bkmr

作者 sysid已验证

Knowledge Management for Humans and Agents

263
Stars
10
Forks
Rust
语言
2026/8/23
添加时间

⚠️ 第三方软件声明

本 Skill 为第三方开源软件,独立托管于 GitHub。SkillTip 仅为信息目录,不控制或维护底层仓库。所显示的安全检查为自动化且范围有限,安装前请自行审查源码。

阅读服务条款

安装

添加到你的 Claude Code skills 目录:

# Add to your Claude Code skills
git clone https://github.com/sysid/bkmr

快速入门

使用 bkmr 等 Skills 的指南。

安全报告

已验证

上次扫描:—

{
  "status": "PASSED",
  "issues": []
}

README.md

rsenv logo

Crates.io Crates.io Docs.rs Build Status

Store anything, find it by meaning, act on it instantly.

Beyond Bookmarks and Snippets: Knowledge Management for Humans and Agents

bkmr - crate of the week 482 - memories, bookmarks, snippets, text - search it, invoke it!

Organize, find, and apply various content types:

  • Web URLs with automatic metadata extraction
  • Code snippets for quick access and reuse
  • Shell commands with immediate execution capabilities
  • Markdown documents with live rendering, incl. TOC
  • Plain text with Jinja template interpolation
  • Local files and directories integration

Why bkmr?

  • Developer- and agent-focused: Integrates seamlessly with workflow and toolchain
  • Agent-friendly: JSON output, non-interactive mode, and _mem_ system tag for AI agent memory
  • Multifunctional: Handles many content types with context-aware actions
  • Intelligent: Full-text and semantic search capabilities
  • Privacy-focused: Fully local — database, embeddings, and search all run offline
  • Fast: 20x faster than similar Python tools
  • Automation-ready: Programmatic CLI with --json, --np, --stdout for pipelines and integrations
  • Editor Integration: Built-in LSP server

Agent Memory and Skill

Persistent long-term memory for AI agents. The _mem_ system tag and hsearch (hybrid FTS + semantic search) create a complete read/write memory interface:

# Agent stores memory:
bkmr add "Prod DB is PostgreSQL 15 on port 5433" fact,database \
  --title "Production database config" -t mem --no-web

# Agent queries memories with natural language (hybrid search)
bkmr hsearch "database configuration" -t _mem_ --json --np

# All output is structured JSON — designed for programmatic consumption

Use skill/bkmr-memory. It defines a comprehensive memory protocol with taxonomy, deduplication, and session workflows.

See Agent Integration.

Quick Examples

# Quick fuzzy search with interactive selection
bkmr search --fzf

# Add URL with automatic metadata extraction
bkmr add https://example.com tag1,tag2

# Store code snippet
bkmr add "SELECT * FROM users" sql,_snip_ --title "User Query"

# Shell script with interactive execution
bkmr add "#!/bin/bash\necho 'Hello'" utils,_shell_ --title "Greeting"

# Render markdown in browser with TOC
bkmr add "# Notes\n## Section 1" docs,_md_ --title "Project Notes"

# Import files with frontmatter
bkmr import-files ~/scripts/ --base-path SCRIPTS_HOME

# Local semantic search (no API keys needed)
bkmr sem-search "containerized application security"

# Agent memory: store and retrieve knowledge
bkmr add "Prod DB on port 5433" fact,database --title "Prod DB config" -t mem --no-web
bkmr hsearch "database config" -t _mem_ --json --np

Screenshots

General Usage:

bkmr demo

Fuzzy Search with FZF:

fzf demo

Agent Memory:

agent demo

Detailed walkthroughs: Overview | Getting Started | Search and Filter | Edit and Update | Tag Management

Getting Started

Installation

# Via cargo
cargo install bkmr

# Via pip/pipx/uv
pip install bkmr

# Via brew
brew install bkmr
export ORT_DYLIB_PATH=/opt/homebrew/lib/libonnxruntime.dylib

See Installation Guide for detailed instructions and troubleshooting.

