[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFD4F45GTXGS02YR56MV0K":3,"skill-same:01M6XFD4F45GTXGS02YR56MV0K":102},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6XFD4F45GTXGS02YR56MV0K","pylabrobot",null,"Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fpylabrobot\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"only","when","plate","for","lab","types","pumps","biology","synthetic","across","automation","use","programming","simulation","api","protocols","framework","official","workflows","2026-11-10 10:12:30",false,"---\nname: pylabrobot\ndescription: Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.\ncategory: biology\nlicense: MIT license\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# PyLabRobot\n\n## Overview\n\nPyLabRobot is a hardware-agnostic, pure Python Software Development Kit for automated and autonomous laboratories. Use this skill to control liquid handling robots, plate readers, pumps, heater shakers, incubators, centrifuges, and other laboratory automation equipment through a unified Python interface that works across platforms (Windows, macOS, Linux).\n\n## When to Use This Skill\n\nUse this skill when:\n- Programming liquid handling robots (Hamilton STAR\u002FSTARlet, Opentrons OT-2, Tecan EVO)\n- Automating laboratory workflows involving pipetting, sample preparation, or analytical measurements\n- Managing deck layouts and laboratory resources (plates, tips, containers, troughs)\n- Integrating multiple lab devices (liquid handlers, plate readers, heater shakers, pumps)\n- Creating reproducible laboratory protocols with state management\n- Simulating protocols before running on physical hardware\n- Reading plates using BMG CLARIOstar or other supported plate readers\n- Controlling temperature, shaking, centrifugation, or other material handling operations\n- Working with laboratory automation in Python\n\n## Core Capabilities\n\nPyLabRobot provides comprehensive laboratory automation through six main capability areas, each detailed in the references\u002F directory:\n\n### 1. Liquid Handling (`references\u002Fliquid-handling.md`)\n\nControl liquid handling robots for aspirating, dispensing, and transferring liquids. Key operations include:\n- **Basic Operations**: Aspirate, dispense, transfer liquids between wells\n- **Tip Management**: Pick up, drop, and track pipette tips automatically\n- **Advanced Techniques**: Multi-channel pipetting, serial dilutions, plate replication\n- **Volume Tracking**: Automatic tracking of liquid volumes in wells\n- **Hardware Support**: Hamilton STAR\u002FSTARlet, Opentrons OT-2, Tecan EVO, and others\n\n### 2. Resource Management (`references\u002Fresources.md`)\n\nManage laboratory resources in a hierarchical system:\n- **Resource Types**: Plates, tip racks, troughs, tubes, carriers, and custom labware\n- **Deck Layout**: Assign resources to deck positions with coordinate systems\n- **State Management**: Track tip presence, liquid volumes, and resource states\n- **Serialization**: Save and load deck layouts and states from JSON files\n- **Resource Discovery**: Access wells, tips, and containers through intuitive APIs\n\n### 3. Hardware Backends (`references\u002Fhardware-backends.md`)\n\nConnect to diverse laboratory equipment through backend abstraction:\n- **Liquid Handlers**: Hamilton STAR (full support), Opentrons OT-2, Tecan EVO\n- **Simulation**: ChatterboxBackend for protocol testing without hardware\n- **Platform Support**: Works on Windows, macOS, Linux, and Raspberry Pi\n- **Backend Switching**: Change robots by swapping backend without rewriting protocols\n\n### 4. Analytical Equipment (`references\u002Fanalytical-equipment.md`)\n\nIntegrate plate readers and analytical instruments:\n- **Plate Readers**: BMG CLARIOstar for absorbance, luminescence, fluorescence\n- **Scales**: Mettler Toledo integration for mass measurements\n- **Integration Patterns**: Combine liquid handlers with analytical equipment\n- **Automated Workflows**: Move plates between devices automatically\n\n### 5. Material Handling (`references\u002Fmaterial-handling.md`)\n\nControl environmental and material handling equipment:\n- **Heater Shakers**: Hamilton HeaterShaker, Inheco ThermoShake\n- **Incubators**: Inheco and Thermo Fisher incubators with temperature control\n- **Centrifuges**: Agilent VSpin with bucket positioning and spin control\n- **Pumps**: Cole Parmer Masterflex for fluid pumping operations\n- **Temperature Control**: Set and monitor temperatures during protocols\n\n### 6. Visualization & Simulation (`references\u002Fvisualization.md`)\n\nVisualize and simulate laboratory protocols:\n- **Browser Visualizer**: Real-time 3D visualization of deck state\n- **Simulation Mode**: