different model family then reads the raw diff for the ones that went undeclared. It reports “the checks passed”, never “the method works”. Contributed by @heroarmor.

🔥 Rebuttal mode — reviews just dropped? Don’t panic. ARIS reads every concern, builds a strategy, and drafts a rebuttal that’s grounded, structured, and under the character limit:

/rebuttal "paper/ + reviews" — venue: ICML, character limit: 5000

Three safety gates — rebuttal will NOT finalize if any fails:

• 🔒 No fabrication — every claim maps to paper/review/user-confirmed result
• 🔒 No overpromise — every promise is user-approved
• 🔒 Full coverage — every reviewer concern is tracked

Two outputs: PASTE_READY.txt (exact char count, paste to venue) + REBUTTAL_DRAFT_rich.md (extended version for manual editing).

After acceptance — your paper is in, now prepare the presentation:

/paper-slides "paper/"     # → Beamer PDF + PPTX + speaker notes + Q&A prep
/paper-poster-html "paper/" # → measurement-gated HTML/CSS poster → print-ready PDF

💡 From idea to paper to podium — one toolchain. 🌱

2. 📢 What’s New

⚠️ Any entry that touches skills: bash tools/smart_update.sh --apply pulls it.

• 2026-10-19 — 🔀 Choose reviewers inside the complete Claude Code and Codex skill packs — use — reviewer: or project configuration to select Codex, Gemini/Antigravity, Grok, or Claude Code through your configured reviewer tools. See the routing guide and Codex guide. The Claude Code MCP bridge now respects the requested working directory and records the CLI-reported model, including configured providers such as DeepSeek. Same-family reviews remain provisional. (#473, #472)
• 2026-10-19 — 📚 Catch bibliography field errors with /citation-crosscheck — compare local .bib entries with records freshly fetched from paper sources, then export a side-by-side spreadsheet of author, year, venue, DOI, and arXiv ID discrepancies. Available for Claude Code and Codex, alongside the existing citation audit. (#371; thanks @dty0606)
• 2026-10-19 — 🔬 Bounded parallel work for research workflows — idea generation, literature extraction, and proof checking now share fan-out rules: capped worker batches, coverage records, and sequential fallback when parallel execution is unavailable. Workers collect candidates and evidence; designated reviewers retain final judgments. (#372; thanks @Dennis-Huangm)
• 2026-10-07 — 🧮 From a mathematical statement to a checked Lean proof — new /lean-formalize for Claude Code and Codex. Develop proof routes, connect intermediate lemmas to the original theorem, and deliver reproducible Lean sources with statement and axiom checks. Cross-family reviews target the statement, key arguments, and final assembly. Integrated into five proof/research skills; use it when requested or a concrete proof obligation benefits from formal verification. (#461)
• 2026-10-05 — 🔌 New Grok and Gemini / Antigravity MCP bridges — use your existing CLI login to read files and resume conversations from Claude Code or Codex. (#454)

3. 🚀 Quick Start

# 1. Install skills — project-local symlinks (recommended)
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
bash Auto-claude-code-research-in-sleep/tools/install_aris.sh ~/your-project   # symlinks ARIS skills into /.claude/skills/
# (prefer a global install instead? cp -r Auto-claude-code-research-in-sleep/skills/* ~/.claude/skills/)
# (don't need all 87? --list-groups / --groups X,Y / --skills X — see "Selective install" below)

# 1b. Update later (when upstream changes)
cd Auto-claude-code-research-in-sleep && git pull
bash tools/smart_update.sh --apply   # updates safe skills, flags your personal customizations
# (NEW upstream skills need confirmation — --add-new to accept all non-interactively)

# Optional Codex mirror managed project install
bash tools/install_aris_codex.sh ~/your-codex-project

# Managed Codex project update
cd Auto-claude-code-research-in-sleep && git pull
bash tools/install_aris_codex.sh ~/your-codex-project --reconcile

# Copied Codex installs only (not for projects installed by install_aris_codex.sh)
bash tools/smart_update_codex.sh --local ~/.codex/skills
bash tools/smart_update_codex.sh --local ~/.codex/skills --apply

