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/openraiser/paperfit/paperfit-prioritygit clone --depth 1 https://github.com/OpenRaiser/PaperFitWrote 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/openraiser/paperfit/paperfit-priority)<a href="https://agentmods.dev/commands/openraiser/paperfit/paperfit-priority"><img src="https://agentmods.dev/badge/commands/openraiser/paperfit/paperfit-priority.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 | $0.00000 | $0.00631 |
| Opus 5 | $0.00000 | $0.00316 |
| Sonnet 5 | $0.00000 | $0.00126 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
paperfit-priority 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 5d 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
/paperfit-priority — 调整当前修复优先级
作用: 读取当前状态、视觉重点和修复计划，给出本轮应该先处理什么；若用户明确指定优先顺序，可将该顺序写入 next_actions 供后续 /fix-layout 直接遵循。
用户入口说明:
- 这是优先级解释与调整入口，不要求用户理解 repair plan 的底层结构。
- 若用户以自然语言说明“先修第 5 页空洞，再修表格”，也应路由到同类任务。
- 不要把它变成手动操作内部状态文件的流程说明。
防呆约束:
- 不要调用
TaskCreate、TodoWrite、Plan之类的任务工具 - 不要在这个命令里直接修改论文源码
- 浮动体位置与尺寸问题未收敛前，不要把文本增删改列为第一优先级
工具调用约定
在论文项目根目录内，优先读取：
data/state.jsondata/visual_signal_report.jsondata/repair_plan.json
若用户要求把人工优先级写回状态，可执行：
paperfit runtime --state data/state.json set-next-actions "<action1>" "<action2>" "<action3>"
用法
/paperfit-priority
也可以由自然语言触发，例如：
先修第 5 页的视觉空洞，再处理表格
当前这篇论文应该先修什么
执行流程
- 读取
data/state.json，提取：visual_signals_summary.priority_pagesvisual_signals_summary.priority_objectsrepair_plan_summary.top_candidatesnext_actions
- 总结当前机器优先级。
- 若用户给出人工优先级，则按以下真实执行顺序归并：
- 先处理图表位置、跨栏选择、尺寸不匹配
- 再处理 overfull / underfull / 对齐类问题
- 最后才考虑
semantic_budgeter或其它文本增删改
- 若用户要求持久化该优先级，用
paperfit runtime --state data/state.json set-next-actions ...覆盖next_actions。 - 明确提示下一步应继续执行
/fix-layout，并带着新的优先级约束推进。
输出结果
- 当前 priority pages / priority objects
- 当前 top repair-plan candidates
- 人工优先级是否已写回
next_actions - 下一轮建议动作
调度
- 不直接调用 Agent
- 通过
state.json、visual_signal_report.json、repair_plan.json做优先级解释与写回
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.
- 5d ago First seen · 68 lines · 0 tokens per session scan A 1e7b705d3222
paperfit-priority is a command published in the GitHub repository OpenRaiser/PaperFit (331 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 631 tokens. 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.
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