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kb-framework

作者 RaSt · GitHub ↗ · v1.2.0 · MIT-0
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在 OpenClaw 中安装
/install knowledge-base-framework
功能描述
Erstellt eine hybride Knowledge Base mit automatischer Markdown-, PDF- und OCR-Indexierung, SQLite- und ChromaDB-Integration plus tägliche Datenqualitätsprüf...
使用说明 (SKILL.md)

KB Framework - OpenClaw Skill

Version: 1.2.0
Category: Knowledge Base / Search
Requires: Python 3.9+, SQLite, ChromaDB
Location: ~/.openclaw/kb/


What is the KB Framework?

A complete Knowledge Base with:

  • Hybrid Search (semantic + keyword)
  • Automatic Indexing (Markdown, PDF, OCR)
  • SQLite + ChromaDB Integration
  • Daily Audits for data quality
  • LLM Integration (Ollama/Gemma4, HuggingFace Transformers) for essence generation, reports, file watching, and scheduled jobs
  • EngineRegistry – Central singleton for multi-engine support
  • Generator Parallel Support – primary_first, aggregate, compare

Installation (1 Minute)

1. Install the Skill

# Clone or extract into your OpenClaw workspace
git clone https://github.com/Minenclown/kb-framework.git ~/.openclaw/kb

# Or manually:
cp -r kb-framework ~/.openclaw/kb

2. Install Dependencies

cd ~/.openclaw/kb
pip install -r requirements.txt

# Optional: HuggingFace Transformers support
pip install -r requirements-transformers.txt

3. Initialize Database

python3 ~/.openclaw/kb/kb/indexer.py --init

4. Add CLI Alias

# Add to .bashrc for global access:
alias kb="bash ~/.openclaw/kb/kb.sh"
source ~/.bashrc

Configuration

Configuration is managed via kb/base/config.py:

from kb.base.config import KBConfig

# Get singleton instance
config = KBConfig.get_instance()

# Key properties:
config.base_path        # ~/.openclaw/kb
config.db_path          # ~/.openclaw/kb/knowledge.db
config.library_path     # ~/.openclaw/kb/library
config.chroma_path      # ~/.openclaw/kb/chroma_db

# Environment variable override:
# KB_BASE_PATH=/custom/path

LLM Configuration (kb/biblio/config.py)

from kb.biblio.config import LLMConfig

config = LLMConfig.get_instance()
print(f"Source: {config.model_source}")      # auto, ollama, huggingface, compare
print(f"Model: {config.model}")              # Full model name
print(f"HF Model: {config.hf_model_name}")   # google/gemma-2-2b-it

Environment Variables

Variable Default Description
KB_BASE_PATH ~/.openclaw/kb Base installation path
KB_LLM_MODEL_SOURCE auto Engine: ollama/huggingface/auto/compare
KB_LLM_OLLAMA_MODEL gemma4:e2b Ollama model name
KB_LLM_HF_MODEL google/gemma-2-2b-it HuggingFace model
KB_LLM_PARALLEL_MODE false Enable parallel generation
KB_LLM_PARALLEL_STRATEGY primary_first primary_first/aggregate/compare

Usage

Python API

import sys
sys.path.insert(0, "~/.openclaw/kb")

# Core Indexer
from kb.indexer import BiblioIndexer

with BiblioIndexer("~/.openclaw/kb/knowledge.db") as idx:
    idx.index_file("/path/to/file.md")

# Hybrid Search
from kb.framework.hybrid_search import HybridSearch
hs = HybridSearch()
results = hs.search("Your search term", limit=10)

# LLM Engine API
from kb.biblio.config import LLMConfig
from kb.biblio.engine.registry import EngineRegistry
from kb.biblio.engine.factory import create_engine

config = LLMConfig.get_instance()
print(f"Source: {config.model_source}")

# Create engine (auto mode mit HF primary + Ollama fallback)
engine = create_engine(config)

# Registry für Multi-Engine Zugriff
registry = EngineRegistry.get_instance(config)
primary, secondary = registry.get_both()

# Generator Parallel Support
from kb.biblio.generator import EssenzGenerator
generator = EssenzGenerator()
result = await generator.generate_essence(
    topic="Topic",
    parallel_strategy="primary_first"  # primary_first, aggregate, compare
)

