By MrJoottaUpdated 2026-05-11model-router-premium@clawhub_mrjootta/model-router-premium
Task AutomationTool Use
According to AlphaMoat, as of 2026-09, Model Router has 4,879 downloads and 12 stars — ranked #1,916 of 5,078 AI skills overall, and #360 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 149 | 4.4K | 12 | - |
| SkillHub ↗ | 577 | 4.9K | 11 | — |
Platforms count differently and update at different times.
It reads a model configuration or a simple models list and chooses a fitting model for each incoming request: general or low-complexity work goes to the cheapest available model while harder requests move up. The decision draws on declared capabilities with an optional cost score, a task complexity heuristic and explicit overrides from the caller. Complexity heuristics can route high-stakes legal, medical, security or accuracy-sensitive requests to a model that is too weak. Treat routing as a hint and use human review or an explicit override for such tasks; load only trusted, reviewed model configuration.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.9K | — | 12 |
| 2026-08 | 4.4K | +3.1% | 12 |
| 2026-07 | 4.2K | +7.6% | 12 |
| 2026-06 | 3.9K | +3.6% | 11 |
| 2026-05 | 3.8K | — | 11 |
| 2026-03 | 3.3K | — | 11 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: model-router-premium version: 0.1.1 description: Route model requests based on configured models, costs and task complexity. Use for routing general/low-complexity requests to the cheapest available model and for higher-complexity requests to stronger models. --- # model-router This skill provides a compact, runnable router that inspects an OpenClaw-style configuration (or a simple models JSON) and selects an appropriate model for an incoming request based on:
- declared model capabilities and an optional cost score - task complexity (heuristic: short/simple vs long/complex) - explicit overrides (user or caller hints) Design principles - Keep decision logic small and deterministic. - Default to the cheapest model for general, not-complex tasks. - Escalate to stronger models when the task appears complex or asks for high-fidelity results. - Make model metadata explicit (capabilities, cost_score, tags) so the router is transparent and auditable. What this skill includes - scripts/router.py — a small CLI and library to pick a model given a task description and a models configuration file. - examples/models.json — example model configurations (name, provider, cost_score, capabilities). When to use - Trigger when you need to programmatically choose which LLM to call for a user request. - Use for batching or middleware routing in server apps. Usage (quick) - Prepare a models file (JSON) with model entries. See examples/models.json. - Call: python3 scripts/router.py --models examples/models.json --task "Summarize this email" --mode auto - The script prints the chosen model and the reasoning. Files - scripts/router.py — router CLI/library - examples/models.json — sample model list
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.