# 2. Codex reviewer (review skills call GPT through it) — run from the directory you cloned into in step 1
npm install -g @openai/codex && codex login       # one-time ChatGPT login; the reviewer model comes from ~/.codex/config.toml
claude mcp add codex -s user -- python3 "$(pwd)/Auto-claude-code-research-in-sleep/mcp-servers/codex-exec/server.py"
# then restart Claude Code; `claude mcp list` must show:  codex: python3 …/codex-exec/server.py - ✔ Connected

# 2b. Already had ARIS before 2026-09-11? codex-cli 0.154 removed `codex mcp-server`, so the old registration is dead.
cd Auto-claude-code-research-in-sleep && git pull   # brings mcp-servers/codex-exec/ (git pull is enough; smart_update only syncs skills)
claude mcp remove codex -s user
claude mcp add codex -s user -- python3 "$(pwd)/mcp-servers/codex-exec/server.py"
# restart Claude Code, check `claude mcp list` as above. Skills need no change. Do it on codex 0.153 too — it works there and survives the update.
# Cursor / Trae / Antigravity / Copilot CLI: same "codex" key, command python3 + that path — see the adaptation guides in docs/.
# Installed by copying skills/ without a clone? Clone the repo anywhere and point at its server.py; the file is self-contained.

# 3. Use in Claude Code
claude
> /idea-discovery "your research direction"  # Workflow 1 — be specific! not "NLP" but "factorized gap in discrete diffusion LMs"
> /experiment-bridge                         # Workflow 1.5 — have a plan? implement + deploy + collect results
> /auto-review-loop "your paper topic or scope"  # Workflow 2: review → fix → re-review overnight
> /paper-writing "NARRATIVE_REPORT.md"       # Workflow 3: narrative → polished PDF
> /rebuttal "paper/ + reviews" — venue: ICML    # Workflow 4: parse reviews → draft rebuttal → follow-up
> /resubmit-pipeline "paper/" — venue: NeurIPS  # Workflow 5: port a polished paper to a new venue (text-only, no new experiments)
> /paper-talk "paper/" — venue: ICLR            # Workflow 6: paper → Beamer + PPTX talk + speaker notes + assurance audits
> /research-pipeline "your research direction"  # Full pipeline: Workflow 1 → 1.5 → 2 → 3 end-to-end
> /research-wiki init                           # 📚 Enable persistent research memory (one-time)
> /meta-optimize                                # Meta: analyze usage logs → propose skill improvements

Don’t need all 87 skills? See Selective install below for group/skill-level picks.

Optional Grok CLI bridge: grok-exec exposes grok / grok-reply using your existing Grok CLI login, with grok-4.7 + xhigh defaults, persistent sessions, progress and cancellation. It is a separate MCP service; installing it does not change the reviewer routing of existing skills.

Selectable CLI reviewers: antigravity-exec and grok-exec support the complete packs through — reviewer: antigravity / grok; see the backend contract. The legacy — reviewer: agy still uses gemini-review.

See full setup guide for details and alternative model combinations if you don’t have Claude/OpenAI API.

4. ✨ Features

ARIS chains 87 composable skills across the whole research lifecycle — literature & novelty → idea discovery → GPU experiments → autonomous review loop → paper writing → peer review — with cross-model adversarial review (Claude executes · GPT-6-Astra xhigh reviews · optional GPT-5.5 Pro via Oracle), anti-hallucination DBLP/CrossRef citations, a persistent Research Wiki, flexible model backends, human-in-the-loop checkpoints, and optional Feishu / Zotero / Obsidian / GPU integrations.

🔥 And it scales to any agent’s ultracode-style deep mode — the breadth/firepower pass adapts to the runtime (Claude Code ultracode + workflows on Opus 4.8, Codex spawn_agent, or plain sequential), feeding three roles: breadth · cross-model review → accuracy · research wiki → memory. However a loop is driven, it reports to the same cross-model jury + research wiki — it can drive, never acquit.

• 📚 Research Wiki — persistent knowledge base across papers/ideas/experiments/claims. Failed ideas become anti-repetition memory — ARIS gets smarter every run. Inspired by Karpathy’s LLM Wiki
• 🧩 Extensible — domain-specific skills welcome! Add a SKILL.md and open a PR. See community skills like dse-loop (architecture/EDA)

ARIS ships 87+ skills across literature, ideation, experiments, audit, writing, talks, patents, and meta-utilities — the full catalog (role / category / requirements per skill) lives in docs/SKILLS_CATALOG.md to keep this README scannable.

