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- Microsim Matcher
microsim-matcher_skill
- Python
14
GitHub Stars
2
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
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npx veilstrat add skill dmccreary/claude-skills --skill microsim-matcher- README.md7.3 KB
- SKILL.md24.0 KB
Overview
This skill analyzes diagram, chart, or simulation specifications and returns a ranked list of the most suitable MicroSim generator skills to use. It compares the specification against capabilities of available generators and provides match scores (0-100) with concise, actionable reasoning. Use it to quickly choose the right generator for a given visualization need.
How this skill works
The matcher reads the user specification (free text, markdown, JSON/YAML or requirements list), extracts visual type, data type, interactivity and key trigger words, then compares those characteristics against each generator's capabilities. It scores each generator on data fit, interactivity fit, visual style fit, and trigger-word bonuses, ranks the results, and returns a prioritized list with detailed justification and limitations for each recommendation.
When to use it
- You have a diagram or visualization spec but no preferred generator.
- You want a ranked recommendation from the set of MicroSim generators.
- You need to compare trade-offs between generators for a given spec.
- You want clear reasoning about interactivity and data-type fit.
- You suspect a spec may require multiple generators or custom work.
Best practices
- Provide a clear spec: visual type, sample data, interactivity needs, and domain context.
- Mention explicit trigger words (timeline, map, network, function, Venn, animation) to improve matching.
- Allow the matcher to run its reference-version check or confirm if you want to skip it.
- Use the ranked output to pick a primary generator and one fallback with complementary strengths.
- If no generator scores ≥70, refine the spec or plan a custom p5.js simulation.
Example use cases
- Deciding between a timeline or line chart for historical event visualization.
- Choosing a network library vs. a flowchart tool for course prerequisite maps.
- Selecting Plotly for function plots versus Chart.js for aggregated statistics.
- Determining whether a geographic dataset should use a map generator or a bubble chart.
- Evaluating whether a custom p5.js animation is needed for an interactive simulation.
FAQ
Plain text descriptions, structured markdown, JSON/YAML data, and requirements lists are all supported.
What does a low top score indicate?
A top score below 70 usually means the spec is ambiguous, requires multiple generators, or needs custom development.
Can I skip the reference/version check?
Yes—you can skip it if you understand the risk that generator capability data may be outdated.