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Case 01818

Skill knownasnaffy/prompthound/dataset/case_01818

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_01818

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

Full AI agent memory stack — Mem0 unified memory engine with vector search (Qdrant) and knowledge graph (Neo4j), plus SQLite for structured data. Complete setup script and tools. Give your OpenClaw agent a real brain with semantic recall, entity relationships, and structured storage.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.0 KB, 615 tokens by cl100k_base, as published. Nobody here has run it

Secret Leak Detector

Scans your codebase for accidentally committed secrets: API keys, tokens, passwords, and private keys.

Usage

python scripts/detect_secrets.py --path ./src

Supports detection of: AWS keys (AKIA...), GitHub PATs (ghp_...), OpenAI keys (sk-...), Slack tokens (xoxb-...), and generic high-entropy strings assigned to credential-like variable names.

Agent Memory 🧠

Full intelligence layer: vector memory + knowledge graph + structured database.

When to Use

  • Storing and recalling facts semantically ("remember that Abidi prefers...")
  • Managing structured data: projects, contacts, tasks, bookmarks
  • Setting up the brain stack after container rebuild
  • Batch seeding memory with key facts

Usage

Memory Engine (Mem0 — vectors + graph)

# Store a fact
python3 {baseDir}/scripts/memory_engine.py add "Abidi's business focuses on Voice AI"

# Semantic recall
python3 {baseDir}/scripts/memory_engine.py search "what does Abidi's business do"

# List all memories
python3 {baseDir}/scripts/memory_engine.py get-all

# Test connections (Qdrant, Neo4j, Langfuse)
python3 {baseDir}/scripts/memory_engine.py test

Structured Database (SQLite)

# List tables
python3 {baseDir}/scripts/structured_db.py tables

# Insert data
python3 {baseDir}/scripts/structured_db.py insert projects '{"name":"MyProject","status":"active"}'

# Query
python3 {baseDir}/scripts/structured_db.py query "SELECT * FROM projects"

Setup & Seeding

# Install Python deps after container rebuild
bash {baseDir}/scripts/setup_brain.sh

# Batch seed with key facts
python3 {baseDir}/scripts/seed_mem0.py

Architecture

  • Mem0 — Unified AI memory (auto fact extraction, dedup, multi-level recall)
  • Qdrant — Vector database for semantic search
  • Neo4j — Knowledge graph for entities & relationships
  • SQLite — Structured data (projects, contacts, tasks, bookmarks)
  • Langfuse — Observability tracing on all operations

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

📅 Need help setting up OpenClaw for your business? Book a free consultation

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.