Sync ParaSkill detect-presentation-gaming
Browse files- LICENSE +21 -0
- README.md +56 -0
- SKILL.md +110 -0
- agents/openai.yaml +4 -0
LICENSE
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MIT License
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Copyright (c) 2026 bhxdianzhang
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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license: mit
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language:
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- zh
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- en
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tags:
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- codex-skill
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- ai-agent
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- scientific-writing
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- research-workflow
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- paraskill
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- paradoxgpt
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- checker
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pretty_name: DetectPresentationGaming
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---
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# DetectPresentationGaming
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`detect-presentation-gaming` is a ParaSkill research workflow skill for the `checker` family.
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## Purpose
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Detect whether a paper's perceived strengths come from genuine substance or from presentation gaming — strength inflation, parroting of self-positioning language, or "first/fundamental/comprehensive" claims without evidence. The defensive flip of ARGAR: turn its attack knowledge into a detector so authors and reviewers can catch repackaging. Use to audit a draft (or a revision) before submission.
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## Metadata
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- Family: `checker`
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- Owner model: `ParadoxGPT-Checker-4B`
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- Output type: `diagnosis`
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- Version: `0.1`
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- Default language: `zh`
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## Included Files
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```text
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DetectPresentationGaming/
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SKILL.md
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agents/openai.yaml
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README.md
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LICENSE
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```
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`SKILL.md` is the executable skill instruction. `README.md` is the Hugging Face card.
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## Installation
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Copy this folder into a Codex-discoverable skills directory, or reference `SKILL.md` directly from an agent workflow.
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```bash
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mkdir -p ~/.codex/skills
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cp -r DetectPresentationGaming ~/.codex/skills/detect-presentation-gaming
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```
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## Source
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This skill is part of the ParaSkill library for ParadoxGPT. The full routing table is maintained in `SKILL_INDEX.json` in the source bundle.
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SKILL.md
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---
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name: detect-presentation-gaming
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description: >-
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Detect whether a paper's perceived strengths come from genuine substance or
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from presentation gaming — strength inflation, parroting of self-positioning
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language, or "first/fundamental/comprehensive" claims without evidence. The
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defensive flip of ARGAR: turn its attack knowledge into a detector so authors
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and reviewers can catch repackaging. Use to audit a draft (or a revision)
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before submission.
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metadata:
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owner_model: ParadoxGPT-Checker-4B
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skill_family: checker
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version: "0.1"
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output_type: diagnosis
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default_language: zh
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trigger_keywords: [presentation gaming, strength inflation, parroting, overclaim detection, repackaging, gaming AI review, 包装, 夸大, gaming]
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required_inputs: [paper_context, claimed_strengths, evidence]
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optional_inputs: [reviews]
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handoff_to: [detect-limitation-laundering, enforce-content-preservation-zones, identify-fatal-concerns]
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---
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# Detect Presentation Gaming
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## Purpose
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检测一篇论文的 perceived strengths 是来自**真实实质**还是 **presentation gaming**——strength inflation(把局部结果包装成普遍贡献)、parroting(照搬论文自我标定词当优点)、无证据的 "first/fundamental/comprehensive"。这是 ARGAR 攻击知识的**防御翻转**:把"怎么 gaming"变成"怎么检测 gaming",让作者/审稿人在投稿前抓住 repackaging。
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## When to Use
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- 投稿前自查:我的优点是实质还是包装?
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- 审视一次修改是否在 gaming(对比改前改后)。
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- 作为 reviewer,判断一篇 strengths 是否 inflation。
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## Do Not Use
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- 只查 limitation 是否被 laundering → 用 `detect-limitation-laundering`。
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- 查修改是否越界改了数字/公式 → 用 `enforce-content-preservation-zones`。
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- 想找致命 concern → 用 `identify-fatal-concerns`。
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## Required Inputs
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- `paper_context`: 全文(定位证据)
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- `claimed_strengths`: 论文(或修改)声称的 strengths
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- `evidence`: 论文内可用的证据
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## optional Inputs
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- `reviews`: 已有 review(看 reviewer 是否被 parroting 带偏)
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## Output Contract
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输出必须包含:
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1. **per_strength_verdict** — 每条 claimed_strength:genuine / inflated / parroted + 依据。
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2. **inflation_evidence** — 哪些措辞("first/comprehensive/fundamental")无证据支撑。
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3. **parroting_instances** — reviewer(若有)复述论文自我标定词的地方。
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4. **substance_gap** — perceived strength 与真实 evidence 的落差。
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5. **verdict** — overall: clean / borderline / gamed,附最关键的 1-2 个问题。
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## Procedure
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1. 逐条 claimed_strength 核对是否有 paper 内实质证据。
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2. 标 inflation 措辞(无证据的强词)。
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3. 若有 reviews,看 reviewer 是否 parrot 论文标定词(ARGAR 的典型信号)。
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4. 量化 substance_gap,给 verdict。
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## Quality Bar
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一个好的输出必须:
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- 每条 verdict 指向可核查证据(或其缺失)。
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- 区分"措辞夸张但有事"与"纯包装无证据"。
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- parroting 有具体复述实例。
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一个差的输出:
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- 笼统"有夸大"无指向。
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- 把正常强调误判为 inflation。
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- 忽视 reviewer parroting 信号。
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## Failure Modes
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- **误判正常强调**:有证据的强措辞被当 inflation。
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- **漏 parroting**:reviewer 复述词没被抓。
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- **只看措辞不看证据**:把"comprehensive"一刀切。
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## Handoff
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- 发现 limitation 被不诚实处理 → `detect-limitation-laundering`
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- 发现改动了数字/公式 → `enforce-content-preservation-zones`
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- gamed 的 strength 关联致命问题 → `identify-fatal-concerns`
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## Example
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Input:
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```
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claimed_strengths: ["the first method addressing a fundamental gap"]
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evidence: [Fig2 三任务曲线;method §3]
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```
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Output(节选):
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```
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per_strength_verdict:
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- "first/fundamental" → inflated。"first"无检索依据;"fundamental"无理论支撑;实质证据仅 Fig2(局部)。
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inflation_evidence: "first", "fundamental" 无对应证据。
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substance_gap: 实质=局部不饱和;措辞=首创/根本。落差大。
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verdict: gamed(borderline)。建议降措辞为"we show interaction rounds are a scalable resource, with unsaturated gains (Fig2)"。
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```
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agents/openai.yaml
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interface:
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display_name: "Detect Presentation Gaming"
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short_description: "Detect whether a paper's perceived strengths come from genuine substance or from presentat"
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default_prompt: "Use $detect-presentation-gaming for this research-writing task."
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