→ Browse all 87 skills by category in the full catalog →

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5. 📈 Score Progression (Real Run)

A real overnight 4-round run on an ML research project — the AI reviewer’s score climbed 5.0/10 (borderline reject) → 7.5/10 (review-ready) as the loop autonomously ran 20+ GPU experiments, rewrote the narrative framing, and killed claims that didn’t hold up, all without human intervention.

6. 🏆 Community Showcase — Papers Built with ARIS

Real projects that used the full ARIS pipeline end-to-end. The scores listed are AI-review signals (CSPaper / Stanford Agentic Reviewer), not venue acceptances — and since ARIS optimizes through AI-review loops, high AI scores are an expected byproduct, not proof of acceptance (human reviewers still bring literature / venue / community judgment an AI reviewer misses). Used ARIS for a paper? Open an issue / PR to be featured!

7. 🧩 Awesome Community Skills & Extensions

Domain-specific skills and external projects contributed by the community. PRs welcome — just add a skills/your-skill/SKILL.md and open a PR!

💡 How to use: Community skills are not auto-wired into core workflows. To use one, ask your executor (Claude Code / OpenClaw / etc.) to read the skill’s SKILL.md, then plug it into the appropriate workflow stage based on the description below.

🎉 Community Skills (15): research-refine · experiment-plan · research-refine-pipeline · grant-proposal · paper-poster (deprecated → paper-poster-html) · paper-slides · mermaid-diagram · proof-writer · comm-lit-review · dse-loop · idea-discovery-robot · formula-derivation · paper-illustration · writing-systems-papers · skills-codex

🌐 External Projects & Docs (16): rosetta · open-source-hardening-skills · CitationClaw · auto-hparam-tuning · paper-to-course · deep-research-skills · Antigravity Adaptation Guide · OpenClaw Adaptation Guide · Cursor Adaptation Guide · Codex+Claude Review Bridge · Trae Adaptation Guide · MiniMax-AI/cli · posterly · Claude Fleet · anti-defensive-writing-Skill · OrcaReplay

🙌 Thanks to every contributor! We fold the tables below to keep the README readable — but every skill and project here is equally valued. PRs always welcome!

8. 🔄 Workflows

These skills compose into a full research lifecycle. Each workflow can be used independently or chained together:

• Exploring a new area (e.g., writing a survey)? Start with Workflow 1 → /idea-discovery
• Have a plan, need to implement and run? Workflow 1.5 → /experiment-bridge
• Already have results, need iterative improvement? Workflow 2 → /auto-review-loop
• Ready to write the paper? Workflow 3 → /paper-writing (or step by step: /paper-plan → /paper-figure → /paper-write → /paper-compile → /auto-paper-improvement-loop)
• Got reviews back? Need to rebuttal? Workflow 4 → /rebuttal — parse reviews, draft safe rebuttal, follow-up rounds
• Full pipeline? Workflow 1 → 1.5 → 2 → 3 → submit → 4 → /research-pipeline + /rebuttal — from idea through submission and rebuttal
• Need a multi-session proof campaign? Workflow 7 → /proof-orchestrator — run-directory proof runs, GPT Pro handoff packages, cross-run continuation
• Want ARIS to remember and learn? 📚 /research-wiki init — persistent memory across sessions. Papers, ideas, failed experiments compound over time
• Want ARIS to improve itself? Workflow M → /meta-optimize — analyze usage logs, propose skill improvements, reviewer-gated

⚠️ Important: These tools accelerate research, but they don’t replace your own critical thinking. Always review generated ideas with your domain expertise, question the assumptions, and make the final call yourself. The best research comes from human insight + AI execution, not full autopilot.