CLI (Recommended)

# Core commands:
kb index /path/to/file.md        # Index a file
kb search "machine learning"     # Search knowledge base
kb sync                          # Sync ChromaDB with SQLite
kb audit                         # Run full audit
kb ghost                         # Find orphaned entries
kb warmup                        # Preload ChromaDB model

# LLM commands:
kb llm status                    # LLM system status
kb llm generate essence "topic"  # Generate an essence
kb llm generate report daily     # Generate a daily report
kb llm watch start               # Start file watcher
kb llm scheduler list            # List scheduled jobs
kb llm config show               # Show LLM config

# LLM Engine management:
kb llm engine status             # Show all engine status
kb llm engine switch huggingface # Switch to HuggingFace
kb llm engine test               # Test both engines

Legacy Scripts (kb/scripts/)

# Index PDFs with OCR
python3 ~/.openclaw/kb/kb/scripts/index_pdfs.py /path/to/pdfs/

# Ghost Scanner (finds orphaned DB entries)
python3 ~/.openclaw/kb/kb/scripts/kb_ghost_scanner.py

# Full Audit
python3 ~/.openclaw/kb/kb/scripts/kb_full_audit.py

# ChromaDB Warmup (at boot)
python3 ~/.openclaw/kb/kb/scripts/kb_warmup.py

Architecture

~/.openclaw/kb/
├── SKILL.md                    # This file
├── README.md                   # Detailed documentation
├── CHANGELOG.md               # Version history
├── kb.sh                       # CLI wrapper
├── knowledge.db               # SQLite metadata database
├── chroma_db/                  # ChromaDB vector database
├── library/                    # Content library
│   ├── content/               # Raw files (PDFs, studies)
│   │   ├── Gesundheit/
│   │   └── Medizin_Studien/
│   └── agent/                 # Markdown files (agent docs)
│       ├── memory/
│       └── projektplanung/
└── kb/                        # Python package
    ├── __main__.py            # CLI entry point: python -m kb
    ├── indexer.py             # Core Indexer (BiblioIndexer)
    ├── config.py              # KB config facade
    ├── update.py              # Auto-updater
    │
    ├── base/                  # Core components
    │   ├── __init__.py
    │   ├── config.py          # KBConfig singleton
    │   ├── db.py              # KBConnection
    │   ├── logger.py          # KBLogger
    │   └── command.py         # Base command class
    │
    ├── commands/              # CLI commands
    │   ├── __init__.py
    │   ├── audit.py           # kb audit
    │   ├── backup.py          # kb backup
    │   ├── engine.py          # kb llm engine
    │   ├── ghost.py           # kb ghost
    │   ├── llm.py             # kb llm (status, generate, watch, scheduler)
    │   ├── search.py          # kb search
    │   ├── sync.py            # kb sync
    │   └── warmup.py          # kb warmup
    │
    ├── biblio/                # LLM Integration
    │   ├── config.py          # LLMConfig singleton
    │   ├── engine/
    │   │   ├── registry.py    # EngineRegistry singleton
    │   │   ├── factory.py     # EngineFactory (Protocol)
    │   │   ├── base.py        # BaseLLMEngine interface
    │   │   ├── ollama_engine.py
    │   │   └── transformers_engine.py
    │   ├── generator/
    │   │   ├── essence_generator.py
    │   │   └── report_generator.py
    │   ├── scheduler/
    │   │   └── task_scheduler.py
    │   └── templates/
    │       ├── essence_template.md
    │       └── report_template.md
    │
    ├── framework/             # Search & embeddings
    │   ├── __init__.py
    │   ├── hybrid_search/     # Hybrid search implementation
    │   ├── providers/         # Search providers
    │   ├── chroma_integration.py
    │   ├── chroma_plugin.py   # Collection management
    │   ├── embedding_pipeline.py
    │   ├── reranker.py
    │   ├── chunker.py
    │   ├── fts5_setup.py
    │   ├── synonyms.py
    │   └── batching.py
    │
    ├── library/               # Library management
    │   └── knowledge_base/
    │
    ├── obsidian/              # Obsidian vault integration
    │   ├── vault.py
    │   ├── parser.py
    │   └── resolver.py
    │
    ├── scripts/               # Standalone scripts
    │   ├── index_pdfs.py
    │   ├── kb_full_audit.py
    │   ├── kb_ghost_scanner.py
    │   ├── kb_warmup.py
    │   ├── sync_chroma.py
    │   └── migrate_fts5.py
    │
    └── llm/                   # (legacy, prefer biblio)

