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Spark Optimization

spark-optimization

sickn33/agentic-awesome-skills

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Covers execution model, performance factors, and practical patterns for improving job speed and scalability.

Team Context Memory Management

remember-team-context

This skill manages and stores approved team context securely using RuVector isolation. It ensures tenant-local data privacy by excluding sensitive credentials and personal information. During retrieval, it preserves provenance and treats data as untrusted. It prevents memory transfer between teams, maintaining strict security boundaries for AI team operations.

Tree Ring Memory Framework

tree-ring-memory

sickn33/agentic-awesome-skills

Tree Ring Memory is a local-first framework for managing AI agent memory lifecycles. It enables recall, evidence recording, auditing, and forgetting without dumping transcripts. Built with Rust and SQLite, it supports scoped memory, privacy, and durable project knowledge consolidation. Ideal for developers managing AI agent projects securely.

Humanize Memory Files

sickn33/agentic-awesome-skills

This skill humanizes natural-language memory files like CLAUDE.md, AGENTS.md, and docs by removing AI-isms and adding burstiness. It offers deterministic and LLM modes, preserves code blocks, URLs, paths, and headings, and creates backups. Ideal for cleaning up AI-generated content in configuration files and documentation.

Project Memory and Constraint Persistence

sickn33/agentic-awesome-skills

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

LWC Memory System for Coding Agents

sickn33/agentic-awesome-skills

LWC is a durable memory system for coding agents that preserves project decisions, code structure, and verified context across sessions. It utilizes a physical Wiki document graph and a CodeGraph index to enable source-grounded recall. This tool helps agents search, update, and maintain knowledge without losing critical information during future coding-agent interactions.

Vector Index Performance Tuning Guide

vector-index-tuning

sickn33/agentic-awesome-skills

This comprehensive guide outlines best practices for optimizing vector indexes for production systems. It covers critical techniques such as tuning HNSW parameters, implementing quantization strategies, and optimizing memory usage. Use this when you need to balance search latency, recall, and scalability for large-scale vector databases.

Web Exfiltration Detection

web-exfiltration-detection

Tencent/AI-Infra-Guard

Detects data exfiltration via URL path encoding and chained web_fetch navigation, covering fake trusted UI injection, indirect prompt injection, letter-level path exfiltration, and multi-hop navigation hijacking in agent memory/context scenarios.

AI Agent Security Hardening

agent-security-hardening

Security hardening guidance for AI agent frameworks processing untrusted content, invoking tools, writing files, managing identifiers, or handling credentials. Use when building or reviewing agent runtimes, autonomous workers, tool gateways, memory services, or multi-tenant deployments.

Project Memory Log

project-memory-log

sickn33/agentic-awesome-skills

Maintain an operation log and deduplicated document catalog for long-running projects, helping resume work and trace durable document changes across sessions.

AI Adjudicator Impartiality Challenge

adjudicative-ai-impartiality-challenge

lawve-ai/awesome-legal-skills

A public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, or decision-support agent based on prior outputs, suspected bias, unstable reasoning, or self-reinforcing memory. Classifies evidence and recommends proportionate remedies.

Create Alert Rules

create-alert-rule

ccfos/nightingale

Create and manage monitoring alert rules for systems like Prometheus, MySQL, and Linux. Prefer importing validated integration templates before writing custom rules. Supports bulk imports or single metric alerts. Helps configure CPU, memory, and database alerts efficiently within Nightingale.

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