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Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"coverage","when","for","fragment","tracks","Tokenization","machine","high-performance","bed","computational","files","use","python","learning","analysis","applications","detection","toolkit","ml","machine learning","rust","performance","2026-11-10 10:12:30",false,"---\nname: gtars\ndescription: High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.\n---\n\n# Gtars: Genomic Tools and Algorithms in Rust\n\n## Overview\n\nGtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.\n\nUse this skill when working with:\n- Genomic interval files (BED format)\n- Overlap detection between genomic regions\n- Coverage track generation (WIG, BigWig)\n- Genomic ML preprocessing and tokenization\n- Fragment analysis in single-cell genomics\n- Reference sequence retrieval and validation\n\n## Installation\n\n### Python Installation\n\nInstall gtars Python bindings:\n\n```bash\nuv uv pip install gtars\n```\n\n### CLI Installation\n\nInstall command-line tools (requires Rust\u002FCargo):\n\n```bash\n# Install with all features\ncargo install gtars-cli --features \"uniwig overlaprs igd bbcache scoring fragsplit\"\n\n# Or install specific features only\ncargo install gtars-cli --features \"uniwig overlaprs\"\n```\n\n### Rust Library\n\nAdd to Cargo.toml for Rust projects:\n\n```toml\n[dependencies]\ngtars = { version = \"0.1\", features = [\"tokenizers\", \"overlaprs\"] }\n```\n\n## Core Capabilities\n\nGtars is organized into specialized modules, each focused on specific genomic analysis tasks:\n\n### 1. Overlap Detection and IGD Indexing\n\nEfficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.\n\n**When to use:**\n- Finding overlapping regulatory elements\n- Variant annotation\n- Comparing ChIP-seq peaks\n- Identifying shared genomic features\n\n**Quick example:**\n```python\nimport gtars\n\n# Build IGD index and query overlaps\nigd = gtars.igd.build_index(\"regions.bed\")\noverlaps = igd.query(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Foverlap.md` for comprehensive overlap detection documentation.\n\n### 2. Coverage Track Generation\n\nGenerate coverage tracks from sequencing data with the uniwig module.\n\n**When to use:**\n- ATAC-seq accessibility profiles\n- ChIP-seq coverage visualization\n- RNA-seq read coverage\n- Differential coverage analysis\n\n**Quick example:**\n```bash\n# Generate BigWig coverage track\ngtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig\n```\n\nSee `references\u002Fcoverage.md` for detailed coverage analysis workflows.\n\n### 3. Genomic Tokenization\n\nConvert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data.\n\n**When to use:**\n- Preprocessing for genomic ML models\n- Integration with geniml library\n- Creating position encodings\n- Training transformer models on genomic sequences\n\n**Quick example:**\n```python\nfrom gtars.tokenizers import TreeTokenizer\n\ntokenizer = TreeTokenizer.from_bed_file(\"training_regions.bed\")\ntoken = tokenizer.tokenize(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Ftokenizers.md` for tokenization documentation.\n\n### 4. Reference Sequence Management\n\nHandle reference genome sequences and compute digests following the GA4GH refget protocol.\n\n**When to use:**\n- Validating reference genome integrity\n- Extracting specific genomic sequences\n- Computing sequence digests\n- Cross-reference comparisons\n\n**Quick example:**\n```python\n# Load reference and extract sequences\nstore = gtars.RefgetStore.from_fasta(\"hg38.fa\")\nsequence = store.get_subsequence(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Frefget.md` for reference sequence operations.\n\n### 5. Fragment Processing\n\nSplit and analyze fragment files, particularly useful for single-cell genomics data.\n\n**When to use:**\n- Processing single-cell ATAC-seq data\n- Splitting fragments by cell barcodes\n- Cluster-based fragment analysis\n- Fragment quality control\n\n**Quick example:**\n```bash\n# Split fragments by clusters\ngtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir .\u002Fby_cluster\u002F\n```\n\nSee `references\u002Fcli.md` for fragment processing commands.\n\n### 6. Fragment Scoring\n\nScore fragment overlaps against reference datasets.