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/research-plan)<a href="https://agentmods.dev/commands/ligphidonk/oh-my--paper/research-plan"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/research-plan/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/research-plan"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/research-plan.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 | $0.00018 | $0.00447 |
| Opus 5 | $0.00009 | $0.00224 |
| Sonnet 5 | $0.00004 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
research-plan 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.
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。先全面读取项目状态，再和用户一起决定接下来做什么。
第一步：读取完整状态
cat .pipeline/memory/project_truth.md
cat .pipeline/memory/orchestrator_state.md
cat .pipeline/tasks/tasks.json
cat .pipeline/memory/review_log.md
cat .pipeline/docs/research_brief.json
cat .pipeline/memory/experiment_ledger.md
cat .pipeline/memory/decision_log.md
第二步：生成状态摘要，和用户对话
用 AskUserQuestion 展示项目当前状态：
项目：[主题] 当前阶段：[stage] — 进度 [X/Y 任务完成]
最近进展：[1-2句话]
待解决：[阻塞项或待审报告，如有]
建议下一步：[你认为最合适的下一步]
选项（根据阶段动态生成）：
按建议继续：[具体下一步]我有其他想法先看看详细的任务列表推进到下一阶段
第三步：根据用户选择行动
- 选择继续：准备
execution_context.md，建议使用对应的命令（/delegate、/survey-blitz等） - 选择调整：
AskUserQuestion进一步了解想法，更新计划 - 选择查看任务：列出当前阶段所有任务及状态
最后：更新状态文件
{
"updates": [
{
"file": "orchestrator_state.md",
"content": "（更新后的状态）"
},
{
"file": "execution_context.md",
"content": "（为下一步准备的任务包）"
}
]
}
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 · 62 lines · 18 tokens per session scan A 206cca6c64ba
research-plan is a command published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 447 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.