Agent skill / aj-geddes
Model explainability makes machine learning decisions transparent and interpretable, enabling trust, compliance, debugging, and actionable insights from predictions.
Core file
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionml-model-explanationExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill ml-model-explanationFetches ml-model-explanation from aj-geddes/useful-ai-prompts and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ml-model-explanation. Access via /ml-model-explanationin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Copy the command for your terminal
Package manager
npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill ml-model-explanationWorks with
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
aj-geddes/useful-ai-prompts
aj-geddes/useful-ai-prompts
aj-geddes/useful-ai-prompts
davila7/claude-code-templates
intellectronica/agent-skills
am-will/codex-skills
ml-model-explanation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ml-model-explanation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ml-model-explanation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ml-model-explanation reduced setup friction for our internal harness; good balance of opinion and flexibility.
ml-model-explanation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend ml-model-explanation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for ml-model-explanation matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for ml-model-explanation matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: ml-model-explanation is focused, and the summary matches what you get after install.
Useful defaults in ml-model-explanation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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