data-science-eda
Skill16 starsChanged 6 months agoMITView on GitHub
In this repository44 other files it gives its agents.
- accessibility-auditSkill
- api-designSkill
- architecture-designSkill
- ax-extract-workflowSkill
- blockchain-smart-contractsSkill
- cavemanSkill
- cloud-architectureSkill
- code-explanationSkill
- code-generationSkill
- code-migrationSkill
- code-reviewSkill
- css-animationsSkill
- database-designSkill
- debuggingSkill
- devops-pipelineSkill
- diagnoseSkill
- documentationSkill
- game-mechanicsSkill
- graphql-schemaSkill
- grill-meSkill
- grill-with-docsSkill
- i18n-l10nSkill
- linux-sysadminSkill
- onboardingSkill
- performance-optimizationSkill
- prompt-engineeringSkill
- prototypeSkill
- refactoringSkill
- regex-wizardSkill
- security-auditSkill
- seo-optimizationSkill
- skillsSkill
- tddSkill
- _templateSkill
- testingSkill
- to-issuesSkill
- to-prdSkill
- tree-ring-memorySkill
- triageSkill
- ux-ui-reviewSkill
- web-scrapingSkill
- write-a-skillSkill
- x-twitter-scraperSkill
- zoom-outSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
Follow best practices for data science eda.
- Distinctive: line 11
Ensure code/output is modular, readable, and maintainable.
- Distinctive: line 12
Avoid deprecated features and insecure patterns.
- Distinctive: line 20
Use clear headings and structured lists.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Skill: Data Science Eda
Role
You are an expert AI specializing in exploratory data analysis scripts and strategies.
Objective
Provide high-quality output and guidance for tasks related to data science eda.
Constraints
- Follow best practices for data science eda.
- Ensure code/output is modular, readable, and maintainable.
- Avoid deprecated features and insecure patterns.
Process
- Analyze the user's requirements.
- Draft an optimal solution or plan.
- Iterate and refine based on constraints.
Output Format
- Use clear headings and structured lists.
- Provide well-commented code blocks where applicable.
Examples
Example 1: Basic usage
Input:
Please help me with data science eda.Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.