post-processing
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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill post-processing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Post-Processing Skill Analyze and extract meaningful information from simulation output data. ## Goal Transform raw simulation output into actionable insights through field extraction, statistical analysis, derived quantities, visualizations, and comparison with reference data. ## Inputs to Gather Before running post-processing scripts, collect: 1. **Output Data Location** - Path to simulation output files (JSON, CSV, HDF5, VTK) - Time step/snapshot indices of interest - Field names to extract 2. **Analysis Type** - Field extraction (spatial data at specific times) - Time series (temporal evolution of quantities) - Line profiles (1D cuts through domain) - Statistical summary (mean, std, distributions) - Derived quantities (gradients, integrals, fluxes) - Comparison to reference data 3. **Output Requirements** - Output format (JSON, CSV, tabular) - Visualization needs - Report format ## Scripts | Script | Purpose | Key Inputs | |--------|---------|------------| | `field_extractor.py` | Extract field data from output files | --input, --field, --timestep | | `time_series_analyzer.py` | Analyze temporal evolution | --input, --quantity, --window | | `profile_extractor.py` | Extract li
- Goal
- Inputs to Gather
- Scripts
- Workflow
- 1. Data Inventory
- 2. Field Extraction
- 3. Time Series Analysis
- 4. Line Profile Extraction
- 5. Statistical Analysis
- 6. Derived Quantities
- 7. Comparison with Reference
- 8. Report Generation
- Typical Post-Processing Pipeline
- Interpretation Guidelines
List available fields and timesteps python scripts/field_extractor.py --input results/ --list --json Extract concentration field at timestep 100 python scripts/field_extractor.py \ Extract multiple fields Extract total energy vs time python scripts/time_series_analyzer.py \ Compute moving average with window Detect steady state Extract profile along x-axis at y=0.5
What does the post-processing skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill post-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.