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Use for field extraction, time series analysis, line profiles, statistical summaries, derived quantity computation, result comparison to references, and automated report generation from simulation results.","eric861129\u002FSKILLS_All-in-one","eric861129","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F74950944?v=4","01M6CXHDYEYRPFGGV4EFXCSYCF","ChiYu-Huang","01M6CXHE2J5CN1EBTY0FKV3ZKW",52,"MIT","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FScientific & Research Tools\u002Fpost-processing\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"post processing","write","time series analysis","series","generation","time","references","use","extract","results","visualize","read","grep","analyze","Bash","simulation","analysis","data","time series","report","2026-11-10 10:12:30",false,"---\nname: post-processing\ndescription: Extract, analyze, and visualize simulation output data. Use for field extraction, time series analysis, line profiles, statistical summaries, derived quantity computation, result comparison to references, and automated report generation from simulation results.\nallowed-tools: Read, Bash, Write, Grep, Glob\n---\n\n# Post-Processing Skill\n\nAnalyze and extract meaningful information from simulation output data.\n\n## Goal\n\nTransform raw simulation output into actionable insights through field extraction, statistical analysis, derived quantities, visualizations, and comparison with reference data.\n\n## Inputs to Gather\n\nBefore running post-processing scripts, collect:\n\n1. **Output Data Location**\n   - Path to simulation output files (JSON, CSV, HDF5, VTK)\n   - Time step\u002Fsnapshot indices of interest\n   - Field names to extract\n\n2. **Analysis Type**\n   - Field extraction (spatial data at specific times)\n   - Time series (temporal evolution of quantities)\n   - Line profiles (1D cuts through domain)\n   - Statistical summary (mean, std, distributions)\n   - Derived quantities (gradients, integrals, fluxes)\n   - Comparison to reference data\n\n3. **Output Requirements**\n   - Output format (JSON, CSV, tabular)\n   - Visualization needs\n   - Report format\n\n## Scripts\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `field_extractor.py` | Extract field data from output files | --input, --field, --timestep |\n| `time_series_analyzer.py` | Analyze temporal evolution | --input, --quantity, --window |\n| `profile_extractor.py` | Extract line profiles | --input, --field, --start, --end |\n| `statistical_analyzer.py` | Compute field statistics | --input, --field, --region |\n| `derived_quantities.py` | Calculate derived quantities | --input, --quantity, --params |\n| `comparison_tool.py` | Compare to reference data | --simulation, --reference, --metric |\n| `report_generator.py` | Generate summary reports | --input, --template, --output |\n\n## Workflow\n\n### 1. Data Inventory\n\nFirst, understand what data is available:\n\n```bash\n# List available fields and timesteps\npython scripts\u002Ffield_extractor.py --input results\u002F --list --json\n```\n\n### 2. Field Extraction\n\nExtract spatial field data at specific timesteps:\n\n```bash\n# Extract concentration field at timestep 100\npython scripts\u002Ffield_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --json\n\n# Extract multiple fields\npython scripts\u002Ffield_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field \"phi,concentration,temperature\" \\\n    --json\n```\n\n### 3. Time Series Analysis\n\nAnalyze temporal evolution of quantities:\n\n```bash\n# Extract total energy vs time\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity total_energy \\\n    --json\n\n# Compute moving average with window\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity mass \\\n    --window 10 \\\n    --json\n\n# Detect steady state\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity residual \\\n    --detect-steady-state \\\n    --tolerance 1e-6 \\\n    --json\n```\n\n### 4. Line Profile Extraction\n\nExtract 1D profiles through the domain:\n\n```bash\n# Extract profile along x-axis at y=0.5\npython scripts\u002Fprofile_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --start \"0,0.5,0\" \\\n    --end \"1,0.5,0\" \\\n    --points 