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 agentmods add commands/ligphidonk/oh-my--paper/syncgit 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/sync)<a href="https://agentmods.dev/commands/ligphidonk/oh-my--paper/sync"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/sync.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01157 |
| Opus 5 | $0.00010 | $0.00579 |
| Sonnet 5 | $0.00004 | $0.00231 |
| Haiku 4.5 | $0.00002 | $0.00116 |
Grade A, and why
sync 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 4d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是 Oh My Paper Conductor。用户调用此命令是因为进度文档没有及时更新。你的任务是全面重建三个核心进度文档，使其准确反映当前真实状态。
第一步：读取所有原始数据
一次性读取所有状态文件，获取完整上下文：
cat .pipeline/tasks/tasks.json
cat .pipeline/memory/project_truth.md
cat .pipeline/memory/orchestrator_state.md
cat .pipeline/memory/execution_context.md
cat .pipeline/memory/experiment_ledger.md
cat .pipeline/memory/decision_log.md
cat .pipeline/memory/literature_bank.md
cat .pipeline/memory/agent_handoff.md
cat .pipeline/memory/review_log.md
cat .pipeline/docs/research_brief.json
第二步：向用户确认遗漏的进展
用 AskUserQuestion 询问：
进度同步
我已读取所有文件，准备重建进度文档。
请简述一下文档中没有记录但实际已完成的事情（如果有）：
- 例：「跑完了 baseline 实验，accuracy 83%」
- 例：「调整了研究方向，改为专注 X 方法」
- 例：「没有遗漏，只是文档没更新」
选项：
没有遗漏，直接从现有文件同步有遗漏，我来描述
如果用户选"有遗漏"，用纯文字追问具体内容，收集后再继续。
第三步：重建 project_truth.md
综合所有信息，完整重写 project_truth.md，结构如下：
# Project Truth
_最后同步：[ISO 日期时间]_
## 研究主题
[来自 research_brief.json]
## 当前阶段
[currentStage] — 总体进度：[X/Y 任务完成]
## 已确认决策
（来自 decision_log.md，每条一行，格式：[日期] 决策内容）
## 阶段进展摘要
### Survey
[完成的文献调研成果，如果有]
### Ideation
[已评估的 idea，选定方向]
### Experiment
[实验结果摘要，包括最佳结果和关键结论]
### Publication
[写作进展，已完成章节]
## 当前最佳实验结果
[来自 experiment_ledger.md 的最优结果，格式：指标名 = 值（实验ID，日期）]
## 方向调整记录
[任何研究方向的变化，按时间排序]
## 风险 / 阻塞项
[当前阻塞或高风险项]
第四步：重建 orchestrator_state.md
完整重写 orchestrator_state.md：
# Orchestrator State
_最后同步：[ISO 日期时间]_
## 全局进度看板
| 阶段 | 状态 | 完成/总计 | 备注 |
|------|------|----------|------|
| Survey | [done/active/pending] | X/Y | |
| Ideation | [done/active/pending] | X/Y | |
| Experiment | [done/active/pending] | X/Y | |
| Publication | [done/active/pending] | X/Y | |
## 当前活跃任务
[列出 tasks.json 中所有 status=in-progress 的任务]
## 最近完成任务（最近5条）
[tasks.json 中最近 done 的任务，含 updatedAt]
## 决策点
[需要 Conductor 做决定的事项，来自 agent_handoff.md 或 review_log.md]
## 下一步建议
[基于当前状态，最合理的下一步行动]
第五步：重建 execution_context.md
完整重写 execution_context.md：
# Execution Context
_最后同步：[ISO 日期时间]_
## 当前任务
**ID:** [当前 in-progress 任务 ID，无则填"待分配"]
**标题:** [任务标题]
**状态:** [in-progress / 待分配]
**详细说明:**
[任务要求]
## 决策树（本轮实验）
[来自已有 execution_context 或 experiment_ledger 的实验决策逻辑]
## 最终评估配置
[当前或最近一次实验的完整配置参数]
## 上下文积累诊断
[本任务已积累的关键上下文，帮助执行者避免重复踩坑]
## 待处理的 Agent 反馈
[来自 agent_handoff.md 中还未处理的交接项]
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.
- 4d ago First seen · 162 lines · 19 tokens per session scan A ddd425281db5
sync is a command published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 1,157 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
git
Git operations with intelligent commit messages and workflow optimization.
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.