Database Schema

files Table

Field Type Description
id TEXT UUID
file_path TEXT Absolute path
file_name TEXT Filename
file_category TEXT Category
file_type TEXT pdf/md/txt
file_size INTEGER Bytes
line_count INTEGER Lines
file_hash TEXT SHA256
last_indexed TIMESTAMP Last indexing
index_status TEXT indexed/pending/failed
source_path TEXT Original path
indexed_path TEXT MD extract path
is_indexed INTEGER 0/1

file_sections Table

Field Type Description
id TEXT UUID
file_id TEXT FK → files
section_header TEXT Heading
section_level INTEGER 1-6
content_preview TEXT First 500 characters
content_full TEXT Full content
keywords TEXT JSON Array
importance_score REAL 0.0-1.0

keywords Table

Field Type Description
id INTEGER AUTOINCREMENT
keyword TEXT Word
weight REAL Frequency

Library Structure

library/content/ - Raw Files

All non-Markdown files:

library/content/
├── Gesundheit/           # PDFs, Studies
├── Medizin_Studien/      # Medical Literature
├── Bücher/              # Books, Guides
├── Sonstiges/           # Uncategorized
└── [category]/          # Custom categories possible

library/agent/ - Markdown Files

All .md files for agents:

library/agent/
├── projektplanung/       # Agent plans
├── memory/               # Daily logs
├── Workflow_Referenzen/  # Reusable workflows
├── agents/              # Agent-specific docs
└── [category]/         # Custom categories possible

Integrating New Files

Rule: library/[content|agent]/[category]/[topic]/[file]

Examples:

# New health PDF
library/content/Gesundheit/2026/Chelat-Therapie.pdf

# New agent plan
library/agent/projektplanung/Treechat_Upgrade.md

# New learning
library/agent/learnings/2026-04-12_Git_Workflow.md

Workflows

Basic Search Workflow

# 1. Index content
kb index ./library/content/Gesundheit/

# 2. Search
kb search "Vitamin D Mangel"

# 3. Verify with audit
kb audit

LLM Essence Generation

# 1. Check status
kb llm engine status

# 2. Generate essence
kb llm generate essence "Vitamin D"

# 3. Or via Python API
python3 -c "
from kb.biblio.engine.registry import EngineRegistry
registry = EngineRegistry.get_instance()
print(registry.primary_provider)
"

Sync & Audit Cycle

# Sync ChromaDB with SQLite
kb sync --stats

# Find orphaned entries
kb ghost

# Full integrity audit
kb audit -v --csv audit_results.csv

API Reference

KBConfig (kb/base/config.py)

from kb.base.config import KBConfig

config = KBConfig.get_instance()

# Properties
config.base_path        # Path: ~/.openclaw/kb
config.db_path          # Path: ~/.openclaw/kb/knowledge.db
config.library_path     # Path: ~/.openclaw/kb/library
config.chroma_path      # Path: ~/.openclaw/kb/chroma_db

# Methods
config.validate()        # Validate paths exist
config.reload()         # Force reload from env
KBConfig.reset()        # Reset singleton (for tests)

LLMConfig (kb/biblio/config.py)

from kb.biblio.config import LLMConfig

config = LLMConfig.get_instance()

# Properties
config.model_source     # str: ollama, huggingface, auto, compare
config.model            # str: Full model identifier
config.hf_model_name    # str: HuggingFace model name
config.ollama_model      # str: Ollama model name
config.parallel_mode    # bool
config.parallel_strategy # str: primary_first, aggregate, compare

# Methods
config.reload(model_source=...)  # Reload with new config
config.to_dict()                # Serialize to dict

EngineRegistry (kb/biblio/engine/registry.py)

from kb.biblio.engine.registry import EngineRegistry

registry = EngineRegistry.get_instance()

# Properties
registry.primary_provider   # str: Current primary engine
registry.secondary_provider # str: Current secondary engine

# Methods
registry.get_primary()           # Get primary engine instance
registry.get_secondary()         # Get secondary engine instance
registry.get_both()              # (primary, secondary)
registry.is_engine_available(src)  # Check availability
registry.reset()                 # Reset singleton

HybridSearch (kb/framework/hybrid_search/)

from kb.framework import HybridSearch

search = HybridSearch()

# Search returns context pointers with line numbers
results = search.search("query", limit=10)

for r in results:
    print(f"{r.file_path}:{r.line_number} [{r.score}]")
    print(f"  → {r.content_preview[:80]}...")

