home / skills / hexbee / hello-skills / china-model-selection-guide
This skill helps select the best-fit model from several options based on input type, task complexity, and delivery goals to optimize routing.
npx playbooks add skill hexbee/hello-skills --skill china-model-selection-guideReview the files below or copy the command above to add this skill to your agents.
---
name: china-model-selection-guide
description: China model selection and task-routing guide for Doubao-Seed-2.0-Code, GLM-5, MiniMax-M2.5, and Kimi-K2.5. Use when users need to choose the best-fit model by input type, task complexity, engineering constraints, and delivery goals, including staged multi-model workflows.
---
# China Model Selection Guide
Follow this flow to recommend models. Load `references/china-model-selection-guide.md` for the full Chinese playbook, scenarios, strengths, and prompt templates.
## Quick Triage
Answer two questions first, then give a primary pick.
1. Identify core input type
- Visual-first input (UI mockups, screenshots, sketches): prefer `Doubao-Seed-2.0-Code`
- Very long text or many files (dozens of docs, full codebase): prefer `Kimi-K2.5`
- Structured engineering prompts (clear coding requirements, Shell commands): prefer `GLM-5` or `MiniMax-M2.5`
2. Identify task complexity
- Complex reasoning or autonomous planning (system design, codebase refactor): prefer `GLM-5`
- Cross-language engineering (Python/C++, Java/Go): prefer `MiniMax-M2.5`
- Clear task but heavy execution (UI-to-code, template generation): prefer `Doubao-Seed-2.0-Code`
## Tie-Break Rules
When multiple models fit, decide in this order.
1. Satisfy hard constraints first: vision, long-context, cross-language, agentic planning
2. Then compare cost and latency: pick better price/performance at similar quality
3. Finally split by phase: allow multi-model routing inside one project
## Composite Task Routing
Use this default pipeline.
1. Planning: `GLM-5` for architecture, decomposition, interfaces, schema decisions
2. Build:
- Frontend and visual replication: `Doubao-Seed-2.0-Code`
- Backend scripts, cross-language tasks, terminal automation: `MiniMax-M2.5`
3. Integration debugging: route hard cross-module issues back to `GLM-5`
4. Documentation handoff: send codebase and large document sets to `Kimi-K2.5`
## Output Format
Always include these in recommendations.
1. Decision: primary model + fallback model
2. Rationale: map to input type, complexity, and constraints
3. Risks: likely weak points and rollback strategy
4. Execution: a ready-to-use prompt draft
## References
- Full guide and examples (Chinese): `references/china-model-selection-guide.md`
This skill provides a compact China-focused model selection and task-routing guide for Doubao-Seed-2.0-Code, GLM-5, MiniMax-M2.5, and Kimi-K2.5. It helps teams pick the best-fit model based on input type, task complexity, engineering constraints, and delivery goals. The skill also defines a default multi-model pipeline for staged projects and mandates a consistent recommendation output format.
The guide first triages by input type (visual, long-text, structured prompts) and task complexity (reasoning, cross-language, execution-heavy). It applies tie-break rules that prioritize hard constraints (vision, long context, cross-language, agentic planning), then cost/latency, then project phase to select primary and fallback models. For composite tasks it prescribes a default pipeline: GLM-5 for planning, Doubao for visual/frontend, MiniMax for backend/automation, and Kimi for large-document handoff. Every recommendation includes decision, rationale, risks, and an execution-ready prompt draft.
What if multiple models match the triage?
Follow tie-break rules: satisfy hard constraints first, then compare cost/latency, and finally split by project phase to allow multi-model routing.
How should recommendations be presented?
Always include: Decision (primary + fallback), Rationale mapped to input/constraints, Risks and rollback strategy, and an execution-ready prompt draft.