[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAP6VS6B21YZXWRJVJ9TJW":3,"skill-same:01M6DAP6VS6B21YZXWRJVJ9TJW":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":31,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":14,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6DAP6VS6B21YZXWRJVJ9TJW","tooluniverse-data-integration-analysis",null,"Integrate statistical analysis results with biological knowledge from ToolUniverse tools. After computing associations or differential expression, use pathway analysis, literature search, drug-target databases, and variant annotation to interpret findings biologically. Use when statistical results need biological context, when users want to go beyond p-values to understand mechanisms, or when combining data analysis with literature evidence.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-data-integration-analysis\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"tooluniverse data integration analysis","data integration","integration","data","analysis","statistical analysis","databases","evidence","integrate","use","results","context","knowledge","data analysis","Tools","search","go","2026-11-10 10:12:30",false,"---\nname: tooluniverse-data-integration-analysis\ndescription: Integrate statistical analysis results with biological knowledge from ToolUniverse tools. After computing associations or differential expression, use pathway analysis, literature search, drug-target databases, and variant annotation to interpret findings biologically. Use when statistical results need biological context, when users want to go beyond p-values to understand mechanisms, or when combining data analysis with literature evidence.\n---\n\n## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do -- execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n# Data Integration Analysis\n\nBridge the gap between statistical results and biological understanding. After any computational analysis produces significant findings, this skill teaches how to interpret them using ToolUniverse's biological knowledge tools -- the key advantage over platforms that only do data analysis.\n\n**IMPORTANT**: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Respond in the user's language.\n\n---\n\n## When to Use This Skill\n\nApply when:\n- Statistical analysis produced a list of significant genes, variants, metabolites, or exposures\n- Users want to go beyond p-values to understand WHY something is significant\n- Combining computational results with published evidence\n- Interpreting differential expression, GWAS hits, or association study results biologically\n- Users ask \"what does this result mean?\" after running an analysis\n\n**NOT for** (use other skills instead):\n- Running the statistical analysis itself --> Use `tooluniverse-statistical-modeling` or `tooluniverse-rnaseq-deseq2`\n- Pure gene enrichment without prior analysis --> Use `tooluniverse-gene-enrichment`\n- Pure literature review --> Use `tooluniverse-literature-deep-research`\n- Single variant interpretation --> Use `tooluniverse-variant-interpretation`\n\n---\n\n## Step 1: Statistical Results to Biological Questions\n\nMap each type of significant finding to the right biological question:\n\n| Finding Type | Biological Question | Tool Discovery Query |\n|---|---|---|\n| Significant gene list | What pathways are enriched? What functions converge? | `find_tools(\"gene enrichment pathway analysis\")` |\n| Significant variant (rsID) | What is the functional impact? Which gene is affected? | `find_tools(\"variant annotation functional impact\")` |\n| Significant exposure\u002Fchemical | What is the biological mechanism? Which pathways? | `find_tools(\"chemical gene pathway toxicology\")` |\n| Significant drug association | What is the molecular target? What is the MOA? | `find_tools(\"drug target mechanism action\")` |\n| Significant metabolite | Which metabolic pathway is perturbed? | `find_tools(\"metabolite pathway identification\")` |\n\n**Key principle**: Do not stop at \"gene X is significant.\" Ask: significant in what context? Through what mechanism? With what downstream consequence?\n\n---\n\n## Step 2: Multi-Database Evidence Integration\n\nFor each significant finding, query multiple sources and synthesize. The pattern:\n\n1. **Literature evidence**: Search PubMed\u002FEuropePMC for published studies linking your finding to the phenotype. Look for meta-analyses and systematic reviews first.\n2. **Genetic association evidence**: Query GWAS Catalog or OpenTargets to check whether genetic evidence independently supports the association.\n3. **Pathway context**: Query KEGG, Reactome, or WikiPathways to place the finding in a biological pathway. Identify upstream regulators and downstream effectors.\n4. **Interaction networks**: Query STRING or BioGRID for protein-protein interactions. Look for whether your significant genes cluster in the same network neighborhood.