Full Pipeline 🚀

/research-lit → /idea-creator → /novelty-check → /research-refine → /experiment-bridge → /auto-review-loop → /paper-writing → submit → /rebuttal → accept! 🎉
  (survey)      (brainstorm)    (verify novel)   (refine method)   (implement+deploy)  (review & fix)      (write paper)   (send)   (reply to reviewers)
  ├────────────── Workflow 1: Idea Discovery ──────────────┤ ├ Workflow 1.5 ─┤ ├── Workflow 2 ──┤ ├── Workflow 3 ──┤         ├── Workflow 4 ──┤

                                     📚 research-wiki (persistent memory — papers, ideas, experiments, claims)
                                        ↕ reads before ideation, writes after every stage, failed ideas = anti-repetition memory

                                              /meta-optimize (Workflow M — runs independently, improves ARIS itself)
                                                 ↑ reads .aris/meta/events.jsonl (accumulated from all runs above)

Workflow 1: Idea Discovery & Method Refinement 🔍

“What’s the state of the art? Where are the gaps? How do we solve it?”

Don’t have a concrete idea yet? Just give a research direction — /idea-discovery handles the rest:

1. 📚 Survey the landscape (recent papers, open problems, recurring limitations)
2. 🧠 Brainstorm 8-12 concrete ideas via GPT-6-Astra xhigh
3. 🔍 Filter by feasibility, compute cost, and quick novelty search
4. 🛡️ Validate top ideas with deep novelty check + devil’s advocate review
5. 🧪 Pilot top 2-3 ideas in parallel on different GPUs (30 min - 2 hr each)
6. 🏆 Rank by empirical signal — ideas with positive pilot results rise to the top
7. 🔬 Refine the top idea into a problem-anchored proposal via iterative GPT-6-Astra review
8. 🧪 Plan claim-driven experiments with ablations, budgets, and run order

The output is a ranked IDEA_REPORT.md plus a refined proposal (refine-logs/FINAL_PROPOSAL.md) and experiment plan (refine-logs/EXPERIMENT_PLAN.md) for the top idea. Dead-end ideas are documented too, saving future exploration.

Skills involved: research-lit + idea-creator + novelty-check + research-review + research-refine-pipeline

💡 One-command shortcut: /idea-discovery "your research direction" runs this entire workflow automatically.

🔄 Human-in-the-loop: At each AUTO_PROCEED-controlled selection checkpoint, true reports results and continues in the same turn; false asks for your feedback before continuing. If you request changes, tell it what’s missing — it refines the prompt and regenerates. Explicit gates such as Feishu interactive approval remain user-controlled.

⚙️ Pilot experiment budgets (max hours, timeout, GPU budget) are configurable — see Customization.

📝 Blog post: Claude Code 两月 NeurIPS 指北

Workflow 1.5: Experiment Bridge 🔗

“I have a plan. Now implement it, deploy it, and get me initial results.”

Already have an experiment plan (from Workflow 1 or your own)? /experiment-bridge turns it into running code:

1. 📋 Parse the experiment plan (refine-logs/EXPERIMENT_PLAN.md)
2. 💻 Implement experiment scripts (reuse existing code, add proper argparse/logging/seeds)
3. 🔍 GPT-6-Astra code review — cross-model review catches logic bugs before wasting GPU hours (code review: true by default)
4. ✅ Sanity check — run the smallest experiment first to catch runtime bugs
5. 🚀 Deploy full experiment suite to GPU via /run-experiment
6. 📊 Collect initial results and update the experiment tracker

Skills involved: experiment-bridge + run-experiment + monitor-experiment

💡 One-command shortcut: /experiment-bridge reads refine-logs/EXPERIMENT_PLAN.md automatically. Or point it to any plan: /experiment-bridge "my_plan.md".

⚙️ CODE_REVIEW, AUTO_DEPLOY, SANITY_FIRST, MAX_PARALLEL_RUNS are configurable — see Customization.

Workflow 2: Auto Research Loop 🔁 (sleep & wake up to results)

“Review my paper, fix what’s wrong, repeat until it’s good.”

GPT-6-Astra reviews → identifies weaknesses → suggests experiments → Claude Code writes scripts, deploys to GPU, monitors results, rewrites the paper — all while you sleep. Just add your GPU server config to CLAUDE.md.

