Skill UI

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

This skill guides the process of routing a complex goal through an AI's scored-memory loop using the `acc_act(runtime="solve")` function. It provides a structured workflow for deliberation, handling intermediate `brain_frame` results, and submitting continuous proposals. It emphasizes systematic reasoning and requires careful resolution of all generated frames to ensure a robust and verifiable solution.

This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.

A comprehensive technical reference detailing idiomatic and efficient coding practices for the C language. It covers critical topics such as safe memory management, pointer handling, robust error checking, and modern string manipulation techniques, helping developers write reliable and performant C code.

This skill generates a comprehensive capacity plan document for any service. It assists technical teams in proactively managing infrastructure by modeling traffic growth, forecasting resource utilization (CPU, memory, connections), projecting costs, and defining clear scaling strategies. Use this when assessing current resource headroom, planning for seasonal peaks, or defining a long-term infrastructure action roadmap.

A comprehensive guide for conducting memory forensics to extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps (LSASS). This technique is crucial during incident response or breach assessment to identify compromised accounts or investigate lateral movement.

This skill provides advanced detection methods for credential dumping, covering LSASS memory access, SAM hive extraction, and NTDS.dit theft (e.g., using Mimikatz). It utilizes Sysmon Event ID 10 and Windows Security logs to enable threat hunting, assisting SOC analysts in triaging EDR alerts and building robust SIEM correlation rules.

Sliver is an open-source, cross-platform Command and Control (C2) framework used for adversary emulation in authorized red-team engagements. This skill guides users through deploying the C2 server, establishing multi-protocol listeners (mTLS, DNS, HTTPS), generating cross-platform implants, and executing advanced post-exploitation techniques, including pivoting and in-memory execution via the Armory.

This skill provides a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

The `hf-mem` skill estimates the necessary VRAM or system memory required to load Safetensors or GGUF model weights for inference directly from the Hugging Face Hub. It uses efficient HTTP Range requests, avoiding local downloads. Users can determine if a model fits on their GPU or instance, including optional estimations for the KV cache and setting limits like maximum sequence length.

Context Kit is an external framework designed to organize and manage personal, durable context data (like identity details, memories, and domain knowledge) into secure Markdown artifacts. It helps users set up robust knowledge bases for coding agents (e.g., Claude Code), guiding them on best practices for data privacy, local storage, and safe integration, while preventing sensitive data leaks.