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 system-profilegit 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/system-profile)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/system-profile"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/system-profile/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/system-profile"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/system-profile.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 | $0.00042 | $0.01020 |
| Opus 5 | $0.00021 | $0.00510 |
| Sonnet 5 | $0.00008 | $0.00204 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
system-profile 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 7d 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:
- system-profile — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Profile
Profile the specified target and summarize the results. Target: $ARGUMENTS
Instructions
You are a profiling assistant. Based on the user's target, choose appropriate profiling strategies, including writing instrumentation code when needed, then run profiling, analyze results, and produce a summary.
Step 1: Determine the profiling target
Parse $ARGUMENTS to understand what to profile. Examples:
- A Python script or module
- A running process (PID or service name)
- A specific function or code block
- An entire framework or system (e.g., "autogen", "vllm serving") — profile its end-to-end execution, identify bottlenecks across components
- "gpu" / "interconnect" / "memory" for focused profiling
If $ARGUMENTS is empty or unclear, ask the user.
Step 2: Choose profiling methods
Select from external tools and/or code instrumentation as appropriate. Don't limit yourself to the examples below — use whatever makes sense for the target.
External tools (check availability first):
- CPU:
cProfile,py-spy,line_profiler,perf stat,/usr/bin/time -v - Memory:
tracemalloc,memory_profiler,memray - GPU:
nvidia-smi,nvidia-smi dmon,nvitop,torch.profiler,nsys - Interconnect:
nvidia-smi topo -m,nvidia-smi nvlink,NCCL_DEBUG=INFO - System:
strace -c,iostat,vmstat
Code instrumentation — when external tools are insufficient, write and insert profiling code into the target. Typical scenarios:
- Timing specific code blocks (wall time vs CPU time)
- Measuring CPU-GPU or GPU-GPU transfer size, frequency, and bandwidth
- Tracking memory allocation across CPU and GPU to detect redundancy
- Wrapping NCCL collectives to measure latency and throughput
- Adding CUDA event timing around kernels
Design the instrumentation based on what you observe in the code — don't use a fixed template.
Step 3: Key dimensions to investigate
Depending on the target, focus on some or all of these:
CPU overhead
- Context switching (voluntary / involuntary)
- CPU utilization: ratio of CPU time to wall time
- Per-function execution time hotspots
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.
- 7d ago First seen · 104 lines · 42 tokens per session scan A 9d31e32d2a63
system-profile 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 42 tokens to every session and 1,020 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 2026-09-03.
Other skills, from other repositories
codex
Multi-AI adversarial fallback chain — three tiers: Codex CLI (primary), OpenCode/GLM5 (fallback), Claude subagent (last resort). Three modes: review (code review with pass/fail gate), challenge (adversarial — tries to break your code), consult (ask anything with session continuity). Use when asked to "codex review"…
code-review
Performs structured code review on a file or directory.
debugging
Systematic debugging of issues — use when a test fails, runtime error occurs, unexpected behavior is reported, or an awareness tick produces anomalous results.
obstacle-resolution
Resolve obstacles using fallback chains — use when an approach fails, a dependency is unavailable, an API returns errors, or a task is blocked and needs an alternative path forward.
chimera-carry-the-failure-forward
A retry loop that overwrites its feedback variable shows attempt 3 only the failure of attempt 2 — so it re-derives the patch attempt 1 already tried.
chimera-ground-it-in-the-source
Take the signature from the installed version, not from memory — a plausible API is indistinguishable from a real one until it runs.