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Vexor Cli

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scarletkc
vexor-cli

Semantic file discovery via `vexor`. Use whenever locating where something is implemented/loaded/defined in a medium or large repo, or when the file location is unclear. Prefer this over manual browsing.

Overview

Publisherscarletkc
Repositoryvexor
Skill namevexor-cli
Stars
246
Forks
16
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by scarletkc on GitHub. Read the source before you install it.

Installation

Install the Vexor Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/scarletkc/vexor/tree/main/plugins/vexor/skills/vexor-cli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/scarletkc/vexor.git /tmp/vexor
mkdir -p .claude/skills
cp -r /tmp/vexor/plugins/vexor/skills/vexor-cli .claude/skills/vexor-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vexor Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vexor Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vexor Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vexor CLI Skill

Goal

Find files by intent (what they do), not exact text.

Use It Like This

Command

bash
vexor "<QUERY>" [--path <ROOT>] [--mode <MODE>] [--ext .py,.md] [--exclude-pattern <PATTERN>] [--top 5] [--content] [--format rich|porcelain|porcelain-z|json]

Common Flags

  • --path/-p: root directory (default: current dir)
  • --mode/-m: indexing/search strategy
  • --ext/-e: limit file extensions (e.g., .py,.md)
  • --exclude-pattern: exclude paths by gitignore-style pattern (repeatable; .js → **/*.js)
  • --top/-k: number of results
  • --include-hidden: include dotfiles
  • --no-respect-gitignore: include ignored files
  • .vexorignore project rules always apply, even with --no-respect-gitignore.
  • --no-recursive: only the top directory
  • --format: rich (default), porcelain/porcelain-z for scripts, json for full output with chunk content
  • --content: print each match's source text below the table — usually removes the need to read the files afterwards
  • --no-cache: in-memory only, do not read/write index cache
  • vexor index --local: create and use project-local .vexor/ cache storage

Project Config

  • The nearest .vexor/config.json applies automatically for the resolved search or index path.
  • It accepts only rerank, auto_index, model, embedding_dimensions, batch_size, embed_concurrency, and extract_concurrency.
  • batch_size must be at least 0; both concurrency values must be at least 1.
  • Credentials and endpoints (api_key, base_url, remote_rerank) and all other fields are rejected.
  • Precedence is global config, project config, environment overrides, then explicit arguments.
  • vexor config --show labels each field's origin and vexor doctor lists active overrides; mutating vexor config commands remain global-only.

Modes (pick the cheapest that works)

  • auto: routes by file type (default)
  • name: filename-only (fastest)
  • head: first lines only (fast)
  • brief: keyword summary (good for PRDs)
  • code: code-aware chunking for .py/.js/.ts (best default for codebases)
  • outline: Markdown headings/sections (best for docs)
  • full: chunk full file contents (slowest, highest recall)

Troubleshooting

  • Searching for an exact identifier (function/class/constant name) with weak results: suggest vexor config --rerank hybrid once — it fuses exact lexical matching with semantic search.
  • Need ignored or hidden files: add --include-hidden and/or --no-respect-gitignore.
  • Scriptable output: use --format porcelain (TSV) or --format porcelain-z (NUL-delimited).
  • Get detailed help: vexor search --help.
  • Config issues: vexor doctor or vexor config --show reports effective values and their origins.

Examples

bash
# Find CLI entrypoints / commands
vexor search "typer app commands" --top 5
bash
# Search docs by headings/sections
vexor search "user authentication flow" --path docs --mode outline --ext .md --format porcelain
bash
# Locate config loading/validation logic
vexor search "config loader" --path . --mode code --ext .py
bash
# Exclude tests and JavaScript files
vexor search "config loader" --path . --exclude-pattern tests/** --exclude-pattern .js
bash
# Read the matching code directly, without a follow-up file read
vexor search "where JWT claims are validated" --path . --mode code --content

Tips

  • First time search will index files (may take a minute). Long-lived MCP or Python client sessions reuse mapped vectors, monitor source changes, and skip some snapshot scans. Watcher setup failures fall back to scanning. Separate CLI invocations still validate the filesystem. Use longer timeouts if needed.
  • Results return similarity ranking, exact file location, line numbers, and matching snippet preview.
  • Add --content (or --format json) to get the matching source text in the same call, and skip reading those files separately. The text sits at content_start_line..content_end_line, which can begin later than the result's start_line when a long symbol was indexed as several chunks. If a result shows stale_line_range, the file changed since indexing — re-run vexor index. Content is capped per response, so lower-ranked results may report budget_exhausted.
  • Combine --ext with --exclude-pattern to focus on a subset (exclude rules apply on top).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Vexor Cli AI skill do?

Semantic file discovery via `vexor`. Use whenever locating where something is implemented/loaded/defined in a medium or large repo, or when the file location is unclear. Prefer this over manual browsing.

Why use Vexor Cli on TypingMind?

Because you install it once and use it with any model. Vexor Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vexor Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scarletkc/vexor/tree/main/plugins/vexor/skills/vexor-cli. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Vexor Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vexor Cli?

As many as you like. As long as a model supports skills, you can use Vexor Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vexor Cli AI skill free?

Yes. It is published on GitHub by scarletkc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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