Test protocols without physical hardware\n- **State Tracking**: Monitor tip presence and liquid volumes visually\n- **Deck Editor**: Graphical tool for designing deck layouts\n- **Protocol Validation**: Verify protocols before running on hardware\n\n## Quick Start\n\nTo get started with PyLabRobot, install the package and initialize a liquid handler:\n\n```python\n# Install PyLabRobot\n# uv pip install pylabrobot\n\n# Basic liquid handling setup\nfrom pylabrobot.liquid_handling import LiquidHandler\nfrom pylabrobot.liquid_handling.backends import STAR\nfrom pylabrobot.resources import STARLetDeck\n\n# Initialize liquid handler\nlh = LiquidHandler(backend=STAR(), deck=STARLetDeck())\nawait lh.setup()\n\n# Basic operations\nawait lh.pick_up_tips(tip_rack[\"A1:H1\"])\nawait lh.aspirate(plate[\"A1\"], vols=100)\nawait lh.dispense(plate[\"A2\"], vols=100)\nawait lh.drop_tips()\n```\n\n## Working with References\n\nThis skill organizes detailed information across multiple reference files. Load the relevant reference when:\n- **Liquid Handling**: Writing pipetting protocols, tip management, transfers\n- **Resources**: Defining deck layouts, managing plates\u002Ftips, custom labware\n- **Hardware Backends**: Connecting to specific robots, switching platforms\n- **Analytical Equipment**: Integrating plate readers, scales, or analytical devices\n- **Material Handling**: Using heater shakers, incubators, centrifuges, pumps\n- **Visualization**: Simulating protocols, visualizing deck states\n\nAll reference files can be found in the `references\u002F` directory and contain comprehensive examples, API usage patterns, and best practices.\n\n## Best Practices\n\nWhen creating laboratory automation protocols with PyLabRobot:\n\n1. **Start with Simulation**: Use ChatterboxBackend and the visualizer to test protocols before running on hardware\n2. **Enable Tracking**: Turn on tip tracking and volume tracking for accurate state management\n3. **Resource Naming**: Use clear, descriptive names for all resources (plates, tip racks, containers)\n4. **State Serialization**: Save deck layouts and states to JSON for reproducibility\n5. **Error Handling**: Implement proper async error handling for hardware operations\n6. **Temperature Control**: Set temperatures early as heating\u002Fcooling takes time\n7. **Modular Protocols**: Break complex workflows into reusable functions\n8. **Documentation**: Reference official docs at https:\u002F\u002Fdocs.pylabrobot.org for latest features\n\n## Common Workflows\n\n### Liquid Transfer Protocol\n\n```python\n# Setup\nlh = LiquidHandler(backend=STAR(), deck=STARLetDeck())\nawait lh.setup()\n\n# Define resources\ntip_rack = TIP_CAR_480_A00(name=\"tip_rack\")\nsource_plate = Cos_96_DW_1mL(name=\"source\")\ndest_plate = Cos_96_DW_1mL(name=\"dest\")\n\nlh.deck.assign_child_resource(tip_rack, rails=1)\nlh.deck.assign_child_resource(source_plate, rails=10)\nlh.deck.assign_child_resource(dest_plate, rails=15)\n\n# Transfer protocol\nawait lh.pick_up_tips(tip_rack[\"A1:H1\"])\nawait lh.transfer(source_plate[\"A1:H12\"], dest_plate[\"A1:H12\"], vols=100)\nawait lh.drop_tips()\n```\n\n### Plate Reading Workflow\n\n```python\n# Setup plate reader\nfrom pylabrobot.plate_reading import PlateReader\nfrom pylabrobot.plate_reading.clario_star_backend import CLARIOstarBackend\n\npr = PlateReader(name=\"CLARIOstar\", backend=CLARIOstarBackend())\nawait pr.setup()\n\n# Set temperature and read\nawait pr.set_temperature(37)\nawait pr.open()\n# (manually or robotically load plate)\nawait pr.close()\ndata = await pr.read_absorbance(wavelength=450)\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.pylabrobot.org\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002FPyLabRobot\u002Fpylabrobot\n- **Community Forum**: https:\u002F\u002Fdiscuss.pylabrobot.org\n- **PyPI Package**: https:\u002F\u002Fpypi.org\u002Fproject\u002FPyLabRobot\u002F\n\nFor detailed usage of specific capabilities, refer to the corresponding reference file in the `references\u002F` directory.\n\n","# PyLabRobot\n\n## Overview\n\nPyLabRobot is a hardware-agnostic, pure Python Software Development Kit for automated and autonomous laboratories. Use this skill to control liquid handling robots, plate readers, pumps, heater shakers, incubators, centrifuges, and other laboratory automation equipment through a unified Python interface that works across platforms (Windows, macOS, Linux).