100 \\\n    --json\n\n# Interface profile (through center)\npython scripts\u002Fprofile_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --axis x \\\n    --slice-position 0.5 \\\n    --json\n```\n\n### 5. Statistical Analysis\n\nCompute statistics over field data:\n\n```bash\n# Global statistics\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --json\n\n# Statistics in specific region\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --region \"x>0.3 and x\u003C0.7\" \\\n    --json\n\n# Distribution analysis\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --histogram \\\n    --bins 50 \\\n    --json\n```\n\n### 6. Derived Quantities\n\nCalculate physical quantities from raw data:\n\n```bash\n# Compute interface area\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity interface_area \\\n    --threshold 0.5 \\\n    --json\n\n# Compute gradient magnitude\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity gradient_magnitude \\\n    --field phi \\\n    --json\n\n# Compute volume fractions\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity volume_fraction \\\n    --field phi \\\n    --threshold 0.5 \\\n    --json\n\n# Compute flux through boundary\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity boundary_flux \\\n    --field concentration \\\n    --boundary \"x=0\" \\\n    --json\n```\n\n### 7. Comparison with Reference\n\nCompare simulation results to reference data:\n\n```bash\n# Compare to analytical solution\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fprofile.json \\\n    --reference reference\u002Fanalytical.json \\\n    --metric l2_error \\\n    --json\n\n# Compare to experimental data\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fhistory.json \\\n    --reference experimental_data.csv \\\n    --metric rmse \\\n    --interpolate \\\n    --json\n\n# Compare two simulations\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results_fine\u002Ffield.json \\\n    --reference results_coarse\u002Ffield.json \\\n    --metric max_difference \\\n    --json\n```\n\n### 8. Report Generation\n\nGenerate automated reports:\n\n```bash\n# Generate summary report\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --output report.json \\\n    --json\n\n# Generate with specific sections\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --sections \"summary,statistics,convergence\" \\\n    --output report.json \\\n    --json\n```\n\n## Typical Post-Processing Pipeline\n\nFor a complete simulation analysis:\n\n```bash\n# Step 1: Inventory available data\npython scripts\u002Ffield_extractor.py --input results\u002F --list --json\n\n# Step 2: Extract final state statistics\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_final.json \\\n    --field phi \\\n    --json\n\n# Step 3: Analyze convergence history\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity residual \\\n    --detect-steady-state \\\n    --json\n\n# Step 4: Compute derived quantities\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_final.json \\\n    --quantity volume_fraction \\\n    --field phi \\\n    --json\n\n# Step 5: Compare to reference (if available)\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fprofile.json \\\n    --reference benchmark\u002Fexpected.json \\\n    --metric l2_error \\\n    --json\n\n# Step 6: Generate summary report\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --output analysis_report.json \\\n    --json\n```\n\n## Interpretation Guidelines\n\n### Time Series Analysis\n- **Monotonic decrease** in energy: System approaching equilibrium\n- **Oscillations** in residual: May indicate time step too large\n- **Plateau** in quantities: Steady state reached\n- **Sudden jumps**: Possible numerical instability\n\n### Statistical Analysis\n- **Bimodal distribution** of order parameter: Two-phase mixture\n- **High variance**: Heterogeneous microstructure\n- **Skewed distribution**: Asymmetric phase fractions\n\n### Comparison Metrics\n| Metric | Interpretation |\n|--------|----------------|\n| L2 error \u003C 1% | Excellent agreement |\n| L2 error 1-5% | Good agreement |\n| L2 error 5-10% | Moderate agreement |\n| L2 error > 10% | Poor agreement, investigate |\n\n## Output Format\n\nAll scripts support `--json` flag for machine-readable output:\n\n```json\n{\n    \"script\": \"field_extractor\",\n    \"version\": \"1.0.0\",\n    \"input_file\": \"results\u002Ffield_0100.json\",\n    \"field\": \"concentration\",\n    \"data\": {\n        \"shape\": [100, 100],\n        \"min\": 0.1,\n        \"max\": 0.9,\n        \"mean\": 0.5\n    },\n    \"values\": [[...], [...]]