Initial Setup

# Generate configuration
bkmr --generate-config > ~/.config/bkmr/config.toml

# Create database
bkmr create-db ~/.config/bkmr/bkmr.db

# Optional: Configure location
export BKMR_DB_URL=~/path/to/db

First Use

# Add your first bookmark
bkmr add https://github.com/yourusername/yourrepo github,project

# Search and find
bkmr search github

# Interactive fuzzy search
bkmr search --fzf

Quick Start Guide: See the Quick Start for a 5-minute tutorial.

Command Reference

CommandDescription
searchFull-text search with tag filtering, FZF, JSON output
hsearchHybrid search: FTS + semantic with RRF fusion
sem-searchSemantic search using local embeddings (offline, no API keys)
addAdd bookmarks (URLs, snippets, scripts, markdown, env vars)
openSmart action dispatch based on content type
editEdit bookmarks (smart: opens source file for imports)
updateModify tags and custom openers
deleteDelete bookmarks by ID
showDisplay bookmark details
import-filesImport files/directories with frontmatter parsing
tagsView tag taxonomy with usage counts
infoShow configuration, database path, embedding status
backfillGenerate missing embeddings
clear-embeddingsClear all embeddings and content hashes
lspStart LSP server for editor snippet completion
completionGenerate shell completions (bash, zsh, fish)
surpriseOpen random URL bookmarks

Complete command documentation: See Basic Usage for detailed examples.

Smart Content Actions

bkmr intelligently handles different content types with appropriate actions:

Content TypeDefault ActionSystem Tag
URLsOpen in browser(none)
SnippetsCopy to clipboard_snip_
Shell ScriptsInteractive edit + execute_shell_
MarkdownRender in browser with TOC_md_
Environment VariablesPrint for eval/source_env_
Text DocumentsCopy to clipboard_imported_
Agent MemoryDisplay to stdout_mem_

Rule: A bookmark can have at most one system tag. Local files without a system tag open with the default application.

Learn more: Content Types | Core Concepts

Documentation

Comprehensive documentation is available in the bkmr Wiki:

Getting Started

Core Features

Advanced Topics

Reference

Editor Integrations

Access your snippets directly within your editor without context switching.

Neovim Plugin (Recommended)

bkmr-nvim provides visual interface with zero configuration.

{
  "sysid/bkmr-nvim",
  dependencies = { "nvim-lua/plenary.nvim" },
  config = function()
    require("bkmr").setup() -- Zero config required!
  end,
}

Features: Visual snippet browser, in-editor editing, automatic LSP setup, custom commands

Built-in LSP Server

Compatible with VS Code, Vim, Emacs, Sublime, and any LSP-compatible editor.

# Start LSP server
bkmr lsp

# Disable template interpolation if needed
bkmr lsp --no-interpolation

Features: Automatic completion, language-aware filtering, universal snippets, template interpolation

IntelliJ Platform Plugin

bkmr-intellij-plugin for all JetBrains IDEs.

Features: Seamless LSP integration, Tab navigation, works in IntelliJ IDEA, PyCharm, WebStorm, CLion, RustRover, and all JetBrains IDEs

Complete documentation: Editor Integration

Platform Compatibility

Linux Clipboard: Uses external tools for reliable clipboard persistence.

  • Wayland: Uses wl-copy from wl-clipboard package
  • X11: Uses xclip (preferred) or xsel as fallback
  • Auto-detection: Detects display server via WAYLAND_DISPLAY environment variable

Development

Building from Source

git clone https://github.com/sysid/bkmr.git
cd bkmr
cargo build --release

Running Tests

IMPORTANT: All tests must be run single-threaded:

# Run tests (REQUIRED: single-threaded)
cargo test -- --test-threads=1

# Or use Makefile
make test

Why single-threaded? Tests share a SQLite database and environment variables. Parallel execution causes race conditions.

See Development for complete contributor guide.

Community and Contributions

We welcome contributions! Please check our Contributing Guidelines to get started.

Resources:

常见问题

What is bkmr?

bkmr is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by sysid. Knowledge Management for Humans and Agents. It has 263 GitHub stars.

Is bkmr safe to use?

Yes. bkmr passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install bkmr?

Clone the repository with "git clone https://github.com/sysid/bkmr" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is bkmr written in?

bkmr is primarily written in Rust. It is open-source under sysid on GitHub, so you can review or fork the full source.

Are there alternatives to bkmr?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh bkmr against similar tools.

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