\n\n**When to use:**\n- Evaluating fragment enrichment\n- Comparing experimental data to references\n- Quality metrics computation\n- Batch scoring across samples\n\n**Quick example:**\n```bash\n# Score fragments against reference\ngtars scoring score --fragments fragments.bed --reference reference.bed --output scores.txt\n```\n\n## Common Workflows\n\n### Workflow 1: Peak Overlap Analysis\n\nIdentify overlapping genomic features:\n\n```python\nimport gtars\n\n# Load two region sets\npeaks = gtars.RegionSet.from_bed(\"chip_peaks.bed\")\npromoters = gtars.RegionSet.from_bed(\"promoters.bed\")\n\n# Find overlaps\noverlapping_peaks = peaks.filter_overlapping(promoters)\n\n# Export results\noverlapping_peaks.to_bed(\"peaks_in_promoters.bed\")\n```\n\n### Workflow 2: Coverage Track Pipeline\n\nGenerate coverage tracks for visualization:\n\n```bash\n# Step 1: Generate coverage\ngtars uniwig generate --input atac_fragments.bed --output coverage.wig --resolution 10\n\n# Step 2: Convert to BigWig for genome browsers\ngtars uniwig generate --input atac_fragments.bed --output coverage.bw --format bigwig\n```\n\n### Workflow 3: ML Preprocessing\n\nPrepare genomic data for machine learning:\n\n```python\nfrom gtars.tokenizers import TreeTokenizer\nimport gtars\n\n# Step 1: Load training regions\nregions = gtars.RegionSet.from_bed(\"training_peaks.bed\")\n\n# Step 2: Create tokenizer\ntokenizer = TreeTokenizer.from_bed_file(\"training_peaks.bed\")\n\n# Step 3: Tokenize regions\ntokens = [tokenizer.tokenize(r.chromosome, r.start, r.end) for r in regions]\n\n# Step 4: Use tokens in ML pipeline\n# (integrate with geniml or custom models)\n```\n\n## Python vs CLI Usage\n\n**Use Python API when:**\n- Integrating with analysis pipelines\n- Need programmatic control\n- Working with NumPy\u002FPandas\n- Building custom workflows\n\n**Use CLI when:**\n- Quick one-off analyses\n- Shell scripting\n- Batch processing files\n- Prototyping workflows\n\n## Reference Documentation\n\nComprehensive module documentation:\n\n- **`references\u002Fpython-api.md`** - Complete Python API reference with RegionSet operations, NumPy integration, and data export\n- **`references\u002Foverlap.md`** - IGD indexing, overlap detection, and set operations\n- **`references\u002Fcoverage.md`** - Coverage track generation with uniwig\n- **`references\u002Ftokenizers.md`** - Genomic tokenization for ML applications\n- **`references\u002Frefget.md`** - Reference sequence management and digests\n- **`references\u002Fcli.md`** - Command-line interface complete reference\n\n## Integration with geniml\n\nGtars serves as the foundation for the geniml Python package, providing core genomic interval operations for machine learning workflows. When working on geniml-related tasks, use gtars for data preprocessing and tokenization.\n\n## Performance Characteristics\n\n- **Native Rust performance**: Fast execution with low memory overhead\n- **Parallel processing**: Multi-threaded operations for large datasets\n- **Memory efficiency**: Streaming and memory-mapped file support\n- **Zero-copy operations**: NumPy integration with minimal data copying\n\n## Data Formats\n\nGtars works with standard genomic formats:\n\n- **BED**: Genomic intervals (3-column or extended)\n- **WIG\u002FBigWig**: Coverage tracks\n- **FASTA**: Reference sequences\n- **Fragment TSV**: Single-cell fragment files with barcodes\n\n## Error Handling and Debugging\n\nEnable verbose logging for troubleshooting:\n\n```python\nimport gtars\n\n# Enable debug logging\ngtars.set_log_level(\"DEBUG\")\n```\n\n```bash\n# CLI verbose mode\ngtars --verbose \u003Ccommand>\n```\n","# Gtars: Genomic Tools and Algorithms in Rust\n\n## Overview\n\nGtars is a high-performance Rust toolkit for manipulating, analyzing, and processing genomic interval data. It provides specialized tools for overlap detection, coverage analysis, tokenization for machine learning, and reference sequence management.\n\nUse this skill when working with:\n- Genomic interval files (BED format)\n- Overlap detection between genomic regions\n- Coverage track generation (WIG, BigWig)\n- Genomic ML preprocessing and tokenization\n- Fragment analysis in single-cell genomics\n- Reference sequence retrieval and validation\n\n## Installation\n\n### Python Installation\n\nInstall gtars Python bindings:\n\n```bash\nuv uv pip install gtars\n```\n\n### CLI Installation\n\nInstall command-line tools (requires Rust\u002FCargo):\n\n```bash\n# Install with all features\ncargo install gtars-cli --features \"uniwig overlaprs igd bbcache scoring fragsplit\"\n\n# Or install specific features only\ncargo install gtars-cli --features \"uniwig overlaprs\"\n```\n\n### Rust Library\n\nAdd to Cargo.toml for Rust projects:\n\n```toml\n[dependencies]\ngtars = { version = \"0.1\", features = [\"tokenizers\", \"overlaprs\"] }\n```\n\n## Core Capabilities\n\nGtars is organized into specialized modules, each focused on specific genomic analysis tasks:\n\n### 1. Overlap Detection and IGD Indexing\n\nEfficiently detect overlaps between genomic intervals using the Integrated Genome Database (IGD) data structure.