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dust-llmdust LLM 命令行

Agent Skill

dust-llm 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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1,294

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下载量

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dust-llm(dust LLM 命令行)
来源仓库:https://github.com/dust-tt/dust
仓库路径:skills/dust-llm
安装命令:
npx skills add https://github.com/dust-tt/dust --skill dust-llm
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/dust-tt/dust --skill dust-llm

简介

dust-llm 用于指导添加对新发布大语言模型的支持,涵盖模型配置、定价更新和测试集成。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中需要扩展 LLM 能力或维护模型注册表时使用。
  • 可通过 npx skills add 命令从 GitHub 安装,需确认权限范围和维护状态后再使用。
  • 使用前建议核验是否会触发联网、命令执行或文件读写操作。
  • 可结合来源仓库和原始 README 进一步了解具体用法和限制条件。

SKILL.md

Adding Support for a New LLM Model

This skill guides you through adding support for a newly released LLM.

Quick Reference

Files to Modify

FilePurpose
front/types/assistant/models/{provider}.tsModel ID + configuration
front/lib/api/assistant/token_pricing.tsPricing per million tokens
front/types/assistant/models/models.tsCentral registry
front/lib/api/llm/clients/{provider}/types.tsRouter whitelist
sdks/js/src/types.tsSDK types
front/components/providers/types.tsUI availability (optional)
front/lib/api/llm/tests/llm.test.tsIntegration tests

Prerequisites

Before adding, gather:

  • Model ID: Exact provider identifier (e.g., gpt-4-turbo-2024-04-09)
  • Context size: Total context window in tokens
  • Pricing: Input/output cost per million tokens
  • Capabilities: Vision, structured output, reasoning effort levels
  • Tokenizer: Compatible tokenizer for token counting

Step-by-Step: Adding an OpenAI Model

Step 1: Add Model Configuration

Edit front/types/assistant/models/openai.ts:

export const GPT_4_TURBO_2024_04_09_MODEL_ID = "gpt-4-turbo-2024-04-09" as const;

export const GPT_4_TURBO_2024_04_09_MODEL_CONFIG: ModelConfigurationType = {
  providerId: "openai",
  modelId: GPT_4_TURBO_2024_04_09_MODEL_ID,
  displayName: "GPT 4 turbo",
  contextSize: 128_000,
  recommendedTopK: 32,
  recommendedExhaustiveTopK: 64,
  largeModel: true,
  description: "OpenAI's GPT 4 Turbo model for complex tasks (128k context).",
  shortDescription: "OpenAI's second best model.",
  isLegacy: false,
  isLatest: false,
  generationTokensCount: 2048,
  supportsVision: true,
  minimumReasoningEffort: "none",
  maximumReasoningEffort: "none",
  defaultReasoningEffort: "none",
  supportsResponseFormat: false,
  tokenizer: { type: "tiktoken", base: "cl100k_base" },
};

Step 2: Add Pricing

Edit front/lib/api/assistant/token_pricing.ts:

const CURRENT_MODEL_PRICING: Record<BaseModelIdType, PricingEntry> = {
  // ... existing
  "gpt-4-turbo-2024-04-09": {
    input: 10.0,  // USD per million input tokens
    output: 30.0, // USD per million output tokens
    cache_read_input_tokens: 1.0,      // Optional: cached reads
    cache_creation_input_tokens: 12.5, // Optional: cache creation
  },
};

Step 3: Register in Central Registry

Edit front/types/assistant/models/models.ts:

export const MODEL_IDS = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_ID,
] as const;

export const SUPPORTED_MODEL_CONFIGS: ModelConfigurationType[] = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_CONFIG,
];

Step 4: Update Router Whitelist

Edit front/lib/api/llm/clients/openai/types.ts:

export const OPENAI_WHITELISTED_MODEL_IDS = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_ID,
] as const;

Step 5: Update SDK Types

Edit sdks/js/src/types.ts:

const ModelLLMIdSchema = FlexibleEnumSchema<
  // ... existing
  | "gpt-4-turbo-2024-04-09"
>();

Step 6: Add to UI (Optional)

Edit front/components/providers/types.ts:

export const USED_MODEL_CONFIGS: readonly ModelConfig[] = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_CONFIG,
] as const;

Step 7: Test (Mandatory)

Edit front/lib/api/llm/tests/llm.test.ts:

const MODELS = {
  // ... existing
  [GPT_4_TURBO_2024_04_09_MODEL_ID]: {
    runTest: true,  // Enable for testing
    providerId: "openai",
  },
};

Run test:

RUN_LLM_TEST=true npx vitest --config lib/api/llm/tests/vite.config.js lib/api/llm/tests/llm.test.ts --run

After test passes, set runTest: false to avoid expensive CI runs.

Adding Anthropic Models

Same pattern with Anthropic-specific files:

  1. front/types/assistant/models/anthropic.ts - Add CLAUDE_X_MODEL_ID and config
  2. front/lib/api/llm/clients/anthropic/types.ts - Add to ANTHROPIC_WHITELISTED_MODEL_IDS
  3. front/types/assistant/models/models.ts - Register in central registry
  4. front/lib/api/assistant/token_pricing.ts - Add pricing
  5. sdks/js/src/types.ts - Update SDK types
  6. Test and validate

Model Configuration Properties

PropertyDescription
supportsVisionCan process images
supportsResponseFormatSupports structured output (JSON)
minimumReasoningEffortMin reasoning level ("none", "low", "medium", "high")
maximumReasoningEffortMax reasoning level
defaultReasoningEffortDefault reasoning level
tokenizerTokenizer config for token counting

Validation Checklist

  • Model config added to provider file
  • Pricing updated (input, output, cache if applicable)
  • Registered in central registry (MODEL_IDS + SUPPORTED_MODEL_CONFIGS)
  • Router whitelist updated
  • SDK types updated
  • UI config added (if needed)
  • Integration test passes
  • Test disabled after validation

Troubleshooting

Model not in UI: Check USED_MODEL_CONFIGS in front/components/providers/types.ts

API calls failing: Verify model ID matches provider's exact identifier, check router whitelist

Token counting errors: Validate context size and tokenizer configuration

Pricing issues: Ensure prices are per million tokens in USD

Reference

  • See front/types/assistant/models/openai.ts and anthropic.ts for examples
  • Provider docs: OpenAI, Anthropic, Google, Mistral

适合场景

01

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02

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03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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OpenCode

13.39%
按下载量换算61

Codex

7.54%
按下载量换算34

Antigravity

3.92%
按下载量换算18

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安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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