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model-matrix

Weighted model-routing matrix for OpenClaw (cost-aware, policy-aware, daily scorecard template).

hybredm0 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install hybredm/model-matrix

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Model Matrix Skill

Use this skill to choose the best model per category using a blended score and policy constraints.

Core Policy

  1. Use the cheapest model that preserves quality.
  2. If Anthropic is excluded, auto-promote #2.
  3. Only swap model routes when score delta is material and confidence is high.

Weights

  • Real task evals: 45%
  • Benchmarks: 30%
  • Sentiment (X/Reddit): 20%
  • Cost: 5%

Effective Routing (Current)

  • Research / Planning: Gemini 3.1 Pro
  • Complex Coding / Complex Tasks / Enterprise Discussion: GPT-5.3 Codex
  • Routine Coding + Repeat Cron Ops: GPT-5-mini
  • Citizen Sentiment (X): Grok (x_search)
  • Photo/Image: Gemini image stack
  • Video intelligence trends: Grok ecosystem

Daily Scorecard Template

Category Real Eval (45) Bench (30) Sentiment (20) Cost (5) Raw Score (/100) Raw #1 Effective #1 Confidence
Research
Planning
Coding (complex)
Coding (routine)
Creative Writing
Enterprise Discussion
Citizen Sentiment (X)

Notes

  • If Anthropic returns, include it in raw ranking and let policy choose effective winner.
  • MiniMax remains trial-only unless quality holds over 7 days.

来源信息

Source
github
Repository
openclaw/skills
Path
skills/hybredm/model-matrix
Version
1.0.0
Commit
091414280b50d3603664e82b50bff9051d6ab3fe
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

README

model-matrix

Shareable OpenClaw skill containing a practical weighted model-routing framework.

Includes:

  • blended scoring weights
  • exclusion-aware routing rule
  • daily scorecard template
  • cost-aware routing defaults

来源内容

SKILL.md
# Model Matrix Skill

Use this skill to choose the best model per category using a blended score and policy constraints.

## Core Policy
1. Use the cheapest model that preserves quality.
2. If Anthropic is excluded, auto-promote #2.
3. Only swap model routes when score delta is material and confidence is high.

## Weights
- Real task evals: 45%
- Benchmarks: 30%
- Sentiment (X/Reddit): 20%
- Cost: 5%

## Effective Routing (Current)
- Research / Planning: Gemini 3.1 Pro
- Complex Coding / Complex Tasks / Enterprise Discussion: GPT-5.3 Codex
- Routine Coding + Repeat Cron Ops: GPT-5-mini
- Citizen Sentiment (X): Grok (x_search)
- Photo/Image: Gemini image stack
- Video intelligence trends: Grok ecosystem

## Daily Scorecard Template
| Category | Real Eval (45) | Bench (30) | Sentiment (20) | Cost (5) | Raw Score (/100) | Raw #1 | Effective #1 | Confidence |
|---|---:|---:|---:|---:|---:|---|---|---:|
| Research |  |  |  |  |  |  |  |  |
| Planning |  |  |  |  |  |  |  |  |
| Coding (complex) |  |  |  |  |  |  |  |  |
| Coding (routine) |  |  |  |  |  |  |  |  |
| Creative Writing |  |  |  |  |  |  |  |  |
| Enterprise Discussion |  |  |  |  |  |  |  |  |
| Citizen Sentiment (X) |  |  |  |  |  |  |  |  |

## Notes
- If Anthropic returns, include it in raw ranking and let policy choose effective winner.
- MiniMax remains trial-only unless quality holds over 7 days.
README
# model-matrix

Shareable OpenClaw skill containing a practical weighted model-routing framework.

Includes:
- blended scoring weights
- exclusion-aware routing rule
- daily scorecard template
- cost-aware routing defaults

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