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/survey-blitz)<a href="https://agentmods.dev/commands/ligphidonk/oh-my--paper/survey-blitz"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/survey-blitz/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/survey-blitz"><img src="https://agentmods.dev/badge/commands/ligphidonk/oh-my--paper/survey-blitz.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 | $0.00021 | $0.00492 |
| Opus 5 | $0.00010 | $0.00246 |
| Sonnet 5 | $0.00004 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
survey-blitz 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。执行文献调研前先和用户对齐方向。
第一步：读取研究主题
cat .pipeline/memory/project_truth.md
cat .pipeline/docs/research_brief.json
cat .pipeline/memory/literature_bank.md # 查看已有多少文献
第二步：展示搜索计划，等待确认
用 AskUserQuestion 展示：
准备搜索以下方向的文献：
- [方向 A]（关键词：...）
- [方向 B]（关键词：...）
- [方向 C]（关键词：...）
目标：约 20-30 篇，已有 X 篇 技能：inno-deep-research + paper-finder
选项：
确认，开始搜索调整搜索方向只搜某个方向
如果用户选择调整，AskUserQuestion 询问具体方向修改，更新后再确认一次。
第三步：执行搜索（仅在确认后）
/codex:rescue --background 阅读 .pipeline/memory/project_truth.md 获取研究主题。使用 .claude/skills/inno-deep-research/SKILL.md 搜索以下方向：[确认后的方向列表]。将论文逐条追加到 .pipeline/memory/literature_bank.md（格式：| DOI/URL | Title | Year | Venue | Relevance | accepted | Date |）。完成后生成 .pipeline/docs/gap_matrix.md 分析研究空白，并更新 agent_handoff.md。
用 /codex:status 等待完成。
第四步：展示结果摘要
结果回来后告诉用户：
- 新增了多少篇（总计多少篇）
- 主要覆盖了哪些方向
- gap_matrix.md 找到了哪几个研究空白
用 AskUserQuestion 询问：
够了，进入 /idea-forge还需要补充搜索某个方向看看 gap_matrix 后再决定
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 · 56 lines · 21 tokens per session scan A 207b643687ab
survey-blitz is a command published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 492 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.