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/invention-structuringWrote 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/invention-structuring)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/invention-structuring"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/invention-structuring/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/invention-structuring"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/invention-structuring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00056 | $0.01742 |
| Opus 5.5 | $0.00022 | $0.00697 |
| Sonnet 5.5 | $0.00011 | $0.00348 |
| Haiku 4.5 | $0.00006 | $0.00174 |
Grade A, and why
invention-structuring 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 8d 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:
- invention-structuring — 92% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invention Structuring
Before each reviewer slot, follow Selectable Reviewers: resolve invocation →
.aris/reviewer.json→ Claude Code default (Codex MCP) withreviewer_route.pythrough the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only tobackend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections areREVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.
Structure the invention into a formal disclosure based on: $ARGUMENTS
Adapted from the refinement pattern in /research-refine for patent invention decomposition.
Constants
REVIEWER_MODEL = gpt-6-astra— External reviewer for invention decomposition validationMAX_REFINEMENT_ROUNDS = 3— Maximum structuring iterations
Inputs
- Invention description from
$ARGUMENTS patent/INVENTION_BRIEF.mdif existspatent/PRIOR_ART_REPORT.md— prior art landscapepatent/NOVELTY_ASSESSMENT.md— novelty analysis
Shared References
Load ../shared-references/patent-writing-principles.md for the Problem-Solution-Advantage framework and claimable subject matter guidelines.
Workflow
Step 1: Problem-Solution-Advantage Framework
Structure the invention using the universal patent framework:
Technical Problem (要解决的技术问题):
- Derived from prior art deficiencies identified in NOVELTY_ASSESSMENT.md
- Must be a specific, technical problem (not a commercial or social problem)
- Statement format: "The technical problem to be solved is how to [specific technical objective] given [specific technical constraint]."
Technical Solution (技术方案):
- The invention's specific technical contribution
- Focus on the mechanism, not the result
- Must be described at a level that matches the intended claim scope
- Identify which features are known vs. inventive
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.
- 8d ago Changed · +13 lines 551c6d34c7a3
- 1mo ago Changed ced6627e44e9
- 1mo ago First seen · 189 lines · 56 tokens per session scan A bc0ac16349e9
invention-structuring is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,176 stars, last pushed 4d ago), licensed MIT. It adds 56 tokens to every session and 1,742 once invoked, about $0.0002 per session on Opus 5.5. 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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