\n5. **Clinical relevance**: Check ClinVar for variant clinical significance, DGIdb or ChEMBL for druggability, or ClinicalTrials.gov for ongoing interventions.\n\n**Evidence grading** (grade each piece of evidence):\n\n| Grade | Source Type | Example |\n|---|---|---|\n| T1 (Strong) | Randomized clinical trial, Mendelian randomization | \"RCT showed drug X reduces outcome Y\" |\n| T2 (Moderate) | Large cohort study, GWAS with replication | \"GWAS meta-analysis in 500k subjects\" |\n| T3 (Suggestive) | Case-control study, animal model | \"Mouse knockout shows phenotype\" |\n| T4 (Hypothesis) | In silico prediction, pathway inference | \"Network analysis suggests involvement\" |\n\n---\n\n## Step 3: Causal Reasoning\n\nStatistical association is not causation. Apply these reasoning frameworks:\n\n**DAG construction**: Before interpreting, sketch the causal directed acyclic graph (DAG).\n- Identify potential **confounders** (common causes of exposure and outcome) -- these must be adjusted for.\n- Identify potential **mediators** (on the causal path) -- do NOT adjust for these if estimating total effect.\n- Identify **colliders** (common effects) -- conditioning on colliders introduces bias.\n\n**Triangulation**: The same finding supported by different methods with different biases strengthens causal inference.\n- Observational association + Mendelian randomization + animal experiment = strong triangulated evidence\n- If MR contradicts observational data, suspect confounding in the observational study\n\n**Mendelian randomization logic**: Genetic variants (instruments) are assigned at conception, so they are not confounded by lifestyle or reverse causation. If a genetic variant that increases exposure X also increases disease Y, this supports X causing Y. Check instrument strength (F-statistic > 10), exclusion restriction (variant affects Y only through X), and pleiotropy (MR-Egger intercept).\n\n**Mediation analysis**: If gene G is associated with both exposure and outcome, ask: does the exposure effect on outcome go through G? Use the finding's pathway context (Step 2) to propose mediators, then check if adjusting for the mediator attenuates the effect.\n\n---\n\n## Step 4: Cross-Validation\n\nBefore reporting a finding as robust, attempt to falsify it:\n\n1. **Replication**: Search literature and datasets (DataCite, GEO, ArrayExpress) for independent datasets where the same finding can be tested. A finding that replicates in an independent cohort is much stronger.\n2. **Biological plausibility**: Does the mechanism make biological sense? Check if animal or cell models support it (PubMed search for \"[gene] knockout [phenotype]\" or \"[chemical] exposure [cell type]\").\n3. **Genetic support**: Check if GWAS evidence supports the direction of effect. If your analysis says gene X is protective but GWAS shows risk alleles increase X expression, there is a contradiction to resolve.\n4. **Dose-response**: If available, check whether the effect increases with dose. A dose-response relationship strengthens causal inference.\n5. **Negative controls**: If possible, test the same analysis on a finding where you expect no association. If the negative control also shows an association, suspect a methodological artifact.\n\n---\n\n## Step 5: Actionable Reporting\n\nStructure the integrated report as follows:\n\n### Evidence Summary Table\nFor each significant finding, produce one row:\n\n| Finding | Statistical Evidence | Biological Mechanism | Literature Support | Genetic Support | Evidence Grade |\n|---|---|---|---|---|---|\n| Gene X upregulated | FDR=0.001, log2FC=2.3 | PI3K\u002FAKT pathway | 12 papers, 2 RCTs | GWAS: rs123 (p=5e-8) | Strong |\n| Variant rs456 | OR=1.4, p=2e-6 | Splicing disruption | 3 case reports | eQTL in GTEx | Moderate |\n\n### Strength Assessment\n- **Strong**: Statistical significance + biological mechanism + independent replication + genetic support\n- **Moderate**: Statistical significance + biological mechanism + partial replication\n- **Weak**: Statistical significance + plausible mechanism but no independent support\n- **Suggestive**: Statistical trend + computational prediction only\n\n### Knowledge Gaps and Next Steps\n- Which findings lack replication? Propose specific datasets to test in.\n- Which mechanisms are inferred but not experimentally validated? Propose experiments.\n- Which findings have conflicting evidence? State the contradiction explicitly.\n- Generate testable hypotheses: \"If finding X is causal, then [experimental prediction].\"\n\n### Clinical or Public Health Implications\n- State whether the finding is actionable now or requires further validation.\n- If druggable, identify existing therapeutics and their development stage.\n- If a biomarker, assess sensitivity\u002Fspecificity for clinical utility.\n","## COMPUTE, DON'T DESCRIBE\nWhen analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do -- execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.