model-selection
model-selection is an Agent task delegation & fan-out skill for AI agents, published by Resgrid in the Core repository on GitHub. Skillspace rates model-selection D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Strategic Claude model selection for .NET development workflows. Guides when to use Opus 4.6 (deep reasoning, architecture, ambiguous problems) vs Sonnet 4.6 (throughput, large context, routine implementation) vs Haiku 4.5 (fast, cheap subagent tasks). Covers model switching workflows, subagent model assignment, and cost-effective task routing. Load this skill when choosing models for tasks, optimizing costs, working with subagents, or when the user mentions "model", "Opus", "Sonnet", "Haiku", "which model", "cost", "switch model", or "fast mode".
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 218
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 3 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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