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/research-implement-featureWrote 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/research-implement-feature)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-implement-feature"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-implement-feature/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/research-implement-feature"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-implement-feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00120 | $0.08079 |
| Opus 5.5 | $0.00048 | $0.03232 |
| Sonnet 5.5 | $0.00024 | $0.01616 |
| Haiku 4.5 | $0.00012 | $0.00808 |
Grade A, and why
research-implement-feature 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.
How it starts
The opening of the file, as written. The whole thing — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Implement: Feature
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.
Build: $ARGUMENTS
This skill exists for one request shape — "just implement X for me" — where the user has a capability in mind, not an experiment plan, and does not want to be interviewed about it first.
It resolves that request the only honest way: stay autonomous, stop being silent. The skill never blocks to ask permission; it declares every decision the request left open, in a ledger, at the moment it makes it, and then a different model family goes looking for the ones it forgot to declare.
Two invariants
-
Declare before you act. The instant a decision is under-determined by the request and changes an interface or a meaning, it gets a ledger row — before the code that depends on it exists. A ledger reconstructed at the end of the run is not a ledger, it is a changelog, and it systematically omits exactly the assumptions the author stopped noticing.
Under
ASK=semantic, this invariant strengthens to ask before you act for thesemanticclass: the ledger row is the unit of ambiguity, so a row that would have been written silently is a question that gets asked first. -
Spine before features. Rung F0 is a walking skeleton: the thinnest path from real entry point to real artifact, with stubs inside. It must run before any feature is added. Features are then added one rung at a time, each with its own acceptance check, each leaving every earlier rung green.
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 · +11 lines a20e019ace93
- 1mo ago Changed · +2 lines 453416dee9d1
- 1mo ago First seen · 582 lines · 120 tokens per session scan A 26a6a8c1e3c6
research-implement-feature is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,176 stars, last pushed 3d ago), licensed MIT. It adds 120 tokens to every session and 8,079 once invoked, about $0.0005 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 2026-09-16.
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