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bio-logic生物逻辑

Agent Skill

bio-logic 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

392

周安装

16

GitHub Stars

1

下载量

125
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:bio-logic(生物逻辑)
来源仓库:https://github.com/fmschulz/omics-skills
仓库路径:skills/bio-logic
安装命令:
npx skills add https://github.com/fmschulz/omics-skills --skill bio-logic
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/fmschulz/omics-skills --skill bio-logic

简介

用于科学主张和方法论的评估,提供结构化框架评估证据强度和实验设计质量。

  • 适用于论文批判、研究设计评审和科学主张评估场景,使用相关检查清单。
  • 提供方法论、样本量、随机化、盲法和统计方法等方面的评估标准。
  • 安装命令为 npx skills add https://github.com/fmschulz/omics-skills --skill bio-logic。
  • 使用前应根据研究类型选择合适的检查清单部分,跳过不适用项目。

SKILL.md

Bio-Logic: Scientific Reasoning Evaluation

Use structured frameworks to evaluate scientific claims, methodology, and evidence strength.

Instructions

  1. Identify the task (claim assessment, paper critique, study design review).
  2. Apply the relevant checklist below.
  3. Structure output using the provided format.

Critique Checklist

Use relevant sections based on the review scope. Skip items not applicable to the study type.

## Methodology
- [ ] Design matches research question (causal claim → RCT needed)
- [ ] Sample size justified (power analysis reported)
- [ ] Randomization/blinding implemented where feasible
- [ ] Confounders identified and controlled
- [ ] Measurements validated and reliable

## Statistics
- [ ] Tests appropriate for data type
- [ ] Assumptions checked
- [ ] Multiple comparisons corrected
- [ ] Effect sizes + CIs reported (not just p-values)
- [ ] Missing data handled appropriately

## Interpretation
- [ ] Conclusions match evidence strength
- [ ] Limitations acknowledged
- [ ] Causal claims only from experimental designs
- [ ] No cherry-picking or overgeneralization

## Red Flags
- [ ] P-values clustered just below .05
- [ ] Outcomes differ from registration
- [ ] Correlation presented as causation
- [ ] Subgroups analyzed without preregistration

Claim Assessment

  1. Identify claim type (causal, associational, descriptive).
  2. Match evidence to claim type.
  3. Check logical connection between data and conclusion.
  4. Ensure confidence matches evidence strength.

Claim strength ladder:

LanguageRequires
"Proves" / "Demonstrates"Strong experimental evidence
"Suggests" / "Indicates"Observational with controlled confounds
"Associated with"Observational, no causal claim
"May" / "Might"Preliminary or hypothesis-generating

Output Format

## Summary
[1-2 sentences: What was studied and main finding]

## Strengths
- [Specific methodological strengths]

## Concerns
### Critical (threaten main conclusions)
- [Issue + why it matters]

### Important (affect interpretation)
- [Issue + why it matters]

### Minor (worth noting)
- [Issue]

## Evidence Rating
[GRADE level: High/Moderate/Low/Very Low with justification]

## Bottom Line
[What can/cannot be concluded from this evidence]

Quick Reference

TaskAction
Review a paperUse the Critique Checklist below
Evaluate a claimUse Claim Assessment steps
Assess evidence strengthSee references/evidence.md
Identify biasesSee references/biases.md
Statistical errorsSee references/stats.md
Logical fallaciesSee references/fallacies.md
Study design reviewSee references/design.md

Input Requirements

  • Manuscript or study summary
  • Claim statements to evaluate
  • Methods and results (as available)

Output

  • Structured critique with strengths, concerns, and evidence rating
  • Clear recommendation on claim strength and limitations

Quality Gates

  • Claim type matches evidence type
  • Key methodological risks are identified
  • Evidence rating is justified with concrete issues

Examples

Example 1: Quick Paper Critique

Input: "Review this RCT on vitamin D and depression"

Output:

## Summary
RCT of 200 adults testing vitamin D supplementation for depression over 12 weeks.

## Strengths
- Randomized, double-blind, placebo-controlled
- Validated depression scale (PHQ-9)
- Adequate power analysis

## Concerns
### Critical
- 35% dropout, differential by group (attrition bias)
- ITT analysis not performed

### Important
- Single-site limits generalizability

## Evidence Rating
Moderate (downgraded from high due to attrition bias)

## Bottom Line
Suggestive but not conclusive due to differential attrition.

Example 2: Claim Assessment

Input: "This study proves that coffee prevents Alzheimer's"

Assessment: If based on observational data, this is a correlation→causation error. Appropriate language: "Coffee consumption is associated with lower Alzheimer's risk."

Troubleshooting

Issue: Insufficient methods details Solution: Request missing design/statistics information before rating evidence.

Issue: Conflicting results across studies Solution: Report uncertainty and suggest stronger study designs for resolution.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.43%
按下载量换算48

Claude

29.3%
按下载量换算37

Cursor

18.05%
按下载量换算23

Gemini CLI

8.96%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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