1. 🔍 Deep review — GPT-6-Astra xhigh reviews the current paper / claims / experiments and identifies weaknesses
2. 🩹 Fix — Claude implements the fixes (rewrites sections, adds baselines, or runs new experiments via /run-experiment); skips any experiment estimated > 4 GPU-hours and flags it for manual follow-up
3. 📊 Re-evaluate — collect results via /monitor-experiment, update paper, feed back to the reviewer
4. 🔁 Repeat — until score ≥ POSITIVE_THRESHOLD (default 6/10) or MAX_ROUNDS (default 4) is hit; if context window fills mid-loop, the workflow auto-resumes from REVIEW_STATE.json

Skills involved: auto-review-loop + research-review + novelty-check + run-experiment + analyze-results + monitor-experiment

💡 One-command shortcut: /auto-review-loop "your paper topic" runs this entire workflow automatically.

⚙️ MAX_ROUNDS, score threshold, and GPU limits are configurable — see Customization.

📝 Blog post: 开源 | 睡觉 Claude 自动跑实验改文

Workflow 3: Paper Writing Pipeline 📝

“Turn my research narrative into a submission-ready PDF.” Requires a local LaTeX environment — see Prerequisites.

1. 📝 Narrate — write NARRATIVE_REPORT.md (claims, experiments, results, figure descriptions); see templates/NARRATIVE_REPORT_TEMPLATE.md
2. 🧭 Plan — /paper-plan builds the claims-evidence matrix + section plan
3. 📊 Figures — /paper-figure generates data-driven plots and comparison tables from JSON/CSV
4. ✍️ Write — /paper-write produces section-by-section LaTeX
5. 🔧 Compile — /paper-compile builds the PDF, fixes errors, runs the page-limit check
6. ✨ Improve — /auto-paper-improvement-loop runs 2 rounds of GPT-6-Astra content review + final format check

Skills involved: paper-plan + paper-figure + paper-write + paper-compile + auto-paper-improvement-loop + (post-acceptance) paper-poster-html + paper-slides

One-command shortcut: /paper-writing "NARRATIVE_REPORT.md" runs this entire workflow automatically.

Input: A NARRATIVE_REPORT.md describing the research: claims, experiments, results, figures. The more detailed the narrative (especially figure descriptions and quantitative results), the better the output.

Output: A paper/ directory with LaTeX source, clean .bib (only cited entries), and compiled PDF. The PDF is labelled submission-ready only when run at — effort: max | beast (or explicit — assurance: submission) and tools/verify_paper_audits.sh reports green on the three mandatory audits (proof-checker, paper-claim-audit, citation-audit); see Assurance Gate below. At the default balanced level, the output is a reviewed draft.

Auto Paper Improvement Loop ✨

After Workflow 3 generates the paper, /auto-paper-improvement-loop runs 2 rounds of GPT-6-Astra xhigh content review → fix → recompile, plus a final format compliance check, autonomously polishing the paper from rough draft to a reviewer-scored draft. Whether the result is tagged submission-ready is decided separately by the Phase 6 assurance gate (see Assurance Gate).

Workflow 4: Rebuttal 📝 (reply to reviewers safely)

“Reviews are in. Help me draft a safe, grounded rebuttal.”

Got reviews back? /rebuttal parses them, builds a strategy, and drafts a venue-compliant response:

1. 📋 Parse — normalize reviews, validate venue rules (character limit, text-only, etc.)
2. 🔍 Atomize — split each review into issue cards (type, severity, reviewer stance)
3. 🗺️ Strategize — global themes, per-reviewer priorities, character budget, blocked claims
4. 🧪 Evidence sprint — if auto experiment: true, auto-run supplementary experiments via /experiment-bridge
5. ✍️ Draft — global opener + numbered per-reviewer responses + closing for meta-reviewer
6. 🛡️ Safety check — 6 lints: coverage, provenance, commitment, tone, consistency, limit
7. 🔬 GPT-6-Astra stress test — internal skeptical review of the draft
8. 📄 Finalize — two outputs: PASTE_READY.txt (exact character count) + REBUTTAL_DRAFT_rich.md (extended version for manual editing)
9. 🔄 Follow-up rounds — delta replies for reviewer discussions, technically escalating

Skills involved: rebuttal

💡 Quick mode: /rebuttal — quick mode: true stops after parsing + strategy (Phase 0-3). See what reviewers want before committing to a full draft.

⚙️ VENUE, AUTO_EXPERIMENT, QUICK_MODE, MAX_STRESS_TEST_ROUNDS are configurable — see Customization.