ObsidianVault (kb/obsidian/vault.py)

from kb.obsidian import ObsidianVault

vault = ObsidianVault("/path/to/vault")
vault.index()

# Find backlinks
backlinks = vault.find_backlinks("Notes/Meeting.md")

# Search vault
results = vault.search("Project X")

# Full-text search
results = vault.search("keyword")

Troubleshooting

"ChromaDB slow on first start"

python3 ~/.openclaw/kb/kb/scripts/kb_warmup.py
# or
kb warmup

"Search finds nothing"

# Run audit
kb audit -v

# Ghost Scanner (find orphaned entries)
kb ghost

# Check sync status
kb sync --stats

"OCR too slow"

# Enable GPU in index_pdfs.py:
GPU_ENABLED = True  # Default: False

"LLM engine not responding"

# Check engine status
kb llm engine status

# Test both engines
kb llm engine test

# Switch engine if needed
kb llm engine switch ollama

"Database locked"

# Check for running processes
ps aux | grep kb

# Restart if needed
pkill -f "kb.*"

"Config not found"

# Set environment variable
export KB_BASE_PATH=~/.openclaw/kb

# Or programmatically
from kb.base.config import KBConfig
config = KBConfig.reload(base_path="/path/to/kb")

Common Issues

Issue Cause Solution
ImportError: kb.base.config not found Wrong base_path Set KB_BASE_PATH=~/.openclaw/kb
ChromaDB timeout Model not warmed up Run kb warmup first
No search results Empty index or sync needed kb sync then kb audit
Ghost entries found Files moved/deleted kb sync --delete-orphans
LLM timeout Model loading slow Use kb llm engine test to verify
Engine switch failed Model not available Check kb llm engine status

Module Hierarchy

# Core config & database
from kb.base.config import KBConfig
from kb.base.db import KBConnection
from kb.base.logger import KBLogger

# Search framework
from kb.framework import HybridSearch, ChromaIntegration

# Obsidian integration
from kb.obsidian import ObsidianVault
from kb.obsidian.parser import extract_wikilinks, extract_tags

# LLM integration
from kb.biblio.config import LLMConfig
from kb.biblio.engine.registry import EngineRegistry
from kb.biblio.engine.factory import create_engine

License

MIT License - free to use.

