# Send GO/KEGG Enrichment to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "go-kegg-enrichment", "name": "GO/KEGG Enrichment", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/AIPOCH-AI/go-kegg-enrichment", "canonicalUrl": "https://clawhub.ai/AIPOCH-AI/go-kegg-enrichment", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/go-kegg-enrichment", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=go-kegg-enrichment", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "requirements.txt", "SKILL.md", "scripts/main.py", "references/example_gene_list.txt", "references/GO_KEGG_Reference.md", "references/requirements.txt" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/go-kegg-enrichment" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/go-kegg-enrichment", "downloadUrl": "https://openagent3.xyz/downloads/go-kegg-enrichment", "agentUrl": "https://openagent3.xyz/skills/go-kegg-enrichment/agent", "manifestUrl": "https://openagent3.xyz/skills/go-kegg-enrichment/agent.json", "briefUrl": "https://openagent3.xyz/skills/go-kegg-enrichment/agent.md" } } ``` ## Documentation ### GO/KEGG Enrichment Analysis Automated pipeline for Gene Ontology and KEGG pathway enrichment analysis with result interpretation and visualization. ### Features GO Enrichment: Biological Process (BP), Molecular Function (MF), Cellular Component (CC) KEGG Pathway: Pathway enrichment with organism-specific mapping Multiple ID Support: Gene symbols, Entrez IDs, Ensembl IDs, RefSeq Statistical Methods: Hypergeometric test, Fisher's exact test, GSEA support Visualizations: Bar plots, dot plots, enrichment maps, cnet plots Result Interpretation: Automatic biological significance summary ### Supported Organisms Common NameScientific NameKEGG CodeOrgDB PackageHumanHomo sapienshsaorg.Hs.eg.dbMouseMus musculusmmuorg.Mm.eg.dbRatRattus norvegicusrnoorg.Rn.eg.dbZebrafishDanio reriodreorg.Dr.eg.dbFlyDrosophila melanogasterdmeorg.Dm.eg.dbYeastSaccharomyces cerevisiaesceorg.Sc.sgd.db ### Basic Usage # Run enrichment analysis with gene list python scripts/main.py --genes gene_list.txt --organism human --output results/ ### Parameters ParameterDescriptionDefaultRequired--genesPath to gene list file (one gene per line)-Yes--organismOrganism code (human/mouse/rat/zebrafish/fly/yeast)humanNo--id-typeGene ID type (symbol/entrez/ensembl/refseq)symbolNo--backgroundBackground gene list fileall genesNo--pvalue-cutoffP-value cutoff for significance0.05No--qvalue-cutoffAdjusted p-value (q-value) cutoff0.2No--analysisAnalysis type (go/kegg/all)allNo--outputOutput directory./enrichment_resultsNo--formatOutput format (csv/tsv/excel/all)allNo ### Advanced Usage # GO enrichment only with specific ontology python scripts/main.py \\ --genes deg_upregulated.txt \\ --organism mouse \\ --analysis go \\ --go-ontologies BP,MF \\ --pvalue-cutoff 0.01 \\ --output go_results/ # KEGG enrichment with custom background python scripts/main.py \\ --genes treatment_genes.txt \\ --background all_expressed_genes.txt \\ --organism human \\ --analysis kegg \\ --qvalue-cutoff 0.05 \\ --output kegg_results/ ### Gene List File TP53 BRCA1 EGFR MYC KRAS PTEN ### With Expression Values (for GSEA) gene,log2FoldChange TP53,2.5 BRCA1,-1.8 EGFR,3.2 ### Output Files output/ ├── go_enrichment/ │ ├── GO_BP_results.csv # Biological Process results │ ├── GO_MF_results.csv # Molecular Function results │ ├── GO_CC_results.csv # Cellular Component results │ ├── GO_BP_barplot.pdf # Visualization │ ├── GO_MF_dotplot.pdf │ └── GO_summary.txt # Interpretation summary ├── kegg_enrichment/ │ ├── KEGG_results.csv # Pathway results │ ├── KEGG_barplot.pdf │ ├── KEGG_dotplot.pdf │ └── KEGG_pathview/ # Pathway diagrams └── combined_report.html # Interactive report ### Result Interpretation The tool automatically generates biological interpretation including: Top Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio Functional Themes: Clustered biological themes from enriched terms Key Genes: Core genes driving enrichment in significant terms Network Relationships: Gene-term relationship visualization Clinical Relevance: Disease associations (for human genes) ### Technical Difficulty: HIGH ⚠️ AI自主验收状态: 需人工检查 This skill requires: R/Bioconductor environment with clusterProfiler Multiple annotation databases (org.*.eg.db) KEGG REST API access Complex visualization dependencies ### Required R Packages install.packages(c("BiocManager", "ggplot2", "dplyr", "readr")) BiocManager::install(c( "clusterProfiler", "org.Hs.eg.db", "org.Mm.eg.db", "org.Rn.eg.db", "enrichplot", "pathview", "DOSE" )) ### Python Dependencies pip install pandas numpy matplotlib seaborn rpy2 ### Example Workflow Prepare Input: Create gene list from DEG analysis Run Analysis: Execute main.py with appropriate parameters Review Results: Check generated CSV files and visualizations Interpret: Read auto-generated summary for biological insights ### References See references/ for: clusterProfiler documentation KEGG API guide Statistical methods explanation Visualization examples ### Limitations Requires internet connection for KEGG database queries Large gene lists (>5000) may require increased memory Some pathways may not be available for all organisms KEGG API has rate limits (max 3 requests/second) ### Risk Assessment Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow ### Security Checklist No hardcoded credentials or API keys No unauthorized file system access (../) Output does not expose sensitive information Prompt injection protections in place Input file paths validated (no ../ traversal) Output directory restricted to workspace Script execution in sandboxed environment Error messages sanitized (no stack traces exposed) Dependencies audited ### Prerequisites # Python dependencies pip install -r requirements.txt ### Success Metrics Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable ### Test Cases Basic Functionality: Standard input → Expected output Edge Case: Invalid input → Graceful error handling Performance: Large dataset → Acceptable processing time ### Lifecycle Status Current Stage: Draft Next Review Date: 2026-03-06 Known Issues: None Planned Improvements: Performance optimization Additional feature support ## Trust - Source: tencent - Verification: Indexed source record - Publisher: AIPOCH-AI - Version: 1.0.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/go-kegg-enrichment) - [Send to Agent page](https://openagent3.xyz/skills/go-kegg-enrichment/agent) - [JSON manifest](https://openagent3.xyz/skills/go-kegg-enrichment/agent.json) - [Markdown brief](https://openagent3.xyz/skills/go-kegg-enrichment/agent.md) - [Download page](https://openagent3.xyz/downloads/go-kegg-enrichment)