\n\n# Data Integration Analysis\n\nBridge the gap between statistical results and biological understanding. After any computational analysis produces significant findings, this skill teaches how to interpret them using ToolUniverse's biological knowledge tools -- the key advantage over platforms that only do data analysis.\n\n**IMPORTANT**: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Respond in the user's language.\n\n---\n\n## When to Use This Skill\n\nApply when:\n- Statistical analysis produced a list of significant genes, variants, metabolites, or exposures\n- Users want to go beyond p-values to understand WHY something is significant\n- Combining computational results with published evidence\n- Interpreting differential expression, GWAS hits, or association study results biologically\n- Users ask \"what does this result mean?\" after running an analysis\n\n**NOT for** (use other skills instead):\n- Running the statistical analysis itself --> Use `tooluniverse-statistical-modeling` or `tooluniverse-rnaseq-deseq2`\n- Pure gene enrichment without prior analysis --> Use `tooluniverse-gene-enrichment`\n- Pure literature review --> Use `tooluniverse-literature-deep-research`\n- Single variant interpretation --> Use `tooluniverse-variant-interpretation`\n\n---\n\n## Step 1: Statistical Results to Biological Questions\n\nMap each type of significant finding to the right biological question:\n\n| Finding Type | Biological Question | Tool Discovery Query |\n|---|---|---|\n| Significant gene list | What pathways are enriched? What functions converge? | `find_tools(\"gene enrichment pathway analysis\")` |\n| Significant variant (rsID) | What is the functional impact? Which gene is affected? | `find_tools(\"variant annotation functional impact\")` |\n| Significant exposure\u002Fchemical | What is the biological mechanism? Which pathways? | `find_tools(\"chemical gene pathway toxicology\")` |\n| Significant drug association | What is the molecular target? What is the MOA? | `find_tools(\"drug target mechanism action\")` |\n| Significant metabolite | Which metabolic pathway is perturbed? | `find_tools(\"metabolite pathway identification\")` |\n\n**Key principle**: Do not stop at \"gene X is significant.\" Ask: significant in what context? Through what mechanism? With what downstream consequence?\n\n---\n\n## Step 2: Multi-Database Evidence Integration\n\nFor each significant finding, query multiple sources and synthesize. The pattern:\n\n1. **Literature evidence**: Search PubMed\u002FEuropePMC for published studies linking your finding to the phenotype. Look for meta-analyses and systematic reviews first.\n2. **Genetic association evidence**: Query GWAS Catalog or OpenTargets to check whether genetic evidence independently supports the association.\n3. **Pathway context**: Query KEGG, Reactome, or WikiPathways to place the finding in a biological pathway. Identify upstream regulators and downstream effectors.\n4. **Interaction networks**: Query STRING or BioGRID for protein-protein interactions. Look for whether your significant genes cluster in the same network neighborhood.\n5. **Clinical relevance**: Check ClinVar for variant clinical significance, DGIdb or ChEMBL for druggability, or ClinicalTrials.gov for ongoing interventions.\n\n**Evidence grading** (grade each piece of evidence):\n\n| Grade | Source Type | Example |\n|---|---|---|\n| T1 (Strong) | Randomized clinical trial, Mendelian randomization | \"RCT showed drug X reduces outcome Y\" |\n| T2 (Moderate) | Large cohort study, GWAS with replication | \"GWAS meta-analysis in 500k subjects\" |\n| T3 (Suggestive) | Case-control study, animal model | \"Mouse knockout shows phenotype\" |\n| T4 (Hypothesis) | In silico prediction, pathway inference | \"Network analysis suggests involvement\" |\n\n---\n\n## Step 3: Causal Reasoning\n\nStatistical association is not causation. Apply these reasoning frameworks:\n\n**DAG construction**: Before interpreting, sketch the causal directed acyclic graph (DAG).\n- Identify potential **confounders** (common causes of exposure and outcome) -- these must be adjusted for.\n- Identify potential **mediators** (on the causal path) -- do NOT adjust for these if estimating total effect.\n- Identify **colliders** (common effects) -- conditioning on colliders introduces bias.\n\n**Triangulation**: The same finding supported by different methods with different biases strengthens causal inference.\n- Observational association + Mendelian randomization + animal experiment = strong triangulated evidence\n- If MR contradicts observational data, suspect confounding in the observational study\n\n**Mendelian randomization logic**: Genetic variants (instruments) are assigned at conception, so they are not confounded by lifestyle or reverse causation. If a genetic variant that increases exposure X also increases disease Y, this supports X causing Y. Check instrument strength (F-statistic > 10), exclusion restriction (variant affects Y only through X), and pleiotropy (MR-Egger intercept).