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Agent Skill · 待分类

synthesis-llm-setup(synthesis LLM 设置)

synthesis-llm-setup 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。

synthesis-llm-setup 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在待分类类的 11,209 个 Skill 里排第 9,067。

总安装 261周安装 11GitHub Stars 3下载 92核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:synthesis-llm-setup(synthesis LLM 设置)
来源仓库:https://github.com/rajivpant/synthesis-skills
仓库路径:skills/synthesis-llm-setup
安装命令:
npx skills add https://github.com/rajivpant/synthesis-skills --skill synthesis-llm-setup
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/rajivpant/synthesis-skills --skill synthesis-llm-setup

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

synthesis-llm-setup 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 中围绕仓库状态和代码变更进行整理。通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。

安装前需确认权限范围、维护状态及是否涉及联网或文件操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/synthesis-llm-setup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/rajivpant/synthesis-skills --skill synthesis-l…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

synthesis-llm-setup 怎么安装?

在终端执行 npx skills add https://github.com/rajivpant/synthesis-skills --skill synthesis-llm-setup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

synthesis-llm-setup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

synthesis-llm-setup 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

synthesis-llm-setup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

synthesis-llm-setup 用的人多吗?

公开数据是累计安装 261 次,近一周安装 11 次,下载 92 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

synthesis-llm-setup 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
synthesis-llm-setup

synthesis LLM 设置 · 本页

2613需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

LLM Setup

Configure Claude Projects, ChatGPT GPTs, Gemini Gems, and other LLM platforms using compiled AI Knowledge content from the ragbot system.

Configuration

These values are user-specific. Update them for your environment.

SettingValueDescription
ai_knowledge_repoai-knowledge-{name}/Your ai-knowledge repository root
compiled_instructions_pathcompiled/{project}/instructions/Path to LLM-specific compiled instructions
knowledge_fileall-knowledge.mdConcatenated knowledge file (auto-generated by CI/CD)
source_dirsource/Directory to edit directly (knowledge concatenation is automatic)

Architecture overview

The AI Knowledge system produces platform-specific content for each LLM's project/custom instruction system.

OperationWhereWhen
Knowledge concatenation (all-knowledge.md)CI/CD (GitHub Actions)Every push to source/
Instruction compilationLocal (ragbot compile)When instructions change (rare)
RAG indexingLocal (ragbot index)When content changes and RAG is needed

Output structure

ai-knowledge-{name}/
├── compiled/
│   └── {project}/
│       └── instructions/         # LLM-specific custom instructions
│           ├── claude.md
│           ├── chatgpt.md
│           └── gemini.md
└── all-knowledge.md              # Concatenated knowledge (auto-generated by CI/CD)

Key principle: Edit source/ files directly. Knowledge concatenation is automatic.

Knowledge delivery strategy:

  • Claude: GitHub sync the repo (Claude reads all-knowledge.md directly)
  • ChatGPT: Upload all-knowledge.md
  • Gemini: Upload all-knowledge.md (works within the 10-file Gem limit)

Claude Projects setup

Custom instructions

  1. Create a new Claude Project (or open an existing one).
  2. Go to Project Knowledge, then Custom Instructions.
  3. Copy content from compiled/{project}/instructions/claude.md.
  4. Paste into the custom instructions field.

Knowledge files

Option A: GitHub sync (recommended)

  1. Connect your GitHub account to Claude.
  2. Sync the repository containing your ai-knowledge repo.
  3. Claude indexes all-knowledge.md and source files automatically.

Option B: Manual upload

  1. Go to Project Knowledge, then Files.
  2. Upload all-knowledge.md from the repo root.

ChatGPT GPT setup

Creating a GPT

  1. Go to https://chat.openai.com/gpts/editor
  2. Click "Create a GPT".
  3. Configure:

- Name: Your project name - Description: Brief description - Instructions: Copy from compiled/{project}/instructions/chatgpt.md

Knowledge files

  1. In the GPT editor, go to the Knowledge section.
  2. Upload all-knowledge.md from the repo root.

Gemini Gems setup

Creating a Gem

  1. Go to https://gemini.google.com/gems
  2. Create a new Gem.
  3. Paste instructions from compiled/{project}/instructions/gemini.md.

Knowledge files

  1. Upload all-knowledge.md from the repo root.
  2. This single file contains all runbooks and datasets merged together.
  3. Works well within Gemini's 10-file limit per Gem.

Other LLMs (Grok, etc.)

  1. Copy instructions from compiled/{project}/instructions/ (use the closest match).
  2. Upload all-knowledge.md from the repo root.

Compilation and inheritance

How it works

Each user compiles projects in their own repo. What content gets included depends on inheritance.

Example: Compiling in ai-knowledge-personal:

compiled/
├── personal/                     # Baseline (ragbot + personal)
├── company/                      # personal + company merged
├── client-a/                     # personal + company + client-a merged
└── client-b/                     # personal + client-b merged

Example: Compiling in ai-knowledge-company (team member without access to personal):

compiled/
├── company/                      # Baseline (ragbot + company, NO personal)
├── client-a/                     # company + client-a (NO personal)
└── client-c/                     # company + client-c

Privacy model

Content is only included if the user has access to the source repo:

  • Private content (ai-knowledge-{personal}) only appears in that user's compilations
  • Team members get team content but not personal content
  • Clients only get client-specific content

Running instruction compilation

# Compile instructions for a project
ragbot compile --project {name}

# Without LLM API calls (just assemble)
ragbot compile --project {name} --no-llm

# Force recompile (ignore cache)
ragbot compile --project {name} --force

# Verbose output
ragbot compile --project {name} --verbose

Knowledge concatenation (all-knowledge.md) is handled automatically by CI/CD -- no manual step needed.

Step-by-step workflow

  1. Compile instructions (only if instructions changed): ragbot compile --project {name} --no-llm
  2. For each project (e.g., client-a):

- Claude: Create project, copy compiled/client-a/instructions/claude.md to custom instructions, GitHub sync the repo - ChatGPT: Create GPT, copy compiled/client-a/instructions/chatgpt.md to instructions, upload all-knowledge.md - Gemini: Create Gem, copy compiled/client-a/instructions/gemini.md to instructions, upload all-knowledge.md

  1. Verify by testing each project with a representative query.

Updating projects

When to update

Knowledge (all-knowledge.md): Updates automatically via CI/CD on every push to source/. No manual step.

Instructions: Recompile only when instructions change (rare):

ragbot compile --project {name} --force

LLM sync:

  • Claude: GitHub sync auto-updates
  • ChatGPT/Gemini: Re-upload all-knowledge.md after source changes

Troubleshooting

"Instructions too long"

  • Move detailed content to knowledge files
  • Check manifest.yaml for token counts
  • Keep instructions focused on identity and behavior

"Knowledge not being used"

  • Verify all-knowledge.md was uploaded correctly
  • Check if content is in instructions vs knowledge
  • For Claude: ensure GitHub sync is active and pointing to the repo

"Inheritance not working"

  • Verify my-projects.yaml exists in the personal repo
  • Check inheritance chain in compile-config.yaml
  • Run with --verbose to see inheritance resolution

"Content from wrong repo appearing"

  • Check which repo you are compiling in
  • Verify you have access to expected repos
  • Remember: content only comes from repos you can access

平台分布

Codex

37.33%
按下载量换算34

Claude

28.82%
按下载量换算27

Cursor

21.82%
按下载量换算20

Gemini CLI

9.28%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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