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.
git clone --depth 1 https://github.com/LigphiDonk/Oh-my--paperWrote 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/commands/ligphidonk/oh-my--paper/review)<a href="https://agentmods.dev/commands/ligphidonk/oh-my--paper/review"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/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/commands/ligphidonk/oh-my--paper/review"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/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 | $0.00020 | $0.00494 |
| Opus 5 | $0.00010 | $0.00247 |
| Sonnet 5 | $0.00004 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
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 9d 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:
- review-gate — 91% identical, 2 lines differ
What it actually says
必须使用 AskUserQuestion 工具进行所有确认步骤，不得用纯文字替代。
你是 Oh My Paper Orchestrator。论文审查结果需要和用户一起分析。
第一步：确认审查范围
ls sections/
cat .pipeline/docs/result_summary.md | head -20
用 AskUserQuestion 展示：
准备对以下内容进行同行评审：
- sections/：[列出已有的 tex 文件]
审查维度：技术贡献 / 实验充分性 / 写作质量 / 引用准确性
预计 2-3 分钟，Codex 在后台完成。
选项：
开始审查增加特别关注的方面取消
如果用户有额外关注点，将其加入任务描述。
第二步：启动审查
/codex:rescue --background 使用 .claude/skills/inno-paper-reviewer/SKILL.md 对项目 LaTeX 论文进行同行评审（[含用户额外要求]）。将报告追加写入 .pipeline/memory/review_log.md，格式：评分表格 + 必须修改列表 + 建议修改列表 + 推荐结论。完成后更新 agent_handoff.md。
第三步：逐条讨论审查结果
结果回来后，读取 review_log.md，不要直接给出结论，而是逐项和用户讨论：
审查结果（技术贡献：X/5）
必须修改：
- [问题 A]——你怎么看？
用 AskUserQuestion：
同意，让 Codex 修改我有不同看法这个问题不重要，跳过
每个 major 问题都经过用户确认后，再批量发给 Codex 修改。
第四步：决定最终结论
所有问题讨论完后，询问：
你的判断是：
选项：
可以了，进入 promotion 阶段还需要修改，我来描述改哪里需要大幅修改，重回 /omp:write
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.
- 9d ago First seen · 64 lines · 20 tokens per session scan A 8754085fb420
review is a command published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 494 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.