\n}\n```\n\n## References\n\nFor detailed information, see:\n\n- `references\u002Fdata_formats.md` - Supported input\u002Foutput formats\n- `references\u002Fstatistical_methods.md` - Statistical analysis methods\n- `references\u002Fderived_quantities_guide.md` - Physical quantity calculations\n- `references\u002Fcomparison_metrics.md` - Error metrics and interpretation\n\n## Requirements\n\n- Python 3.8+\n- NumPy (for numerical operations)\n- No other external dependencies for core functionality\n\n## Version History\n\n- v1.0.0 (2024-12-24): Initial release\n","# Post-Processing Skill\n\nAnalyze and extract meaningful information from simulation output data.\n\n## Goal\n\nTransform raw simulation output into actionable insights through field extraction, statistical analysis, derived quantities, visualizations, and comparison with reference data.\n\n## Inputs to Gather\n\nBefore running post-processing scripts, collect:\n\n1. **Output Data Location**\n   - Path to simulation output files (JSON, CSV, HDF5, VTK)\n   - Time step\u002Fsnapshot indices of interest\n   - Field names to extract\n\n2. **Analysis Type**\n   - Field extraction (spatial data at specific times)\n   - Time series (temporal evolution of quantities)\n   - Line profiles (1D cuts through domain)\n   - Statistical summary (mean, std, distributions)\n   - Derived quantities (gradients, integrals, fluxes)\n   - Comparison to reference data\n\n3. **Output Requirements**\n   - Output format (JSON, CSV, tabular)\n   - Visualization needs\n   - Report format\n\n## Scripts\n\n| Script | Purpose | Key Inputs |\n|--------|---------|------------|\n| `field_extractor.py` | Extract field data from output files | --input, --field, --timestep |\n| `time_series_analyzer.py` | Analyze temporal evolution | --input, --quantity, --window |\n| `profile_extractor.py` | Extract line profiles | --input, --field, --start, --end |\n| `statistical_analyzer.py` | Compute field statistics | --input, --field, --region |\n| `derived_quantities.py` | Calculate derived quantities | --input, --quantity, --params |\n| `comparison_tool.py` | Compare to reference data | --simulation, --reference, --metric |\n| `report_generator.py` | Generate summary reports | --input, --template, --output |\n\n## Workflow\n\n### 1. Data Inventory\n\nFirst, understand what data is available:\n\n```bash\n# List available fields and timesteps\npython scripts\u002Ffield_extractor.py --input results\u002F --list --json\n```\n\n### 2. Field Extraction\n\nExtract spatial field data at specific timesteps:\n\n```bash\n# Extract concentration field at timestep 100\npython scripts\u002Ffield_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --json\n\n# Extract multiple fields\npython scripts\u002Ffield_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field \"phi,concentration,temperature\" \\\n    --json\n```\n\n### 3. Time Series Analysis\n\nAnalyze temporal evolution of quantities:\n\n```bash\n# Extract total energy vs time\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity total_energy \\\n    --json\n\n# Compute moving average with window\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity mass \\\n    --window 10 \\\n    --json\n\n# Detect steady state\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity residual \\\n    --detect-steady-state \\\n    --tolerance 1e-6 \\\n    --json\n```\n\n### 4. Line Profile Extraction\n\nExtract 1D profiles through the domain:\n\n```bash\n# Extract profile along x-axis at y=0.5\npython scripts\u002Fprofile_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --start \"0,0.5,0\" \\\n    --end \"1,0.5,0\" \\\n    --points 100 \\\n    --json\n\n# Interface profile (through center)\npython scripts\u002Fprofile_extractor.