\n\n**When to use:**\n- Finding overlapping regulatory elements\n- Variant annotation\n- Comparing ChIP-seq peaks\n- Identifying shared genomic features\n\n**Quick example:**\n```python\nimport gtars\n\n# Build IGD index and query overlaps\nigd = gtars.igd.build_index(\"regions.bed\")\noverlaps = igd.query(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Foverlap.md` for comprehensive overlap detection documentation.\n\n### 2. Coverage Track Generation\n\nGenerate coverage tracks from sequencing data with the uniwig module.\n\n**When to use:**\n- ATAC-seq accessibility profiles\n- ChIP-seq coverage visualization\n- RNA-seq read coverage\n- Differential coverage analysis\n\n**Quick example:**\n```bash\n# Generate BigWig coverage track\ngtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig\n```\n\nSee `references\u002Fcoverage.md` for detailed coverage analysis workflows.\n\n### 3. Genomic Tokenization\n\nConvert genomic regions into discrete tokens for machine learning applications, particularly for deep learning models on genomic data.\n\n**When to use:**\n- Preprocessing for genomic ML models\n- Integration with geniml library\n- Creating position encodings\n- Training transformer models on genomic sequences\n\n**Quick example:**\n```python\nfrom gtars.tokenizers import TreeTokenizer\n\ntokenizer = TreeTokenizer.from_bed_file(\"training_regions.bed\")\ntoken = tokenizer.tokenize(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Ftokenizers.md` for tokenization documentation.\n\n### 4. Reference Sequence Management\n\nHandle reference genome sequences and compute digests following the GA4GH refget protocol.\n\n**When to use:**\n- Validating reference genome integrity\n- Extracting specific genomic sequences\n- Computing sequence digests\n- Cross-reference comparisons\n\n**Quick example:**\n```python\n# Load reference and extract sequences\nstore = gtars.RefgetStore.from_fasta(\"hg38.fa\")\nsequence = store.get_subsequence(\"chr1\", 1000, 2000)\n```\n\nSee `references\u002Frefget.md` for reference sequence operations.\n\n### 5. Fragment Processing\n\nSplit and analyze fragment files, particularly useful for single-cell genomics data.\n\n**When to use:**\n- Processing single-cell ATAC-seq data\n- Splitting fragments by cell barcodes\n- Cluster-based fragment analysis\n- Fragment quality control\n\n**Quick example:**\n```bash\n# Split fragments by clusters\ngtars fragsplit cluster-split --input fragments.tsv --clusters clusters.txt --output-dir .\u002Fby_cluster\u002F\n```\n\nSee `references\u002Fcli.md` for fragment processing commands.\n\n### 6. Fragment Scoring\n\nScore fragment overlaps against reference datasets.\n\n**When to use:**\n- Evaluating fragment enrichment\n- Comparing experimental data to references\n- Quality metrics computation\n- Batch scoring across samples\n\n**Quick example:**\n```bash\n# Score fragments against reference\ngtars scoring score --fragments fragments.bed --reference reference.bed --output scores.txt\n```\n\n## Common Workflows\n\n### Workflow 1: Peak Overlap Analysis\n\nIdentify overlapping genomic features:\n\n```python\nimport gtars\n\n# Load two region sets\npeaks = gtars.RegionSet.from_bed(\"chip_peaks.bed\")\npromoters = gtars.RegionSet.from_bed(\"promoters.bed\")\n\n# Find overlaps\noverlapping_peaks = peaks.filter_overlapping(promoters)\n\n# Export results\noverlapping_peaks.to_bed(\"peaks_in_promoters.bed\")\n```\n\n### Workflow 2: Coverage Track Pipeline\n\nGenerate coverage tracks for visualization:\n\n```bash\n# Step 1: Generate coverage\ngtars uniwig generate --input atac_fragments.bed --output coverage.wig --resolution 10\n\n# Step 2: Convert to BigWig for genome browsers\ngtars uniwig generate --input atac_fragments.bed --output coverage.bw --format bigwig\n```\n\n### Workflow 3: ML Preprocessing\n\nPrepare genomic data for machine learning:\n\n```python\nfrom gtars.tokenizers import TreeTokenizer\nimport gtars\n\n# Step 1: Load training regions\nregions = gtars.RegionSet.from_bed(\"training_peaks.bed\")\n\n# Step 2: Create tokenizer\ntokenizer = TreeTokenizer.from_bed_file(\"training_peaks.bed\")\n\n# Step 3: Tokenize regions\ntokens = [tokenizer.tokenize(r.chromosome, r.start, r.end) for r in regions]\n\n# Step 4: Use tokens in ML pipeline\n# (integrate with geniml or custom models)\n```\n\n## Python vs CLI Usage\n\n**Use Python API when:**\n- Integrating with analysis pipelines\n- Need programmatic