MCPunk

MCPunk provides tools for performing Roaming RAG

Skills

Explore the skills and capabilities of this skillset.

get_task

Get a single task. Do not use this tool unless explicitly told to do so. After you complete the task, mark it as done by calling the `set_task_done` tool.

add_tasks

Add tasks to be completed by an LLM in the future. Do not add a task unless explicitly instructed to do so. When adding tasks, provide all required context. For example: step 1 set up the ~/git/p1 and ~/git/p2 repos projects step 2 load the diff with ref develop step 3 confirm that the function added in /examples/script.py is consistent with the existing /examples/other_script.py file. The common_prefix is prefixed to each task's action (if not None), it's provided to avoid having to repeat the common context for each task. Call this tool multiple times to add many tasks.

get_a_joke

Get a really funny joke! For testing :)

chunk_details

Get full content of a specific chunk. Returns chunk content as string. Common patterns: 1. Final step after find_matching_chunks_in_file finds relevant chunks 2. Examining implementations after finding definitions/uses

diff_with_ref

Return a summary of the diff between HEAD and the given ref. You probably want the ref to be the 'base' branch like develop or main, off which PRs are made - and you can likely determine this by viewing the most recently checked out branches.

mark_task_done

Set a task as done wth a specific outcome. You can call this multiple times to update the outcome.

configure_project

Configure a new project containing files. Each file in the project is split into 'chunks' - logical sections like functions, classes, markdown sections, and import blocks. After configuring, a common workflow is: 1. list_all_files_in_project to get an overview of the project (with an initial limit on the depth of the search) 2. Find files by function/class definition: find_files_by_chunk_content(... ["def my_funk"]) 3. Find files by function/class usage: find_files_by_chunk_content(... ["my_funk"]) 4. Determine which chunks in the found files are relevant: find_matching_chunks_in_file(...) 5. Get details about the chunks: chunk_details(...) Use ~ (tilde) literally if the user specifies it in paths.

list_all_files_in_project

List all files in a project, returning a file tree. This is useful for getting an overview of the project, or specific subdirectories of the project. A project may have many files, so you are suggested to start with a depth limit to get an overview, and then continue increasing the depth limit with a filter to look at specific subdirectories.

find_files_by_chunk_content

Step 1: Find files containing chunks with matching text. Returns file tree only showing which files contain matches. You must use find_matching_chunks_in_file on each relevant file to see the actual matches. Example workflow: 1. Find files: files = find_files_by_chunk_content(project, ["MyClass"]) 2. For each file, find actual matches: matches = find_matching_chunks_in_file(file, ["MyClass"]) 3. Get content: content = chunk_details(file, match_id)

find_matching_chunks_in_file

Step 2: Find the actual matching chunks in a specific file. Required after find_files_by_chunk_content or list_all_files_in_project to see matches, as those tools only show files, not their contents. This can be used for things like: - Finding all chunks in a file that make reference to a specific function (e.g. find_matching_chunks_in_file(..., ["my_funk"]) - Finding a chunk where a specific function is defined (e.g. find_matching_chunks_in_file(..., ["def my_funk"]) Returns array of {n: name, t: type, id: identifier, chars: length}

list_most_recently_checked_out_branches

List the n most recently checked out branches in the project

Configuration

Customize the skillset to fit your needs.
MCP Server

Connect to MCP Server

MCPunk

客服文档助手
AI 助手协助客服团队创建高质量的支持文档,包括常见问题、工单回复、道歉信和标准操作程序。引导您创建内部资源和面向客户的材料。
Google 分析师
逐步指南,教您如何将 Google Analytics 4 (GA4) 属性连接到 Google 分析师代理。涵盖创建 Google Cloud 服务账户、启用 Analytics Data API、授予 GA4 查看者访问权限,以及配置代理以支持会话、用户、跳出率、转换等指标。非常适合快速在 Bika.ai 中设置 GA4 数据报告。
社区活动分析员
分析社区活动截图,报告参与趋势和讨论亮点。上传社区互动的截图,该智能体会生成一份清晰的markdown报告,总结参与水平、关键讨论主题和显著亮点 — 非常适合社区经理、市场营销人员和产品团队。
AI 网页工程师
AI Programmer 是一个 AI 页面,可以将您的原始发布说明转换为时尚、可发布的 HTML 页面。
股票新闻报告员
这个 AI 智能体实时监控和分析美国主要股票新闻,生成结构化的投资报告,提供关键见解、市场反应和行业级别的总结。
工单管理员
收集、分析和管理来自表单和数据库的支持工单,帮助您高效地跟踪、优先处理和回应。
品牌设计师
一款专为初创数字产品设计的品牌营销 AI 助手,帮助您快速生成适合 Product Hunt、AppSumo 等平台的在线推广材料,涵盖视觉创意、推广标语、品牌语调和卖点传达
办公文档助手
一个专为公司内部运营设计的 AI 虚拟行政助理。帮助您快速创建高质量的内部文档,如公告、会议记录、摘要、表格、流程和人力资源记录。
Discourse 社区管理员
Discourse 社区管理员助手帮助您快速生成清晰、友好且结构良好的用户回复,使社区管理变得更轻松和专业。

Frequently Asked Questions

Bika.ai是免费使用的吗?
是什么让 Bika.ai 如此独特?
一句话快速介绍:什么是Bika.ai?
"BIKA" 这个缩写单词代表什么意思?
Bika.ai是怎么做到AI自动化做事的?
Bika.ai与Kimi、ChatGPT等AI助手有什么区别?
Bika.ai与多维表格有什么区别?
Bika.ai在单表数据量、关联引用变多后,如几万行、几十万行,会卡吗?
Bika.ai中的"空间站"是什么?
付款后我拥有多少个付费空间?
什么是"资源"?
Bika.ai的团队是怎样”吃自己的狗粮“(应用自己的产品)的?
Bika.ai如何帮助提高工作效率?
Bika.ai 的AI自动化功能有哪些特点?
Bika.ai 中的自动化模板是什么?
Bika.ai 是否支持团队协作及权限功能?
Bika.ai是否只适合个人使用?企业团队会不适合?

Embark on Your AI Automation

MCPunk | Bika.ai