pick-model
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
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add whyashthakker/estack --skill pick-modelCompatible with
Supported agents
Choose the right AI model for each task in a project or workflow using current prices, not memory.
Finds where models are used, groups calls by task type, compares live prices from the explainx.ai MCP server, and recommends a model per task with the cost change.
Use when the user asks "which model should I use", "this is too expensive", "cut my AI bill", "cheaper model for X", or sets up subagents, bots, or pipelines that call LLMs.
| name | pick-model |
| description | Choose the right AI model for each task in a project or workflow using current prices, not memory. Finds where models are used, groups calls by task type, compares live prices from the explainx.ai MCP server, and recommends a model per task with the cost change. Use when the user asks "which model should I use", "this is too expensive", "cut my AI bill", "cheaper model for X", or sets up subagents, bots, or pipelines that call LLMs. |
Goal: each task runs on the cheapest model that does it well enough. The strongest model on everything wastes money. The cheapest model on everything wastes time on bad output.
Search the code and config for model use: model IDs, provider SDK calls, agent and subagent definitions, cron jobs and bots. For each call, write down:
With the explainx.ai MCP server: call compare_model_prices for the candidate models, and search_ai_directory or get_ai_directory_entry for model details. Prices change often. Never use prices from memory. Without the server, use the providers' pricing pages and give the date you read them.
Router prices can differ from direct provider prices. Say which one you used.
| Task type | Usual fit |
|---|---|
| Bulk, mechanical (classify, extract, tag, reformat) | Smallest fast model that passes a 10-sample check |
| Writing people read (posts, emails, summaries) | Mid-tier model; strongest only for high-stakes text |
| Code that ships, security review, hard reasoning | Strongest model, or mid-tier plus a strong-model review |
| Long documents | The model with the best price per long-context token, not the best price per short prompt |
Run a small test before a big switch: the same 5 to 10 real inputs on the old and new model, side by side. Show the user the outputs, not only the scores.
A table: task, current model, recommended model, monthly cost before and after (show the math), and risk. Then the total saving. Do not change config until the user agrees. When they agree, make the change in one place (a config constant or env var) so it is easy to switch back.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpick-modelExecute the skills CLI command in your project's root directory to begin installation:
epx add whyashthakker/estack --skill pick-modelFetches pick-model from whyashthakker/estack 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:
.cursor/skills/pick-model
Restart Cursor to activate pick-model. Access via /pick-modelin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
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.
Useful defaults in pick-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
pick-model has been reliable in day-to-day use. Documentation quality is above average for community skills.
pick-model is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: pick-model is focused, and the summary matches what you get after install.
pick-model has been reliable in day-to-day use. Documentation quality is above average for community skills.
pick-model fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added pick-model from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: pick-model is focused, and the summary matches what you get after install.
Useful defaults in pick-model — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
pick-model is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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