control\n- Working with NumPy\u002FPandas\n- Building custom workflows\n\n**Use CLI when:**\n- Quick one-off analyses\n- Shell scripting\n- Batch processing files\n- Prototyping workflows\n\n## Reference Documentation\n\nComprehensive module documentation:\n\n- **`references\u002Fpython-api.md`** - Complete Python API reference with RegionSet operations, NumPy integration, and data export\n- **`references\u002Foverlap.md`** - IGD indexing, overlap detection, and set operations\n- **`references\u002Fcoverage.md`** - Coverage track generation with uniwig\n- **`references\u002Ftokenizers.md`** - Genomic tokenization for ML applications\n- **`references\u002Frefget.md`** - Reference sequence management and digests\n- **`references\u002Fcli.md`** - Command-line interface complete reference\n\n## Integration with geniml\n\nGtars serves as the foundation for the geniml Python package, providing core genomic interval operations for machine learning workflows. When working on geniml-related tasks, use gtars for data preprocessing and tokenization.\n\n## Performance Characteristics\n\n- **Native Rust performance**: Fast execution with low memory overhead\n- **Parallel processing**: Multi-threaded operations for large datasets\n- **Memory efficiency**: Streaming and memory-mapped file support\n- **Zero-copy operations**: NumPy integration with minimal data copying\n\n## Data Formats\n\nGtars works with standard genomic formats:\n\n- **BED**: Genomic intervals (3-column or extended)\n- **WIG\u002FBigWig**: Coverage tracks\n- **FASTA**: Reference sequences\n- **Fragment TSV**: Single-cell fragment files with barcodes\n\n## Error Handling and Debugging\n\nEnable verbose logging for troubleshooting:\n\n```python\nimport gtars\n\n# Enable debug logging\ngtars.set_log_level(\"DEBUG\")\n```\n\n```bash\n# CLI verbose mode\ngtars --verbose \u003Ccommand>\n```\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add davila7\u002Fclaude-code-templates --skill gtars",{"tool":62,"command":63},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill gtars",{"tool":65,"command":66},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill gtars","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002FSKILL.md",true,[70,76,80,84,88,92],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fcli.md","REFERENCE","参考文档",5060,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Fcli.md",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fcoverage.md",3995,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Fcoverage.md",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Foverlap.md",3679,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Foverlap.md",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fpython-api.md",4222,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Fpython-api.md",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Frefget.md",3165,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Frefget.md",{"path":93,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Ftokenizers.md",2444,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fgtars\u002Freferences\u002Ftokenizers.md",[97,126,152,185,220,242],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":108,"publishedAt":41},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. 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Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000009","前端开发",[197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,21,219],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":224,"sourceHtmlUrl":225,"categories":226,"keywords":231,"publishedAt":41},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[227,230],{"categoryNo":228,"categoryName":229,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":194,"categoryName":195,"taxonomyType":106,"taxonomyTypeDesc":107},[232,233,234,235,236,198,34,207,208,237,36,238,239,21,240,117,241],"webapp testing","testing","webapp","browser","playwright","debugging","logs","screenshots","local","behavior",{"skillNo":243,"name":244,"nameZh":6,"description":245,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":189,"licenseFileUrl":246,"sourceHtmlUrl":247,"categories":248,"keywords":256,"publishedAt":41},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[249,252,253],{"categoryNo":250,"categoryName":251,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":254,"categoryName":255,"taxonomyType":165,"taxonomyTypeDesc":166},"01JBSEEDCAT000000000000036","内容创作者",[257,258,259,260,261,262,263,264,200,265,210,144,266,267,268,269,270,180,271,20,272,33,273,274,275,123,276],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]