netizen4-bit

Corpus Hub contributor since 2026-10-26

netizen4-bit's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an agent meets the same problem, it gets this implementation back as a worked example, with the author and the repository attached.

1corpus

1selected commit

3files

335lines

★ 2GitHub stars on repo

Totals across every corpus @netizen4-bit appears in; a commit selected for two corpora counts once. Files and lines count the selected commits; each corpus indexes the whole repository at its pinned commit.

What an agent can do with the leaddirect-mcp corpus

netizen4-bit/leaddirect-mcp: An autonomous Model Context Protocol (MCP) server for scraping B2B leads, finding distributors, and extracting direct contact info

Look up how leaddirect-mcp implements b2b, b2b-leads, b2b-marketing, with answers cited to file and line.
Reuse a worked example: the commit “add zero-cost OSINT reconnaissance, agent skills, token optimiz…” (3 files · Python).
Answer from leaddirect-mcp's real Python source instead of memory (AI, prompts and agents).

Example questions

How does leaddirect-mcp implement b2b?

Paste it into your AI agent. It clones the repo at the pinned commit and indexes it on your machine.

Search the GitHub repository netizen4-bit/leaddirect-mcp with XERJ (https://hub.xerj.org/corpus/netizen4-bit--leaddirect-mcp).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/netizen4-bit/leaddirect-mcp leaddirect-mcp && git -C leaddirect-mcp checkout 2191fa824c4c86d629e852a53148473d0e2c99e9
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./leaddirect-mcp --url http://127.0.0.1:9200 --prefix leaddirect-mcp --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

corpus netizen4-bit--leaddirect-mcp · AI, prompts and agents
Related corpora: calven-ai/marketing-as-code, wieslawgolec/plugin-custom-objects
AI search and retrieval with XERJ · Case study: reference coding for agents

Selected work

GitHub ★ 2

netizen4-bit/leaddirect-mcp

An autonomous Model Context Protocol (MCP) server for scraping B2B leads, finding distributors, and extracting direct contact info

On GitHub: 34 commits · 17 files at the pinned commit · created 2026-09 · last push 2026-10-31

Languages: Python 100%

2191fa824c feat: add zero-cost OSINT reconnaissance, agent skills, token optimiz…

selected commit: 3 files · 335 lines · Python · the project root

The corpus is the whole repository at the pinned commit, not only the files of the selected commit.

live on the hub · licence clearedcorpus netizen4-bit--leaddirect-mcpAIMITb2bb2b-leadsb2b-marketingb2b-salesgrowthlead-generation agreed 2026-10-26

[![XERJ Corpus Hub contributor](https://hub.xerj.org/contributors/netizen4-bit/badge.svg)](https://hub.xerj.org/contributors/netizen4-bit)

Included with the author's permission (email, 2026-10-26), under each repository's own licence, with attribution. Own more repositories? Sign in with GitHub and add them. Want something removed? Reply to the email, withdraw it on /add, or open an issue on xerj-org/xerj.