ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleepnpx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robot)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robot"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robot/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robot"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/idea-discovery-robot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.03390 |
| Opus 5 | $0.00052 | $0.01695 |
| Sonnet 5 | $0.00021 | $0.00678 |
| Haiku 4.5 | $0.00010 | $0.00339 |
Grade A, and why
idea-discovery-robot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- idea-discovery-robot — 95% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robotics Idea Discovery Pipeline
Orchestrate a robotics-specific idea discovery workflow for: $ARGUMENTS
Overview
This skill chains four sub-skills into a single automated pipeline:
/research-lit → /idea-creator (robotics framing) → /novelty-check → /research-review
(survey) (filter + pilot plan) (verify novel) (critical feedback)
But every phase must be grounded in robotics-specific constraints:
- Embodiment: arm, mobile manipulator, drone, humanoid, quadruped, autonomous car, etc.
- Task family: grasping, insertion, locomotion, navigation, manipulation, rearrangement, multi-step planning
- Observation + action interface: RGB/RGB-D/tactile/language; torque/velocity/waypoints/end-effector actions
- Simulator / benchmark availability: simulation-first by default
- Real robot constraints: hardware availability, reset cost, safety, operator time
- Evaluation quality: success rate plus failure cases, safety violations, intervention count, latency, sample efficiency
- Sim2real story: whether the idea can stay in sim, needs offline logs, or truly requires hardware
The goal is not to produce flashy demos. The goal is to produce ideas that are:
- benchmarkable
- falsifiable
- feasible with available robotics infrastructure
- interesting even if the answer is negative
Constants
- MAX_PILOT_IDEAS = 3 — Validate at most 3 top ideas deeply
- PILOT_MODE =
sim-first— Prefer simulation or offline-log pilots before any hardware execution - REAL_ROBOT_PILOTS =
explicit approval only— Never assume physical robot access or approval - AUTO_PROCEED = true — If user does not respond at checkpoints, proceed with the best sim-first option
- REVIEWER_MODEL =
gpt-6-astra— External reviewer model via Codex MCP - TARGET_VENUES = CoRL, RSS, ICRA, IROS, RA-L — Default novelty and reviewer framing
Override inline, e.g.
/idea-discovery-robot "bimanual manipulation" — only sim ideas, no real robotor/idea-discovery-robot "drone navigation" — focus on CoRL/RSS, 2 pilot ideas max
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed 945decb901aa
- 12d ago First seen · 364 lines · 104 tokens per session scan A 4533db8a0aa8
idea-discovery-robot is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (16,030 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 3,390 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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