Three safety gates — rebuttal will NOT finalize if any fails:

• 🔒 Provenance — every claim maps to paper/review/user-confirmed result. No fabrication.
• 🔒 Commitment — every promise is user-approved. No overpromising.
• 🔒 Coverage — every reviewer concern is tracked. Nothing disappears.

Workflow 5: Resubmit Pipeline 🔁 (port a paper to a new venue, text-only)

Port a polished paper from venue A → B under hard, non-overridable guardrails — no new experiments · no bib edits · no framework changes · never overwrites prior submissions — via physical isolation, a 5-layer anonymity check, soft-only audits, whitelist microedits, and a /kill-argument adversarial gate. Full flow + constraints → docs/RESUBMIT_AND_TALK.md

Workflow 6: Conference Talk Pipeline 🎤 (paper → slides → polish → audits)

/paper-talk turns an accepted paper into a talk: outline → /paper-slides (Beamer + PPTX + speaker notes + Q&A) → /slides-polish (per-page Codex visual pass) → optional conference-ready audit gate. Sister to /paper-writing / /paper-poster-html. Full flow → docs/RESUBMIT_AND_TALK.md

Workflow 7: Proof Orchestrator 🧭 (standalone theory track — attack a theorem across days, not turns)

“This proof will take a week and three tools. Keep every attempt, audit, and escalation on the record.”

Hard theorems are campaigns, not chat turns. /proof-orchestrator runs proof work as a stateful, local-first pipeline — each run gets its own directory, completed runs become append-only evidence, and every new run records which prior claims were proved, conjectural, or rejected:

1. 🎯 Freeze the target — exact theorem, assumptions, quantifiers, allowed sources; new run or continuation
2. 🧱 Maintain local evidence — run directory with task.md / materials.md / stable source snapshots
3. 🧠 Attempt locally — full proof, disproof, counterexample, or diagnosis; a stall isolates the smallest hard obligation
4. 🔍 Audit correctness — every lemma, bound, and quantifier checked against assumptions; claims labeled proved / imported / conjectural / repaired / unsupported
5. ✒️ Edit for exposition — two gates: the 7-line notation scorecard (100% core-object retention, zero undefined symbols, zero collisions) and the top-down derivation-structure gate (acyclic dependency graph, no subgoal silently assuming the target — a bug-catcher in disguise: a proof that resists a clean target-first rewrite is usually hiding a gap, and circular reasoning surfaces the moment you try)
6. 📦 GPT Pro handoff — a stalled proof becomes a package: browser-prompt.md (copy-paste ready), source-manifest.md (source roles), handoff.md (upload order + return instructions)
7. 🧾 Re-audit the returned proof — format repair only, then the full correctness audit and exposition edit produce final.md

Skills involved: proof-orchestrator (+ llm-chat MCP for the optional DeepSeek branch)

⚖️ Optional DeepSeek second opinion: request it at the audit stage for a verified cross-family adversarial pass — findings are validated locally and recorded as run-local provisional evidence. An installed call-gpt-pro skill can likewise automate the GPT Pro dispatch when you ask for it on a run.

🧭 Standalone by design. Workflow 7 is theory work’s own track — no Workflow 1–6 stage calls it, and /proof-checker keeps the submission gate in Workflow 3. Reach for it when a theorem outgrows single-shot /proof-writer drafting: multi-day campaigns, cross-run continuation, or when the strongest available prover lives in a browser tab.

💡 Continuation: point it at a prior run (next.md / redo.md) — it reads final.md / audit.md, reuses only audited claims, and opens a fresh run directory with full provenance.

📚 Research Wiki — Persistent Research Memory

“Stop re-deriving. Start compounding.” — inspired by Karpathy’s LLM Wiki

Without the wiki, ARIS is stateless — every /idea-discovery starts from scratch. With the wiki, ARIS accumulates knowledge across the entire research lifecycle: papers read, ideas tested, experiments run, claims verified or invalidated.

The key insight: failed ideas are the most valuable memory. A researcher who knows what doesn’t work generates better ideas than one starting from zero.

Setup:

> /research-wiki init     # one-time, creates research-wiki/ in your project

That’s it. Once initialized, the wiki works automatically.

**Spir