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --axis x \\\n    --slice-position 0.5 \\\n    --json\n```\n\n### 5. Statistical Analysis\n\nCompute statistics over field data:\n\n```bash\n# Global statistics\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field concentration \\\n    --json\n\n# Statistics in specific region\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --region \"x>0.3 and x\u003C0.7\" \\\n    --json\n\n# Distribution analysis\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_0100.json \\\n    --field phi \\\n    --histogram \\\n    --bins 50 \\\n    --json\n```\n\n### 6. Derived Quantities\n\nCalculate physical quantities from raw data:\n\n```bash\n# Compute interface area\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity interface_area \\\n    --threshold 0.5 \\\n    --json\n\n# Compute gradient magnitude\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity gradient_magnitude \\\n    --field phi \\\n    --json\n\n# Compute volume fractions\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity volume_fraction \\\n    --field phi \\\n    --threshold 0.5 \\\n    --json\n\n# Compute flux through boundary\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_0100.json \\\n    --quantity boundary_flux \\\n    --field concentration \\\n    --boundary \"x=0\" \\\n    --json\n```\n\n### 7. Comparison with Reference\n\nCompare simulation results to reference data:\n\n```bash\n# Compare to analytical solution\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fprofile.json \\\n    --reference reference\u002Fanalytical.json \\\n    --metric l2_error \\\n    --json\n\n# Compare to experimental data\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fhistory.json \\\n    --reference experimental_data.csv \\\n    --metric rmse \\\n    --interpolate \\\n    --json\n\n# Compare two simulations\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results_fine\u002Ffield.json \\\n    --reference results_coarse\u002Ffield.json \\\n    --metric max_difference \\\n    --json\n```\n\n### 8. Report Generation\n\nGenerate automated reports:\n\n```bash\n# Generate summary report\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --output report.json \\\n    --json\n\n# Generate with specific sections\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --sections \"summary,statistics,convergence\" \\\n    --output report.json \\\n    --json\n```\n\n## Typical Post-Processing Pipeline\n\nFor a complete simulation analysis:\n\n```bash\n# Step 1: Inventory available data\npython scripts\u002Ffield_extractor.py --input results\u002F --list --json\n\n# Step 2: Extract final state statistics\npython scripts\u002Fstatistical_analyzer.py \\\n    --input results\u002Ffield_final.json \\\n    --field phi \\\n    --json\n\n# Step 3: Analyze convergence history\npython scripts\u002Ftime_series_analyzer.py \\\n    --input results\u002Fhistory.json \\\n    --quantity residual \\\n    --detect-steady-state \\\n    --json\n\n# Step 4: Compute derived quantities\npython scripts\u002Fderived_quantities.py \\\n    --input results\u002Ffield_final.json \\\n    --quantity volume_fraction \\\n    --field phi \\\n    --json\n\n# Step 5: Compare to reference (if available)\npython scripts\u002Fcomparison_tool.py \\\n    --simulation results\u002Fprofile.json \\\n    --reference benchmark\u002Fexpected.json \\\n    --metric l2_error \\\n    --json\n\n# Step 6: Generate summary report\npython scripts\u002Freport_generator.py \\\n    --input results\u002F \\\n    --output analysis_report.json \\\n    --json\n```\n\n## Interpretation Guidelines\n\n### Time Series Analysis\n- **Monotonic decrease** in energy: System approaching equilibrium\n- **Oscillations** in residual: May indicate time step too large\n- **Plateau** in quantities: Steady state reached\n- **Sudden jumps**: Possible numerical instability\n\n### Statistical Analysis\n- **Bimodal distribution** of order parameter: Two-phase mixture\n- **High variance**: Heterogeneous microstructure\n- **Skewed distribution**: Asymmetric phase fractions\n\n### Comparison Metrics\n| Metric | Interpretation |\n|--------|----------------|\n| L2 error \u003C 1% | Excellent agreement |\n| L2 error 1-5% | Good agreement |\n| L2 error 5-10% | Moderate agreement |\n| L2 error > 10% | Poor agreement, investigate |\n\n## Output Format\n\nAll scripts support `--json` flag for machine-readable output:\n\n```json\n{\n    \"script\": \"field_extractor\",\n    \"version\": \"1.0.0\",\n    \"input_file\": \"results\u002Ffield_0100.json\",\n    \"field\": \"concentration\",\n    \"data\": {\n        \"shape\": [100, 100],\n        \"min\": 0.1,\n        \"max\": 0.9,\n        \"mean\": 0.5\n    },\n    \"values\": [[...], [...]]