Tem Image Analyzer
Documentstem-image-analyzerTem Image Analyzer is an Agent Skill for documents published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
Transmission Electron Microscopy image analysis skill for nanoparticle size, morphology, and crystallography assessment
How to use this skill
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- Copy and paste the prompt below.
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Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/nanotechnology/skills/tem-image-analyzer/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/tem-image-analyzer, but wait for my approval before writing files or running scripts.
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TEM Image Analyzer
Purpose
The TEM Image Analyzer skill provides comprehensive analysis of transmission electron microscopy data for nanomaterial characterization, enabling automated particle detection, size distribution analysis, and crystallographic structure determination.
Capabilities
- Automated particle detection and sizing
- Morphology classification
- Lattice fringe analysis
- Selected area electron diffraction (SAED) indexing
- High-resolution TEM (HRTEM) analysis
- STEM-HAADF imaging
Usage Guidelines
Image Analysis Workflow
-
Particle Detection
- Apply appropriate thresholding
- Use watershed for touching particles
- Count minimum 200 particles for statistics
-
Size Measurement
- Calibrate pixel size from scale bar
- Measure Feret diameter or equivalent circular diameter
- Report mean, standard deviation, distribution
-
Crystallographic Analysis
- Index SAED patterns to phase
- Measure d-spacings from lattice fringes
- Identify zone axis from HRTEM
Process Integration
- Multi-Modal Nanomaterial Characterization Pipeline
- Statistical Particle Size Distribution Analysis
- In-Situ Characterization Experiment Design
Input Schema
{
"image_path": "string",
"analysis_type": "sizing|morphology|crystallography",
"scale_bar": {"length": "number", "pixels": "number"},
"expected_material": "string (for indexing)"
}
Output Schema
{
"particle_statistics": {
"count": "number",
"mean_size": "number (nm)",
"std_dev": "number (nm)",
"size_distribution": {"bins": [], "counts": []}
},
"morphology": {
"shapes": [{"type": "string", "fraction": "number"}],
"aspect_ratio": "number"
},
"crystallography": {
"phase": "string",
"d_spacings": ["number (nm)"],
"zone_axis": "string"
}
}