\n\n**Mediation analysis**: If gene G is associated with both exposure and outcome, ask: does the exposure effect on outcome go through G? Use the finding's pathway context (Step 2) to propose mediators, then check if adjusting for the mediator attenuates the effect.\n\n---\n\n## Step 4: Cross-Validation\n\nBefore reporting a finding as robust, attempt to falsify it:\n\n1. **Replication**: Search literature and datasets (DataCite, GEO, ArrayExpress) for independent datasets where the same finding can be tested. A finding that replicates in an independent cohort is much stronger.\n2. **Biological plausibility**: Does the mechanism make biological sense? Check if animal or cell models support it (PubMed search for \"[gene] knockout [phenotype]\" or \"[chemical] exposure [cell type]\").\n3. **Genetic support**: Check if GWAS evidence supports the direction of effect. If your analysis says gene X is protective but GWAS shows risk alleles increase X expression, there is a contradiction to resolve.\n4. **Dose-response**: If available, check whether the effect increases with dose. A dose-response relationship strengthens causal inference.\n5. **Negative controls**: If possible, test the same analysis on a finding where you expect no association. If the negative control also shows an association, suspect a methodological artifact.\n\n---\n\n## Step 5: Actionable Reporting\n\nStructure the integrated report as follows:\n\n### Evidence Summary Table\nFor each significant finding, produce one row:\n\n| Finding | Statistical Evidence | Biological Mechanism | Literature Support | Genetic Support | Evidence Grade |\n|---|---|---|---|---|---|\n| Gene X upregulated | FDR=0.001, log2FC=2.3 | PI3K\u002FAKT pathway | 12 papers, 2 RCTs | GWAS: rs123 (p=5e-8) | Strong |\n| Variant rs456 | OR=1.4, p=2e-6 | Splicing disruption | 3 case reports | eQTL in GTEx | Moderate |\n\n### Strength Assessment\n- **Strong**: Statistical significance + biological mechanism + independent replication + genetic support\n- **Moderate**: Statistical significance + biological mechanism + partial replication\n- **Weak**: Statistical significance + plausible mechanism but no independent support\n- **Suggestive**: Statistical trend + computational prediction only\n\n### Knowledge Gaps and Next Steps\n- Which findings lack replication? Propose specific datasets to test in.\n- Which mechanisms are inferred but not experimentally validated? Propose experiments.\n- Which findings have conflicting evidence? State the contradiction explicitly.\n- Generate testable hypotheses: \"If finding X is causal, then [experimental prediction].\"\n\n### Clinical or Public Health Implications\n- State whether the finding is actionable now or requires further validation.\n- If druggable, identify existing therapeutics and their development stage.\n- If a biomarker, assess sensitivity\u002Fspecificity for clinical utility.\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-data-integration-analysis","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-data-integration-analysis",{"tool":66,"command":67},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-data-integration-analysis",{"tool":69,"command":70},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-data-integration-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-data-integration-analysis\u002FSKILL.md",true,[],[75,95,124,151,165,181],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":49},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[82,83,84,85,86,87,88,89,90,41,43,91,92,93,94],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","repo","injection","execution","file",{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":104,"publishedAt":49},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[105,106,107,108,109,110,111,112,113,114,115,116,117,118,41,119,120,121,122,123],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":136,"publishedAt":49},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[130,133],{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":134,"categoryName":135,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[137,138,139,140,141,142,143,144,145,146,106,147,148,149,150],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":49},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[153,158,159,160,161,162,163,164],"setup","report","project","installation","project setup","issues","state",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":172,"publishedAt":49},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[171],{"categoryNo":102,"categoryName":103,"taxonomyType":21,"taxonomyTypeDesc":22},[167,173,87,174,175,176,41,177,178,179,180],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":182,"name":158,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":49},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[158,160,121,187,162,188],"files","interactive"]