ARIS

Use this skill when the user names ARIS, Auto-claude-code-research-in-sleep, Auto Research in Sleep, ARIS-Code, ARIS skills, ARIS research workflows, ARIS installer scripts, ARIS MCP reviewer backends, ARIS research-wiki/watchdog tooling, or asks how to run or maintain the ARIS skill corpus.

ARIS is a skill-based ML research automation harness. It coordinates a writing/execution agent with independent reviewers, persistent research state, GPU experiment operations, and audit gates through Markdown SKILL.md workflows and small helper scripts.

Route By Task

• Install, update, or distribute ARIS skills: use sub-skills/install-and-distribution/SKILL.md for Claude Code, Codex CLI, Copilot CLI, selective installs, manifest reconciliation, project config blocks, and install troubleshooting.
• Choose ARIS workflows or slash skills: use sub-skills/workflow-routing-and-skill-catalog/SKILL.md for W1-W6 research workflow routing, skill groups, common parameters, Codex mirrors, and artifact handoffs.
• Configure reviewer/provider backends: use sub-skills/review-and-provider-backends/SKILL.md for Codex MCP, Claude/Gemini overlays, generic OpenAI-compatible LLM servers, MiniMax, ModelScope, manual review, Feishu/Lark, and cross-model review invariants.
• Recover state or run/monitor experiments: use sub-skills/state-recovery-and-experiment-ops/SKILL.md for research-wiki/, pipeline status, session recovery, hooks, watchdog tasks, experiment queues, and GPU/remote-server operating cautions.
• Edit or verify the ARIS repository itself: use sub-skills/repository-maintenance/SKILL.md for skill-catalog integrity, helper-resolution linting, Codex mirrors/overlays, provenance rules, focused tests, and safe contribution checks.

Fast Start

1. Identify the host platform: Claude Code, Codex CLI, GitHub Copilot CLI, Cursor/Trae/Antigravity, or standalone ARIS-Code.
2. Decide whether the user is trying to use ARIS in a research project or modify the ARIS repository. Project-use tasks route to install/workflow/provider/state sub-skills; source edits route to repository maintenance.
3. ARIS is not a top-level pip-installable Python package. For a project install, use the official installer from the user's ARIS checkout or release in dry-run mode first, then follow references/install-distribution-reference.md before applying changes.
4. Keep external backends explicit. Codex, Claude, Gemini, MiniMax, ModelScope, Feishu/Lark, Overleaf, LaTeX, remote SSH, Vast, Modal, and GPUs are optional runtime integrations that need their own credentials or host tools.
5. For safety, prefer read-only diagnostics before running mutating installers or long-running experiments. The bundled scripts in scripts/ are read-only helpers.

Minimal verification from this generated skill (run from the generated aris/ directory, or replace scripts/... with that skill directory's path):

python scripts/aris_project_doctor.py --project /path/to/research-project

Bundled References and Scripts

• references/capability-map.md maps ARIS capabilities to the owning sub-skill and source evidence used to distill them.
• references/install-distribution-reference.md summarizes install layouts, manifests, and update choices across platforms.
• references/helper-resolution-and-project-files.md describes helper lookup, project files, artifact contracts, and state files.
• references/troubleshooting.md covers cross-cutting failures: missing skills, stale installs, reviewer/backend mismatch, helper lookup, optional dependency, and state-recovery problems.
• references/repo-provenance.md records the source revision and extraction baseline.
• scripts/aris_project_doctor.py performs read-only checks on a target research project for ARIS installation and state indicators.
• scripts/aris_helper_resolver.py prints the helper path selected by the ARIS helper-resolution chain for a target project.

Avoid

• Do not treat same-family Codex self-review as cross-model acceptance; mark it provisional unless an independent reviewer or deterministic verifier supplies the acceptance gate.
• Do not claim that live reviewer APIs, Feishu/Lark, Overleaf, LaTeX, or GPU backends are working unless the current environment has been explicitly checked.
• Do not run ARIS installers, update scripts, SSH/GPU jobs, or MCP servers without user intent; they can mutate projects, start services, or require credentials.
• Do not use this skill for generic literature review, GPU rental, paper writing, or patent drafting unless the task is specifically about ARIS's implementation or routing of those workflows.