安全使用建议
What to consider before installing: - Source verification: SKILL.md tells you to git clone https://github.com/Minenclown/kb-framework.git and to use git pull for updates. The skill bundle already contains the full source; cloning/pulling from a remote repo will run network-provided code that may differ from what you inspected. Only clone/pull from a repository you trust and review commit history before running. - Install commands are risky: the instructions ask you to pip install -r requirements.txt and (as a fallback) run get-pip via curl. These steps fetch and execute third‑party packages. Install in an isolated virtualenv or sandbox and inspect requirements.txt before installing. - File-system scope: This KB purposely reads arbitrary user files for indexing and contains writer functions that can create/update/delete notes in an Obsidian vault. That behavior is expected for the feature set but means you should configure the KB to operate on a dedicated directory or backup your vault before enabling write/delete operations. Verify path validation logic in kb/obsidian/writer.py if you plan to let it modify files. - Auto-updates & remote code: The project provides an auto-updater and recommends using a symlink to a git repo as source-of-truth. If you accept auto-updates or run git pull, remote commits will change the code that runs on your machine. If you require strong assurance, disable auto-update and review updates before applying. - LLM & network usage: The documentation claims 'offline-first' yet also documents Ollama/HuggingFace engine usage and instructs git/pip operations. Confirm whether your chosen engine configuration will make external network calls (Ollama server, remote model downloads from HF) and whether that is acceptable. Local HuggingFace quantized models still download large artifacts unless you already have them cached. - What would change this assessment: If you can confirm (by inspecting files) that install.sh and update.py contain no remote download/execution code and that the writer/file-deletion code strictly enforces vault-bound path validation, the risk lowers. Conversely, if install/update scripts perform unchecked downloads or the writer has buggy path checks, the risk is higher. Practical steps: 1) Inspect install.sh and update.py for network calls before running. 2) Open requirements.txt and scan packages for unfamiliar or native‑extension installers. 3) Run the code inside a disposable VM or container and point it at test data first. 4) If you will enable write operations, backup your vault and test the writer on a copy. 5) Prefer manual updates (review commits) rather than automatic git pulls.
功能分析
Type: OpenClaw Skill Name: knowledge-base-framework Version: 1.2.0 The knowledge-base-framework skill provides a comprehensive suite for document indexing, hybrid search, and LLM integration. It is classified as suspicious due to the inclusion of high-risk capabilities that, while aligned with its stated purpose, present a significant attack surface: specifically, an auto-update mechanism (kb/update.py) that downloads and executes code from a remote GitHub repository, and an installation script (install.sh) that utilizes sudo to install system dependencies. Although the codebase includes several security-conscious features such as path and query sanitization (kb/scripts/sanitize.py) and parameterized database interactions, the combination of remote code execution primitives and elevated privilege requirements warrants caution.
能力标签
cryptorequires-oauth-tokenrequires-sensitive-credentials
能力评估
Purpose & Capability
The code and documentation match the stated purpose (hybrid KB with SQLite + ChromaDB, PDF/OCR indexing, LLM integration, Obsidian support). The presence of Obsidian writer, sync, audit, and engine management code is coherent with a KB/agent integration skill. However there are mismatches: the registry metadata claims no required env vars or binaries while SKILL.md and other docs advertise many environment variables (KB_BASE_PATH, KB_LLM_*), and INSTALL.md / SKILL.md advise cloning a remote GitHub repo even though the skill bundle already contains the full source — this inconsistency could cause confusion or lead users to pull different code from the network.
Instruction Scope
Runtime instructions direct the agent/user to run git clone / pip install / create symlinks / run indexer and install scripts. Those steps will read arbitrary user files for indexing and (via the Obsidian writer) include code paths that can create, update, move, or delete files inside a vault. The docs and SECURITY_FUNCTIONS claim path validation and 'No Network Operations', but the install/update instructions explicitly require network access (git, pip, optional curl), and LLM engines (Ollama/HuggingFace) may involve network/local server calls. The instructions also recommend running bootstrap commands (get-pip via curl) and to use a repository symlink plus git pull for updates — both are high-scope actions that change runtime code.
Install Mechanism
There is no platform install spec in the registry, but the bundle includes install scripts (install.sh, scripts/root_level/install.sh) and clear instructions to git clone and pip install requirements.txt. That means installing will fetch packages from PyPI and may execute included install scripts; using git clone/git pull allows remote code updates. The instruction to run get-pip via curl (present as a fallback in INSTALL.md) is a higher-risk pattern. While the code packaged in the skill is present, the user guidance encourages fetching/updating code from external sources — this increases attack surface.
Credentials
