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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill embodiment-descriptiongit clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleepWrote 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/embodiment-description)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/embodiment-description"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/embodiment-description/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/embodiment-description"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/embodiment-description.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00048 | $0.01500 |
| Opus 5 | $0.00024 | $0.00750 |
| Sonnet 5 | $0.00010 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
embodiment-description 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 11d 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:
- embodiment-description — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embodiment Description
Write detailed embodiments for: $ARGUMENTS
Embodiments describe HOW to make and use the invention -- they are the patent equivalent of experiment sections, but describe the invention rather than evaluating it empirically.
Constants
MIN_EMBODIMENTS = 1— At least one complete embodiment requiredMAX_EMBODIMENTS = 3— Practical limit; more embodiments strengthen enablementEMBODIMENT_STYLE = detailed—detailed(full working example) oroutline(sketch)REFERENCE_NUMERAL_PREFIX = 100— Starting reference numeral for first figure's components
Inputs
patent/INVENTION_DISCLOSURE.md— invention decomposition (core/supporting/optional features)patent/CLAIMS.md— drafted claims that the embodiments must support- User-provided figures (if any) in any directory
patent/figures/numeral_index.mdif it exists (from/figure-description)
Workflow
Step 1: Plan Embodiments
For each claim category (method, system, etc.), plan at least one embodiment:
| Embodiment | Covers Claims | Type | Key Variations |
|---|---|---|---|
| 1 | Claims 1, X | Best mode / preferred | [primary implementation] |
| 2 | Claims 2, 3 | Alternative | [different parameters/materials] |
| 3 | Claims 4, 5 | Additional alternative | [different configuration] |
Step 2: Write Each Embodiment
For each embodiment, write a detailed description following this structure:
Opening paragraph: "In one embodiment, [invention summary with reference to what is being described]."
Component/step-by-step description:
For method embodiments:
- Describe each step in order
- Reference figure numerals: "As shown in FIG. 1, at step 202, the processor 102 receives the input data 104..."
- Include specific parameters, ranges, and conditions
- Describe what happens at each decision point
For system/apparatus embodiments:
- Describe each component
- Reference figure numerals: "Referring to FIG. 1, the system 100 comprises a processor 102, a memory 104, and a communication interface 106..."
- Describe interconnections between components
- Describe operation of the system step-by-step
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.
- 11d ago First seen · 130 lines · 48 tokens per session scan A 8833b4569435
embodiment-description is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 1,500 once invoked, about $0.0002 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.
Other skills, from other repositories
privacy-by-design
Use when building apps that collect user data. Ensures privacy protections are built in from the start—data minimization, consent, encryption.
ethics-committee
Act as a research ethics committee — stress-test a protocol the way an IRB / REC / HREC would. Reviews informed consent, risk-benefit balance, vulnerable populations, data privacy, deception, debriefing, payment, dual-use risks, AI/LLM use in research, and equity in recruitment. Produces a committee-style decision…
freelance-dev-sow
When to use. A potential client wants a fixed-scope software project and needs a one-page SOW before they sign + pay deposit.
advogado-criminal
Advogado criminalista especializado em Maria da Penha, violencia domestica, feminicidio, direito penal brasileiro, medidas protetivas, inquerito policial e acao penal.
advogado-especialista
Advogado especialista em todas as areas do Direito brasileiro: familia, criminal, trabalhista, tributario, consumidor, imobiliario, empresarial, civil e constitucional.
regulatory-research-fallback
Fallback workflow for regulatory research when web extraction tools fail on government PDFs.