\n}\n```\n\n## References\n\nFor detailed information, see:\n\n- `references\u002Fdata_formats.md` - Supported input\u002Foutput formats\n- `references\u002Fstatistical_methods.md` - Statistical analysis methods\n- 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Tools\u002Fpost-processing\u002Fscripts\u002Fstatistical_analyzer.py",{"path":119,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":120,"sourceHtmlUrl":121},"scripts\u002Ftime_series_analyzer.py",14828,"https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FScientific & Research Tools\u002Fpost-processing\u002Fscripts\u002Ftime_series_analyzer.py",[123,148,180,208,232,250],{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":130,"publishedAt":44},"01M6CZ1WR3KNVTKSWH4GNWT646","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fwhat-if-oracle\u002FSKILL.md",[129],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[131,132,133,134,135,136,137,138,40,139,140,141,142,35,25,143,144,145,146,147],"oracle","what if oracle","go","run","planning","ux","futures","risk","explore","decision","branch","question","idea","exploration","options","stress-test","strategies",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":166,"publishedAt":44},"01M6CZ1QHGJE91A95MSN5EK5Q9","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fvenue-templates\u002FSKILL.md",[154,157,160,161],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[167,168,169,170,171,172,173,174,38,175,35,25,176,177,178,179],"templates","venue templates","research","guidelines","NeurIPS","ICML","LaTeX","science","access","edit","proposals","Academic","requirements",{"skillNo":181,"name":182,"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":190,"publishedAt":44},"01M6CZ1KBQD2E5RTFB3BGVX7CD","the-fool","Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fthe-fool\u002FSKILL.md",[186,187],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":188,"categoryName":189,"taxonomyType":164,"taxonomyTypeDesc":165},"01JBSEEDCAT000000000000030","开发工程师",[191,192,133,193,194,134,195,196,197,198,199,200,201,202,177,203,204,205,206,207],"the fool","review","workflow","architecture","code","audit","forge","devil","team","designer","reviewer","challenge","stress test","evidence","expert","poke","ideas",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":212,"categories":213,"keywords":217,"publishedAt":44},"01M6CZ1ES826S7P1X3333E40TA","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\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-writing\u002FSKILL.md",[214,215,216],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":162,"categoryName":163,"taxonomyType":164,"taxonomyTypeDesc":165},[218,219,170,220,221,222,223,224,38,225,35,25,176,31,226,227,228,229,230,231],"writing","scientific writing","core","reporting","deep research","Citations","Outlines","tool","never","structure","deep","full","stage","prisma",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":239,"publishedAt":44},"01M6CZ1B2JGJTB9FRD0TW4RXXE","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-brainstorming\u002FSKILL.md",[238],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[240,241,169,242,135,41,243,244,31,245,246,247,144,248,249,230,28],"brainstorming","scientific brainstorming","sessions","hypothesis","open","ideation","creative","do","connections","research ideation",{"skillNo":251,"name":252,"nameZh":6,"description":253,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":254,"categories":255,"keywords":262,"publishedAt":44},"01M6CZ16JG8N7A2ZX04BGE8J2Y","research-lookup","Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fresearch-lookup\u002FSKILL.md",[256,259,260,261],{"categoryNo":257,"categoryName":258,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":188,"categoryName":189,"taxonomyType":164,"taxonomyTypeDesc":165},[169,263,264,265,41,266,267,38,268,269,35,25,176,31,270,271,272,273,274,178],"lookup","research lookup","search","perplexity","api","parallel","openrouter","backend","up","look","queries","chat"]