The registry metadata declares no required env vars/credentials, but SKILL.md documents several KB_* and KB_LLM_* environment variables for configuration (paths, engine selection). Those variables are configuration-oriented (not secrets) and make sense for a KB tool. The skill does not request cloud API keys in the metadata; however LLM engine usage (Ollama/HuggingFace) and ChromaDB sync could require additional credentials or network endpoints depending on how the user configures them. Overall the declared environment requirements in the registry are incomplete compared to what the SKILL.md documents.
Persistence & Privilege
The skill is not marked always:true and uses the normal autonomous invocation defaults (disable-model-invocation false). It includes code that writes and deletes files (Obsidian writer, ghost/cleanup functions) and an auto-updater (update.py). Those behaviors are coherent with a KB that can sync and modify vaults, but because the install instructions encourage using a git-tracked repo + git pull and include an updater, there is an elevated risk that code could be changed remotely after initial install. This combination (code that can modify user files + recommended auto-updating from a remote repo) is the principal persistence/privilege concern.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install knowledge-base-framework
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /knowledge-base-framework 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.2.0
**Major upgrade: Adds full LLM integration, engine parallelism, and refactors architecture for extensibility.** - Introduced biblio/ directory with complete LLM integration (Ollama and HuggingFace transformers, async generator support, engine registry, parallel generation strategies). - Added configuration singletons for both KB and LLM via `kb/base/config.py` and `kb/biblio/config.py`. - CLI now supports advanced LLM features: status, engine management, essence & report generation, file watching, and scheduling. - Refactored knowledge base structure for clear engine/provider separation and future extensibility. - Removed old knowledge_base directory and replaced with robust framework/ engine/ biblio/ modules. - Added installation scripts and improved configuration via environment variables and .md documentation.
v1.1.0
Version 1.1.0 changelog: - Major refactor introducing explicit modularization of core, command, and library code under `kb/base`, `kb/commands`, and `kb/library/knowledge_base/` - Added command modules for `audit`, `ghost`, `search`, `sync`, and `warmup` - Added detailed implementation files for utilities such as chunking, FTS5 setup, reranking, stopwords, and synonyms - Introduced new documentation files: `FUNCTIONS.md` and `HOW_TO_KB.md` - Removed legacy configuration and redundant documentation/scripts, including `HOW_TO_DB.md` and old root-level scripts - Updated SKILL.md architecture and usage instructions to match new modular structure and command interface
v1.0.4
- Added a new CLI via kb.sh for easier usage (`kb index`, `kb search`, `kb stats`, and more). - Introduced command aliases for common actions (index, search, stats, update, audit, ghost, warmup). - Added HOW_TO_DB.md and UPDATE_GOALS.md documentation for better developer guidance. - Added documentation and planning files in the kb/ directory (BATCH_1_REVIEW.md, BATCH_1_SUMMARY.md, etc.). - New Python entrypoints in kb/__main__.py, kb/update.py, and kb/version.py for CLI and update/version operations.
v1.0.3
- Updated dependency installation instructions to use `pip install -r requirements.txt`. - Changed configuration instructions to recommend setting the KB_DB_PATH environment variable or editing kb/config.py, instead of modifying paths directly in Python files. - No code or feature changes detected; documentation improvements only.
v1.0.2
- Added SECURITY_FUNCTIONS.txt to document or list security-related functions or features. - No other changes; core functionality and documentation remain the same. - Refactored directory and script naming from "kb_framework" to "kb-framework" for consistency. - Added new scripts: config.py, kb_ops.sh, migrate.py, sync_chroma.py, and a test file test_kb.py. - Removed legacy documentation and integration files, including installation and Obsidian-related notes. - Cleaned up unused scripts and outdated tests to streamline the codebase. - Updated dependency instructions in the documentation, removing direct sqlite3 install and improving clarity.
v1.0.1
- Refactored directory and script naming from "kb_framework" to "kb-framework" for consistency. - Added new scripts: config.py, kb_ops.sh, migrate.py, sync_chroma.py, and a test file test_kb.py. - Removed legacy documentation and integration files, including installation and Obsidian-related notes. - Cleaned up unused scripts and outdated tests to streamline the codebase. - Updated dependency instructions in the documentation, removing direct sqlite3 install and improving clarity.
v1.0.0
- Initial release of the open-source Knowledge Base Framework for OpenClaw. - Supports hybrid semantic + keyword search and automated indexing (Markdown, PDF, OCR). - Integrates with SQLite and ChromaDB for storage and search. - Includes a Python API and command-line tools for indexing, search, and audits. - Provides modular architecture and clear example workflows for integrating new files. - Daily audit scripts help maintain data quality and consistency.
元数据
Slug knowledge-base-framework
版本 1.2.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 7
常见问题

kb-framework 是什么?

Erstellt eine hybride Knowledge Base mit automatischer Markdown-, PDF- und OCR-Indexierung, SQLite- und ChromaDB-Integration plus tägliche Datenqualitätsprüf... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 172 次。

如何安装 kb-framework?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install knowledge-base-framework」即可一键安装,无需额外配置。

kb-framework 是免费的吗?

是的,kb-framework 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

kb-framework 支持哪些平台?

kb-framework 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 kb-framework?

由 RaSt(@minenclown)开发并维护,当前版本 v1.2.0。

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