\n\n## When to Use This Skill\n\nUse this skill when:\n- Programming liquid handling robots (Hamilton STAR\u002FSTARlet, Opentrons OT-2, Tecan EVO)\n- Automating laboratory workflows involving pipetting, sample preparation, or analytical measurements\n- Managing deck layouts and laboratory resources (plates, tips, containers, troughs)\n- Integrating multiple lab devices (liquid handlers, plate readers, heater shakers, pumps)\n- Creating reproducible laboratory protocols with state management\n- Simulating protocols before running on physical hardware\n- Reading plates using BMG CLARIOstar or other supported plate readers\n- Controlling temperature, shaking, centrifugation, or other material handling operations\n- Working with laboratory automation in Python\n\n## Core Capabilities\n\nPyLabRobot provides comprehensive laboratory automation through six main capability areas, each detailed in the references\u002F directory:\n\n### 1. Liquid Handling (`references\u002Fliquid-handling.md`)\n\nControl liquid handling robots for aspirating, dispensing, and transferring liquids. Key operations include:\n- **Basic Operations**: Aspirate, dispense, transfer liquids between wells\n- **Tip Management**: Pick up, drop, and track pipette tips automatically\n- **Advanced Techniques**: Multi-channel pipetting, serial dilutions, plate replication\n- **Volume Tracking**: Automatic tracking of liquid volumes in wells\n- **Hardware Support**: Hamilton STAR\u002FSTARlet, Opentrons OT-2, Tecan EVO, and others\n\n### 2. Resource Management (`references\u002Fresources.md`)\n\nManage laboratory resources in a hierarchical system:\n- **Resource Types**: Plates, tip racks, troughs, tubes, carriers, and custom labware\n- **Deck Layout**: Assign resources to deck positions with coordinate systems\n- **State Management**: Track tip presence, liquid volumes, and resource states\n- **Serialization**: Save and load deck layouts and states from JSON files\n- **Resource Discovery**: Access wells, tips, and containers through intuitive APIs\n\n### 3. Hardware Backends (`references\u002Fhardware-backends.md`)\n\nConnect to diverse laboratory equipment through backend abstraction:\n- **Liquid Handlers**: Hamilton STAR (full support), Opentrons OT-2, Tecan EVO\n- **Simulation**: ChatterboxBackend for protocol testing without hardware\n- **Platform Support**: Works on Windows, macOS, Linux, and Raspberry Pi\n- **Backend Switching**: Change robots by swapping backend without rewriting protocols\n\n### 4. Analytical Equipment (`references\u002Fanalytical-equipment.md`)\n\nIntegrate plate readers and analytical instruments:\n- **Plate Readers**: BMG CLARIOstar for absorbance, luminescence, fluorescence\n- **Scales**: Mettler Toledo integration for mass measurements\n- **Integration Patterns**: Combine liquid handlers with analytical equipment\n- **Automated Workflows**: Move plates between devices automatically\n\n### 5. Material Handling (`references\u002Fmaterial-handling.md`)\n\nControl environmental and material handling equipment:\n- **Heater Shakers**: Hamilton HeaterShaker, Inheco ThermoShake\n- **Incubators**: Inheco and Thermo Fisher incubators with temperature control\n- **Centrifuges**: Agilent VSpin with bucket positioning and spin control\n- **Pumps**: Cole Parmer Masterflex for fluid pumping operations\n- **Temperature Control**: Set and monitor temperatures during protocols\n\n### 6. Visualization & Simulation (`references\u002Fvisualization.md`)\n\nVisualize and simulate laboratory protocols:\n- **Browser Visualizer**: Real-time 3D visualization of deck state\n- **Simulation Mode**: Test protocols without physical hardware\n- **State Tracking**: Monitor tip presence and liquid volumes visually\n- **Deck Editor**: Graphical tool for designing deck layouts\n- **Protocol Validation**: Verify protocols before running on hardware\n\n## Quick Start\n\nTo get started with PyLabRobot, install the package and initialize a liquid handler:\n\n```python\n# Install PyLabRobot\n# uv pip install pylabrobot\n\n# Basic liquid handling setup\nfrom pylabrobot.liquid_handling import LiquidHandler\nfrom pylabrobot.liquid_handling.backends import STAR\nfrom pylabrobot.resources import STARLetDeck\n\n# Initialize liquid handler\nlh = LiquidHandler(backend=STAR(), deck=STARLetDeck())\nawait lh.setup()\n\n# Basic operations\nawait lh.pick_up_tips(tip_rack[\"A1:H1\"])\nawait lh.aspirate(plate[\"A1\"], vols=100)\nawait lh.dispense(plate[\"A2\"], vols=100)\nawait lh.drop_tips()\n```\n\n## Working with References\n\nThis skill organizes detailed information across multiple reference files. Load the relevant reference when:\n- **Liquid Handling**: Writing pipetting protocols, tip management, transfers\n- **Resources**: Defining deck layouts, managing plates\u002Ftips, custom labware\n- **Hardware Backends**: Connecting to specific robots, switching platforms\n- **Analytical Equipment**: Integrating plate readers, scales, or analytical devices\n- **Material Handling**: Using heater shakers, incubators, centrifuges, pumps\n- **Visualization**: Simulating protocols, visualizing deck states\n\nAll reference files can be found in the `references\u002F` directory and contain comprehensive examples, API usage patterns, and best practices.