brycewang-stanford
brycewang-stanford/Auto-Empirical-Research-Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill clawbio-guide命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
正在显示 SKILL.md
| name | clawbio-guide |
| description | OpenClaw bioinformatics skill library for genomics pipelines |
| metadata | {"openclaw":{"emoji":"🧪","category":"domains","subcategory":"biomedical","keywords":["ClawBio","bioinformatics","OpenClaw","genomics","pipeline","biological analysis"],"source":"https://github.com/ClawBio/ClawBio"}} |
ClawBio is a bioinformatics skill library for OpenClaw that provides pre-built skills for common genomics and biological analysis tasks — sequence alignment, variant calling, differential expression, pathway analysis, and more. Each skill encapsulates best-practice bioinformatics pipelines as conversational agent capabilities, making complex analyses accessible through natural language.
# Install as OpenClaw plugin
openclaw plugins install @clawbio/clawbio
# Or add to your OpenClaw configuration
# In openclaw.config.json:
{
"plugins": ["@clawbio/clawbio"]
}
| Skill | Pipeline | Description |
|---|---|---|
| sequence-align | BWA/Bowtie2 | Align reads to reference genome |
| variant-call | GATK/BCFtools | Call SNPs and indels |
| rna-seq | STAR + DESeq2 | Differential expression analysis |
| chip-seq | MACS2 + DiffBind | Peak calling and differential binding |
| metagenomics | Kraken2 + Bracken | Taxonomic classification |
| phylogenetics | IQ-TREE + RAxML | Phylogenetic tree construction |
| protein-structure | AlphaFold/ESMFold | Structure prediction |
| pathway-analysis | GSEA + enrichR | Gene set enrichment |
# Through OpenClaw conversational interface:
# "Analyze differential expression between treated and control
# samples in the data/rnaseq/ directory"
# ClawBio executes:
# 1. Quality control (FastQC)
# 2. Trimming (Trimmomatic)
# 3. Alignment (STAR)
# 4. Quantification (featureCounts)
# 5. Differential expression (DESeq2)
# 6. Visualization (volcano plot, MA plot, heatmap)
# 7. Pathway enrichment (GSEA)
# "Call variants from the whole-genome sequencing data
# in samples/ against hg38 reference"
# Pipeline:
# 1. Alignment: BWA-MEM2 → sorted BAM
# 2. Preprocessing: MarkDuplicates, BQSR
# 3. Variant calling: GATK HaplotypeCaller
# 4. Filtering: VQSR or hard filters
# 5. Annotation: VEP or SnpEff
# 6. Report: variant statistics, quality metrics
# "Classify the microbial communities in my 16S/shotgun
# sequencing data and generate taxonomic plots"
# Pipeline:
# 1. Quality filtering (fastp)
# 2. Host decontamination (Bowtie2 vs human)
# 3. Classification (Kraken2 + Bracken)
# 4. Diversity analysis (alpha + beta diversity)
# 5. Differential abundance (LEfSe/ANCOM)
# 6. Visualization (stacked bar, PCoA, heatmap)
{
"clawbio": {
"reference_genomes": {
"hg38": "/data/references/hg38/",
"mm39": "/data/references/mm39/",
"custom": "/data/references/custom/"
},
"tools": {
"aligner": "bwa-mem2",
"variant_caller": "gatk",
"quantifier": "featurecounts",
"de_method": "deseq2"
},
"resources": {
"threads": 8,
"memory_gb": 32,
"gpu": false
},
# Create custom bioinformatics skills
# SKILL.md template for new analysis types
"""
---
name: my-custom-analysis
description: "Custom bioinformatics analysis skill"
metadata:
openclaw:
category: "domains"
subcategory: "biomedical"
---
# My Custom Analysis
## When to use
Describe when this analysis is appropriate.
## Pipeline Steps
1. Input validation
2. Processing step 1
3. Processing step 2
4. Output generation
## Example Usage
Show conversational examples.
"""