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/meta-optimizeWrote 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/meta-optimize)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/meta-optimize"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/meta-optimize/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/meta-optimize"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/meta-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Agent Snooping · line 232 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 233 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00081 | $0.05821 |
| Opus 5 | $0.00041 | $0.02910 |
| Sonnet 5 | $0.00016 | $0.01164 |
| Haiku 4.5 | $0.00008 | $0.00582 |
Grade A, and why
meta-optimize 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 5d 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 — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Optimize: Outer-Loop Harness Optimization for ARIS
Analyze accumulated usage logs and propose optimizations for: $ARGUMENTS
Privilege boundary — this skill is a READ-ONLY PRODUCER
meta-optimize proposes; it does not land. The mutation of the skill corpus
is the exclusive job of a separate, human-invoked skill: /meta-apply.
This split is structural, not advisory — it is why a missed instruction cannot let
this loop apply its own patch (the self-acquittal failure mode):
- No
Write/Edittool. This skill cannot edit a SKILL.md / shared-reference / any corpus file with the frictionless mutators. Its only outputs are the REPORT and staged patch files, written under.aris/meta/(a scratch area, never the corpus). - No apply step. There is no in-skill "apply the patch" path (see Step 6). The
producer ends by staging approved patches for
/meta-apply; a human must then invoke/meta-applyto land them. That human action is the landing gate. Bashwrites to the corpus are filtered, not impossible — be honest about the layers. What IS fully closed: the accidental / in-flow self-acquittal — this skill has noWrite/Editand no apply step, so an honest run cannot slip into editing the corpus. Defense-in-depth: install thecorpus_write_guardPreToolUse hook (likemeta_logging.json), which DENIES the common Bash shell-writes (>,tee,sed -i,cp/mv,touch,open(...,'w')) to corpus paths. This is a blacklist, NOT a complete sandbox — a deliberately obscured Bash write (git apply,patch,$var/absolute paths, language file APIs) is not all caught. Full structural prevention requires either removing this skill'sBashor an FS sandbox — over-built for a not-yet-load-bearing producer, so deferred to when the gate carries real auto-modification volume (a brick-3 trigger). The intended backstop against a deliberate write is detection, not prevention — a corpus change with no valid/currentprovenancestamp (content-hash mismatch) would be catchable in a pre-push integrity check — but that verifier is NOT yet built (provenance.pyhascontent_hashbut no integrity-check subcommand, and no pre-push hook runs one). So today the deliberate-write case is neither prevented nor actively detected; track the integrity verifier as a follow-up before this producer goes load-bearing. Its legitimate Bash writes go only to.aris/meta/.
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.
- 5d ago Changed 0599dcc7eec1
- 12d ago First seen · 438 lines · 81 tokens per session scan A 1101b4a43e3a
meta-optimize is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (16,030 stars, last pushed yesterday), licensed MIT. It adds 81 tokens to every session and 5,821 once invoked, about $0.0004 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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