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 zhousodo/paper-submission-check --skill paper-multi-round-reviewgit clone --depth 1 https://github.com/zhousodo/paper-submission-checkWrote 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/zhousodo/paper-submission-check/paper-multi-round-review)<a href="https://agentmods.dev/skills/zhousodo/paper-submission-check/paper-multi-round-review"><img src="https://agentmods.dev/badge/skills/zhousodo/paper-submission-check/paper-multi-round-review/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/zhousodo/paper-submission-check/paper-multi-round-review"><img src="https://agentmods.dev/badge/skills/zhousodo/paper-submission-check/paper-multi-round-review.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 | $0.00144 | $0.03108 |
| Opus 5.5 | $0.00058 | $0.01243 |
| Sonnet 5.5 | $0.00029 | $0.00622 |
| Haiku 4.5 | $0.00014 | $0.00311 |
Grade A, and why
paper-multi-round-review 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 2mo 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 — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Round Academic Paper Peer Review
Simulate a rigorous peer review process with multiple specialized reviewers to identify weaknesses and improve paper quality before submission. Modeled after top venues: NeurIPS, ICLR, USENIX Security, CCS, NDSS, IEEE S&P.
Multi-model design: This skill is designed for cross-model review diversity. Using a single model for all reviewer roles creates echo chamber effects — see multi-model-strategy.md for the rationale and model assignment guide.
Workflow Overview
Multi-Round Review Progress:
- [ ] Phase 0: Paper Intake (read paper, identify venue/domain, select reviewer panel)
- [ ] Phase 1: Independent Reviews (4 reviewers in parallel)
- [ ] Phase 2: Meta-Review Synthesis (AC aggregates, identifies consensus/divergence)
- [ ] Phase 3: Author Response Guidance (draft rebuttal strategy)
- [ ] Phase 4: Revision Execution (implement changes with diff tracking)
- [ ] Phase 5: Re-Review (reviewers reassess, update scores)
- [ ] Phase 6: Final Polish (handoff to paper-submission-check skill if available)
Phase 0: Paper Intake & Model Assignment
Before reviewing, gather context and plan model allocation:
- Read the full paper (all .tex files and .bib)
- Identify target venue type:
- Security conference (USENIX, CCS, NDSS, S&P) → emphasize threat model, real-world applicability
- ML conference (NeurIPS, ICLR, ICML) → emphasize novelty, theoretical grounding, ablations
- Journal (IEEE TDSC/TIFS, ACM TOPS) → emphasize completeness, reproducibility
- Cross-domain (ML + Security) → apply BOTH sets of criteria
- Detect paper domain: APT/intrusion detection, malware, vulnerability, privacy, etc.
- Count pages and check against venue limit
- Assign models to roles — see multi-model-strategy.md for recommended assignments. If only one model is available, see the single-model mitigation protocol in that file
Phase 1: Independent Reviews
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 271 lines · 144 tokens per session scan A bb0d12c638a7
paper-multi-round-review is a skill published in the GitHub repository zhousodo/paper-submission-check (24 stars, last pushed 6mo ago), licensed MIT. It adds 144 tokens to every session and 3,108 once invoked, about $0.0006 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.
paper-multi-round-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paper-multi-round-reviewzhousodo/paper-submission-check | Skill | 24 | 6mo ago | MIT | A | 144 | Codex |
| hr-onboardingnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 62 | Claude Code, Codex |
| book-mirrorgarrytan/gbrain | Skill | 32k | yesterday | MIT | A | 138 | Claude Code |
| exam-forecastanthropics/claude-for-legal | Skill | 9.9k | 3d ago | Apache-2.0 | A | 73 | Claude Code |
| lean-formalizewanshuiyin/Auto-claude-code-research-in-sleep | Skill | 19k | 3d ago | MIT | A | 81 | Claude Code |
| eli5companion-inc/feynman | Skill | 10k | 6d ago | MIT | A | 63 | Claude Code, Codex |
| deck-course-modulenexu-io/html-anything | Skill | 9.4k | 25d ago | Apache-2.0 | A | 25 | Claude Code, Codex |