\n\n## Best Practices\n\nWhen creating laboratory automation protocols with PyLabRobot:\n\n1. **Start with Simulation**: Use ChatterboxBackend and the visualizer to test protocols before running on hardware\n2. **Enable Tracking**: Turn on tip tracking and volume tracking for accurate state management\n3. **Resource Naming**: Use clear, descriptive names for all resources (plates, tip racks, containers)\n4. **State Serialization**: Save deck layouts and states to JSON for reproducibility\n5. **Error Handling**: Implement proper async error handling for hardware operations\n6. **Temperature Control**: Set temperatures early as heating\u002Fcooling takes time\n7. **Modular Protocols**: Break complex workflows into reusable functions\n8. **Documentation**: Reference official docs at https:\u002F\u002Fdocs.pylabrobot.org for latest features\n\n## Common Workflows\n\n### Liquid Transfer Protocol\n\n```python\n# Setup\nlh = LiquidHandler(backend=STAR(), deck=STARLetDeck())\nawait lh.setup()\n\n# Define resources\ntip_rack = TIP_CAR_480_A00(name=\"tip_rack\")\nsource_plate = Cos_96_DW_1mL(name=\"source\")\ndest_plate = Cos_96_DW_1mL(name=\"dest\")\n\nlh.deck.assign_child_resource(tip_rack, rails=1)\nlh.deck.assign_child_resource(source_plate, rails=10)\nlh.deck.assign_child_resource(dest_plate, rails=15)\n\n# Transfer protocol\nawait lh.pick_up_tips(tip_rack[\"A1:H1\"])\nawait lh.transfer(source_plate[\"A1:H12\"], dest_plate[\"A1:H12\"], vols=100)\nawait lh.drop_tips()\n```\n\n### Plate Reading Workflow\n\n```python\n# Setup plate reader\nfrom pylabrobot.plate_reading import PlateReader\nfrom pylabrobot.plate_reading.clario_star_backend import CLARIOstarBackend\n\npr = PlateReader(name=\"CLARIOstar\", backend=CLARIOstarBackend())\nawait pr.setup()\n\n# Set temperature and read\nawait pr.set_temperature(37)\nawait pr.open()\n# (manually or robotically load plate)\nawait pr.close()\ndata = await pr.read_absorbance(wavelength=450)\n```\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.pylabrobot.org\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002FPyLabRobot\u002Fpylabrobot\n- **Community Forum**: https:\u002F\u002Fdiscuss.pylabrobot.org\n- **PyPI Package**: https:\u002F\u002Fpypi.org\u002Fproject\u002FPyLabRobot\u002F\n\nFor detailed usage of specific 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Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[109],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[111,112,113,114,115,116,117,118,30,119,120,121,122,123,124,125,126,127,42,128],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","sample",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":138,"publishedAt":48},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[135],{"categoryNo":136,"categoryName":137,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[121,139,111,140,141,142,30,143,144,145,32,146,40,147,148,149,150,151,152,153],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","with","test","research","results","statsmodels","models","help",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":167,"publishedAt":48},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[160,163,164],{"categoryNo":161,"categoryName":162,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":165,"categoryName":166,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[168,169,170,171,172,173,174,175,176,177,119,43,178,179,180,181,182,183,144,184],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":195,"publishedAt":48},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[191,194],{"categoryNo":192,"categoryName":193,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[196,169,197,198,199,200,201,202,203,176,30,204,205,206,207,208,209,210,32,211],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":48},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[223,224,149,225,226,227,228,118,229,30,119,121,230,231,232,233,234,235,183,236],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":241,"licenseFileUrl":6,"sourceHtmlUrl":242,"categories":243,"keywords":250,"publishedAt":48},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[244,247,248,249],{"categoryNo":245,"categoryName":246,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":161,"categoryName":162,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":165,"categoryName":166,"taxonomyType":26,"taxonomyTypeDesc":27},[168,251,252,253,254,255,196,256,257,30,119,121,258,259,260,261,262,263,169,183],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]