whatisgithub

What is rapid-expert?

luyou666/rapid-expert — explained in plain English

Analysis updated 2026-05-18

50PythonAudience · pm founderComplexity · 3/5LicenseSetup · moderate

In one sentence

An interactive command-line agent that guides someone with no background in a field, like investing or job hunting, through a 5 to 12 day structured learning plan and produces real research deliverables.

Mindmap

mindmap
  root((Rapid Expert))
    What it does
      Structured learning plan
      5 to 12 days
      Real deliverables
    Tech stack
      Python
      CLI
      MCP
      HTTP API
    Use cases
      Investment research
      Job hunting
      Startup validation
    Audience
      PMs and founders
      Career changers

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Generate a customized day-by-day learning plan for a new domain like investment research or job hunting.

USE CASE 2

Have the agent scan the web and GitHub for current sources and rank them by relevance.

USE CASE 3

Produce a real deliverable such as an industry analysis report or competitor comparison.

USE CASE 4

Call the agent programmatically through its HTTP or MCP interface from another platform.

What is it built with?

PythonCLIMCPHTTP API

How does it compare?

luyou666/rapid-experthjcheng0602/paperwisekulunkilabs/vibenetbackup
Stars505050
LanguagePythonPythonPython
Setup difficultymoderatemoderatemoderate
Complexity3/53/53/5
Audiencepm founderresearcherops devops

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires configuring an external AI provider API key on first launch.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

So what is it?

Rapid Expert (Chinese: Ji Su Zhuan Jia) is an interactive command-line agent designed to help someone with no background in a field reach practical competence in 5 to 12 days. The primary use cases described are investment research, startup validation, job hunting, consulting analysis, and product development. Instead of delivering a static collection of documents or a general chatbot, it guides the user through a structured learning path, retrieves current materials from the web and GitHub, and produces real deliverables like industry analysis reports or competitor comparisons. The project is written in Python and runs locally by default, calling external AI model APIs when needed. On first launch it asks the user to configure which AI provider and model to use. After that, the main entry point is a command called study hacker that opens an interactive shell. From there, the agent interviews the user about their domain, current knowledge level, daily time budget, and risk boundaries, then generates a customized learning plan and starts executing it step by step. Under the hood there is a harness layer that breaks work into fixed stages: risk assessment, planning, source scanning, ranking, GitHub search, building, and evaluation. Each stage can be run in sequence, paused for human review, resumed, or approved through a permission gate before sensitive actions (like network searches) proceed. The harness also exposes an HTTP API and an MCP (JSON-RPC over stdin/stdout) interface so external agent platforms can call it programmatically. A file-based task queue allows multiple jobs to run in sequence with a worker process. A lightweight retrieval system lets the agent index sources and search them by query during a session. All runtime files including session logs, outputs, and configuration are kept local and excluded from version control. The README is written primarily in Chinese. The project carries an MIT license and includes an explicit disclaimer that it does not provide legal, investment, medical, or other professional advice. High-stakes decisions in regulated fields must be confirmed by qualified professionals.

Copy-paste prompts

Prompt 1
Set up Rapid Expert and start a study plan for evaluating a startup idea in 10 days.
Prompt 2
Explain the stages this agent's harness goes through, from risk assessment to final evaluation.
Prompt 3
Show me how to call this project's MCP interface from another agent platform.
Prompt 4
Help me configure which AI provider and model Rapid Expert uses on first launch.

Frequently asked questions

What is rapid-expert?

An interactive command-line agent that guides someone with no background in a field, like investing or job hunting, through a 5 to 12 day structured learning plan and produces real research deliverables.

What language is rapid-expert written in?

Mainly Python. The stack also includes Python, CLI, MCP.

What license does rapid-expert use?

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

How hard is rapid-expert to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is rapid-expert for?

Mainly pm founder.

Open on GitHub → Ask about another repo

This repo across BitVibe Labs

Verify against the repo before relying on details.