model-selection-advisor
Choose the right LLM for a task by trading off quality, cost, latency, and constraints. Use when asked which model to use, whether to upgrade/downgrade a model, how to cut LLM costs without hurting quality, or to justify a model choice. Produces a recommendation with the decision criteria, a per-option comparison, a routing strategy (cheap-by-default, escalate when needed), and how to validate the choice with an eval.
npx skills add mohitagw15856/pm-claude-skills --skill model-selection-advisor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Model Selection Advisor Skill The right model is rarely "the biggest one" or "the cheapest one" — it's the smallest model that clears the task's quality bar within its latency and cost budget, with a path to escalate the hard cases. This skill makes that trade-off explicit and defensible, and ties it to an eval so the choice is measured, not vibes. ## Working from a brief Given "what model should I use for summarising support tickets?", **deliver a concrete recommendation anyway** — infer the task's difficulty, volume, and latency sensitivity, label the assumptions, and recommend. Never hand back "it depends" with no pick; give a default and the condition under which you'd change it. ## Required Inputs Ask for these only if they aren't already provided (else infer and label): - **The task** — what the model does, and an example input/output. How hard is it (extraction vs. reasoning vs. open-ended)? - **Quality bar** — what "good enough" means, and the cost of a wrong answer. - **Volume & latency** — requests/day and how fast a response must come back (interactive vs. batch). - **Constraints** — budget, context-length needs, tool use, privacy/region, and whether outputs must be re
- Working from a brief
- Required Inputs
- Output Format
- Model Recommendation: [task]
- Quality Checks
- Anti-Patterns
- Based On
What does the model-selection-advisor skill do?
Choose the right LLM for a task by trading off quality, cost, latency, and constraints. Use when asked which model to use, whether to upgrade/downgrade a model, how to cut LLM costs without hurting quality, or to justify a model choice. Produces a recommendation with the decision criteria, a per-option comparison, a routing strategy (cheap-by-default, escalate when needed), and how to validate the choice with an eval.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill model-selection-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.