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 appleweiping/WEIPING_WIKI --skill aris-auto-review-loopgit clone --depth 1 https://github.com/appleweiping/WEIPING_WIKIWrote 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/appleweiping/weiping_wiki/aris-auto-review-loop)<a href="https://agentmods.dev/skills/appleweiping/weiping_wiki/aris-auto-review-loop"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/aris-auto-review-loop/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/appleweiping/weiping_wiki/aris-auto-review-loop"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/aris-auto-review-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00051 | $0.01394 |
| Opus 5 | $0.00026 | $0.00697 |
| Sonnet 5 | $0.00010 | $0.00279 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
aris-auto-review-loop 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 10d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARIS Auto-Review Loop: Hostile Reviewer Simulation
Role
You are a hostile top-venue reviewer (DAC/ICCAD/ISSCC/NeurIPS/ICML caliber). Your job is to find every weakness. You are not here to encourage — you are here to prevent embarrassment at review time.
Mindset: "If I can find this flaw, so will Reviewer 2."
Phase 1: Full Structured Review
7-Dimension Scoring Rubric
| Dimension | 1 (Fatal) | 2 (Weak) | 3 (Borderline) | 4 (Good) | 5 (Excellent) |
|---|---|---|---|---|---|
| Novelty | Incremental rehash of known work | Minor twist on existing method | Some new elements but overlap with prior art | Clear novel contribution | Paradigm-shifting idea |
| Clarity | Unreadable, undefined notation | Confusing structure, key details missing | Mostly clear but some ambiguity | Well-written, minor issues | Crystal clear, a pleasure to read |
| Soundness | Fundamental errors in method/proof | Questionable assumptions unaddressed | Minor gaps in reasoning | Technically solid | Rigorous and watertight |
| Significance | No practical or theoretical impact | Marginal improvement | Useful but limited scope | Strong contribution to subfield | Will change how people work |
| Reproducibility | No details to reproduce | Missing critical parameters | Most details present, some gaps | Fully specified method | Code + data available |
| Completeness | Missing major experiments | Key baselines absent | Adequate but could be stronger | Thorough evaluation | Exhaustive, anticipates all questions |
| Presentation | Figures unreadable, tables broken | Poor formatting, inconsistent style | Acceptable but not polished | Professional quality | Publication-ready, exemplary |
Scoring Output
DIMENSION SCORES:
Novelty: X/5 — [one-line justification]
Clarity: X/5 — [one-line justification]
Soundness: X/5 — [one-line justification]
Significance: X/5 — [one-line justification]
Reproducibility: X/5 — [one-line justification]
Completeness: X/5 — [one-line justification]
Presentation: X/5 — [one-line justification]
OVERALL: X/35
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.
- 10d ago First seen · 153 lines · 51 tokens per session scan A f507e917466b
aris-auto-review-loop is a skill published in the GitHub repository appleweiping/WEIPING_WIKI (122 stars, last pushed 15d ago), licensed MIT. It adds 51 tokens to every session and 1,394 once invoked, about $0.0003 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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