Case 03771

Skill knownasnaffy/prompthound/dataset/case_03771

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_03771

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

AI-powered knowledge base builder that automatically captures, organizes, and retrieves information. Learns from conversations, documents, and interactions to build a personalized knowledge graph. Enables semantic search and intelligent Q&A.

SKILL.md

6.4 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it

Meta Knowledge Base

Self-building knowledge management system that learns and grows automatically.

Features

1. Auto-Capture

Conversation Learning: Extract key information from chats
Document Parsing: Extract from PDFs, docs, emails
Web Scraping: Learn from visited pages
File Watch: Monitor folders for new content

2. Knowledge Organization

Auto-Tagging: Automatic topic categorization
Entity Extraction: People, companies, concepts
Relationship Mapping: Connect related ideas
Version History: Track knowledge evolution

3. Semantic Search

Vector Embeddings: Semantic similarity search
Hybrid Search: Combine keyword + semantic
Filtering: Filter by date, tags, source
Ranking: Relevance-based results

4. Intelligent Q&A

RAG Pipeline: Retrieve + Generate answers
Context-Aware: Understand conversation context
Citing Sources: Reference original knowledge
Confidence Scoring: Show answer confidence

5. Continuous Learning

User Feedback: Learn from corrections
Implicit Learning: Learn from interactions
Knowledge Updates: Keep information fresh
Gap Identification: Find missing knowledge

Installation

pip install numpy faiss-cpu sentence-transformers

Usage

Initialize Knowledge Base

from meta_knowledge import KnowledgeBase

kb = KnowledgeBase(
    name="my_knowledge",
    embedding_model="paraphrase-multilingual-MiniLM-L12-v2"
)

Add Knowledge

# From text
kb.add(
    content="Python is a high-level programming language...",
    tags=["programming", "python"],
    metadata={"source": "user", "date": "2026-03-22"}
)

# From document
kb.add_from_file("document.pdf", tags=["research"])

# From URL
kb.add_from_url("https://example.com/article", tags=["news"])

Search

# Semantic search
results = kb.search(
    query="What is machine learning?",
    top_k=5
)

for r in results:
    print(f"{r.score:.2f} | {r.content[:100]}...")

Q&A

# Ask questions
answer = kb.ask(
    question="What do I know about AI?",
    include_sources=True
)

print(answer['answer'])
print("Sources:", answer['sources'])

Knowledge Graph

# Get entity relationships
graph = kb.get_knowledge_graph()

# Find related concepts
related = kb.find_related("Python", depth=2)

API Reference

Adding Knowledge

Method	Description
`add(content, ...)`	Add single piece of knowledge
`add_batch(contents)`	Add multiple items
`add_from_file(path)`	Parse and add file
`add_from_url(url)`	Fetch and add web content
`add_from_email(email)`	Parse email content

Searching

Method	Description
`search(query, top_k)`	Semantic search
`hybrid_search(query, ...)`	Keyword + semantic
`filter_search(query, filters)`	Search with filters
`find_similar(content)`	Find similar items

Q&A

Method	Description
`ask(question, ...)`	Get answer with RAG
`get_context(question)`	Get relevant context
`generate_summary(topic)`	Generate topic summary

Management

Method	Description
`get_knowledge_graph()`	Get entity relationships
`list_tags()`	List all tags
`export(format)`	Export knowledge
`import_(data)`	Import knowledge

Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Sources   │────▶│  Ingestion  │────▶│   Storage    │
│ - Chat      │     │ - Parser    │     │ - Vector DB  │
│ - Docs      │     │ - Embedder  │     │ - Graph DB   │
│ - Web       │     │ - Indexer   │     │ - Document   │
└─────────────┘     └─────────────┘     └─────────────┘
                                           │
                    ┌──────────────────────┘
                    ▼
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Query     │────▶│   Retrieve  │────▶│   Generate  │
│ - Search    │     │ - Vector    │     │ - LLM       │
│ - Ask       │     │ - Graph     │     │ - Cite      │
└─────────────┘     └─────────────┘     └─────────────┘

Embedding Models

Model	Dimensions	Languages	Use Case
paraphrase-multilingual-MiniLM-L12-v2	384	50+	General
bge-small-zh-v1.5	512	Chinese	Chinese
text-embedding-ada-002	1536	EN	Production

Use Cases

Personal Assistant: Remember everything
Team Wiki: Shared knowledge base
Customer Support: Q&A automation
Research: Paper search & summarization
Codebase: Documentation search

Best Practices

Regular Updates: Keep knowledge fresh
Quality over Quantity: Clean data matters
Use Tags: Organize for better retrieval
User Feedback: Improve with corrections
Backup: Export regularly

Integration

With OpenClaw

# Auto-capture from conversations
@hookimpl
def after_message(message, response):
    kb.add(
        content=f"User asked about: {extract_topics(message)}",
        tags=["conversation", extract_topics(message)]
    )

With Skills

# Use knowledge in skills
def my_skill(query):
    context = kb.search(query, top_k=3)
    return generate_response(query, context)

Future Capabilities

Multi-modal knowledge (images, audio)
Real-time sync across devices
Collaborative knowledge base
Automatic knowledge validation