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context-builder上下文构建器

Agent Skill

context-builder 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

1,248

周安装

50

GitHub Stars

141

下载量

404
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:context-builder(上下文构建器)
来源仓库:https://github.com/glebis/claude-skills
仓库路径:skills/context-builder
安装命令:
npx skills add https://github.com/glebis/claude-skills --skill context-builder
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/glebis/claude-skills --skill context-builder

简介

context-builder 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于需要根据关键词或任务场景从来源线索中筛选信息的场景。
  • 通过 npx skills add 命令安装,需确认权限范围和维护状态后再使用。
  • 建议结合原始 README 核验具体用法,注意是否触发联网或文件读写操作。
  • 安装前应评估是否会执行命令或访问外部资源,确保符合安全策略。

SKILL.md

Context Builder

Generate interactive context-building prompts for consulting clients. These prompts are designed to be run in Claude Code -- they guide a team through structured questions using AskUserQuestion, generate output files per section, and compile everything into a reusable CLAUDE.md.

Workflow

Phase 1: Intake (AskUserQuestion)

Ask all intake questions using AskUserQuestion with closed-list options. Gather:

Question 1: Company identifier

  • Options: "I have a website URL", "I have a company name", "I have both"
  • Follow up to get the actual URL/name

Question 2: Who will use this prompt?

  • Options: "Specific person (name + role)", "A team (no specific person)", "Unknown / TBD"
  • If specific person: follow up for name and role

Question 3: Primary consulting focus (multiSelect)

  • "AI automation of current operations"
  • "Existential strategy (what survives AI)"
  • "New business models / pivots"
  • "Product development with AI"

Question 4: Industry

  • "Marketing / Advertising"
  • "Manufacturing / Construction"
  • "SaaS / Software"
  • "Professional Services / Consulting"
  • (Other)

Question 5: Existing context in vault?

  • "Yes, there's a call transcript"
  • "Yes, there are notes/files"
  • "No existing context"
  • If yes: ask for filename or search term to locate it

Question 6: Session language

  • "Russian (questions in Russian, output in English)"
  • "English throughout"
  • "Other"

Phase 2: Research (automated)

Run these research steps in parallel where possible:

  1. Web research: Use WebSearch and WebFetch (via Task agent) to gather:

- What the company does, products/services - Target market, company size, geography - Tech stack, partnerships - Recent news, funding, team info - Competitive landscape

  1. Vault search: Search the Obsidian vault for:

- Transcripts mentioning the company name (Grep in vault root and Daily/) - People files for contacts at the company (People/ folder) - Any existing notes or research

  1. Transcript analysis (if found): Extract from call transcripts:

- Team members and their roles - Current AI tool usage - Pain points and concerns mentioned - Specific processes described - Questions raised by the team

Phase 3: Section Selection (AskUserQuestion)

Present a curated set of sections based on the consulting focus. Use AskUserQuestion with multiSelect to let the user pick which sections to include.

Section Library

Draw from references/section-library.md for the full section catalog. Default section sets by focus:

AI Automation focus:

  1. Process Inventory, 2. Pain Points & Waste, 3. Current Tech Stack, 4. AI Opportunity Mapping, 5. People & Org, 6. Data Reality Check, 7. Quick Wins

Existential Strategy focus:

  1. Revenue & Service Map, 2. The Existential Question, 3. Client Value Chain, 4. New Business Models, 5. Data & Knowledge Assets, 6. People & Org, 7. Quick Wins & Pilots

Full Assessment (both): All 10 sections from the library.

After section selection, ask:

Express mode grouping: Present a suggested grouping of selected sections into 4 Express mega-sections. Let user confirm or adjust.

Phase 4: Generation

Generate two files:

1. The Context-Builder Prompt

Save to: Claude-Drafts/{company-slug}-context-prompt.md

Structure (follow the template in references/prompt-template.md):

---
created_date: '[[YYYYMMDD]]'
type: draft
topic: consulting, AI transformation, {industry}
for: {contact person or team name}
---

# AI Transformation Context Builder -- {Company Name}

## About {Company}
  [Generated from research -- company description, size, market, positioning]

## Current State
  **What's working:** [from research + transcript]
  **The gap:** [from research + transcript]
  [If existential concerns found: **Existential context:**]

## Mode Selection
  [Express vs Deep Dive with section descriptions]

## How This Works
  [Standard interactive session instructions]

## Session Resumability
  [Standard resumability logic]

## Interactive Flow
  [Selected sections with tailored questions]

## Output Files
  [One file per section + final CLAUDE.md]

## Relevant Frameworks
  [Selected from references/frameworks.md based on focus]

2. Instruction File (optional)

If the prompt will be sent to someone external, generate a short instruction file: Claude-Drafts/{company-slug}-context-instructions.md

Containing:

  • What this file is and how to use it
  • Prerequisites (Claude Code or similar)
  • The two modes explained simply
  • What they'll get on output
  • Privacy note (they can share as much or as little as they want)

Phase 5: Delivery (AskUserQuestion)

Question: What to do with the generated files?

  • "Save to vault only"
  • "Save and send via Telegram"
  • "Save and let me review first"

If Telegram: ask for the recipient handle/name, then send using the telegram skill (intro message + file).

Key Principles

  • Maximize closed-list questions: Every AskUserQuestion should have concrete options. Minimize free-text input.
  • Research before asking: Don't ask the user things that can be found via web search or vault search.
  • Tailor sections to context: If the transcript reveals specific concerns (e.g., existential fears, specific tech stack), customize the section questions to reference those specifics.
  • Bake in discovered context: The generated prompt's "About" and "Current State" sections should be rich with researched details so the person running the prompt gets a warm start.
  • Language awareness: If session language is Russian, all AskUserQuestion interactions during prompt execution should be in Russian, but output files in English.

Resources

references/

  • section-library.md -- Full catalog of available sections with question templates
  • prompt-template.md -- Structural template for the generated prompt
  • frameworks.md -- Consulting frameworks to selectively include

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

35.15%
按下载量换算142

Claude

27.4%
按下载量换算111

Cursor

19.59%
按下载量换算79

Gemini CLI

10.1%
按下载量换算41

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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