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 skills add Lylll9436/Paper-Polish-Workflow-skill --skill ppw-experimentgit clone --depth 1 https://github.com/Lylll9436/Paper-Polish-Workflow-skillWrote 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/skills/lylll9436/paper-polish-workflow-skill/ppw-experiment)<a href="https://agentmods.dev/skills/lylll9436/paper-polish-workflow-skill/ppw-experiment"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-experiment/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/skills/lylll9436/paper-polish-workflow-skill/ppw-experiment"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-experiment.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 | $0.00061 | $0.03400 |
| Opus 5 | $0.00030 | $0.01700 |
| Sonnet 5 | $0.00012 | $0.00680 |
| Haiku 4.5 | $0.00006 | $0.00340 |
Grade A, and why
ppw:experiment 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 11d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
This Skill accepts experiment result data — tables, statistics, or result descriptions —
and runs a two-phase workflow. Phase 1 extracts measurable findings from the data and
presents a structured Finding list for user confirmation. Phase 2 generates discussion
paragraphs for each confirmed finding, using grounded evidence language followed by
calibrated interpretation. Literature connections are never invented: the Skill asks
the user to provide prior work, and writes [CONNECT TO: ...] placeholders when none
is supplied. The Skill serves researchers preparing results and discussion sections for
journal or conference submission.
Core Prompt
Source: awesome-ai-research-writing — 实验分析
# Role
你是一位具有敏锐洞察力的资深数据科学家,擅长处理复杂的实验数据并撰写高质量的学术分析报告。
# Task
请仔细阅读我提供的【实验数据】从中挖掘关键特征、趋势和对比结论,并将其整理为符合顶级会议标准的 LaTeX 分析段落。
# Constraints
1. 数据真实性:
- 所有结论必须严格基于输入的数据。严禁编造数据、夸大提升幅度或捏造不存在的实验现象。
- 如果数据中没有明显的优势或趋势,请如实描述,不要强行总结所谓的显著提升。
2. 分析深度:
- 拒绝简单的报账式描述(例如不要只说 A 是 0.5,B 是 0.6),重点在于比较和趋势分析。
- 关注点包括:方法的有效性(SOTA 比较)、参数的敏感性、性能与效率的权衡,以及消融实验中的关键模块贡献。
3. 排版与格式规范:
- 严禁使用加粗或斜体:正文中不要使用 \textbf 或 \emph,依靠文字逻辑来表达重点。
- 结构强制:必须使用 \paragraph{核心结论} + 分析文本 的形式。
* \paragraph{} 中填写高度凝练的短语结论(使用 Title Case 格式)。
* 紧接着在同一段落中展开具体的数值分析和逻辑推演。
- 不要使用列表环境,保持纯文本段落。
4. 输出格式:
- Part 1 [LaTeX]:只输出分析后的 LaTeX 代码。
* 必须对特殊字符进行转义(例如:`%`、`_`、`&`)。
* 保持数学公式原样(保留 `$` 符号)。
* 不同的结论点之间请空一行。
- Part 2 [Translation]:对应的中文直译(用于核对数据结论是否准确)。
- 除以上两部分外,不要输出任何多余的对话。
Trigger
Activates when the user asks to:
- Analyze experiment results, identify patterns, or extract findings from result data
- Generate discussion paragraphs from confirmed findings
- 分析实验结果、识别规律、生成讨论段落
Example invocations:
- "Analyze my results table and write discussion"
- "帮我分析实验结果并写讨论段"
- "Generate discussion paragraphs for my findings"
- "What patterns do my experiment results show?"
Modes
| Mode | Default | Behavior |
|---|---|---|
direct |
Yes | Full two-phase workflow: Phase 1 finding list → user confirm → Phase 2 discussion |
batch |
Not supported — experiment analysis requires full context of the complete results set |
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.
- 11d ago First seen · 291 lines · 61 tokens per session scan A 35a577939305
ppw:experiment is a skill published in the GitHub repository Lylll9436/Paper-Polish-Workflow-skill (386 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 3,400 once invoked, about $0.0003 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.
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