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synthesis-llm-setupsynthesis LLM 设置

Agent Skill

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。

总安装

261

周安装

11

GitHub Stars

3

下载量

92
CodexClaudeCursorGemini CLI

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

2

许可证

unknown

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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

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

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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