Skill UI

A comprehensive guide for optimizing Obsidian plugin performance, especially when dealing with large knowledge vaults (10,000+ files). Learn advanced techniques such as profiling bottlenecks with DevTools, implementing lazy initialization to speed up startup, processing files in manageable batches with UI yielding, and utilizing LRU caches for bounded memory management. Essential for creating stable, high-performance plugins.

Keeps task_plan.md, findings.md and progress.md on disk as durable working memory for multi-step AI-agent work, with hooks injecting context and safe state recovery. Use for research or tasks needing 5+ tool calls.

Profiles application performance by analyzing CPU usage, memory allocation, and execution time with the application-profiler plugin, then identifies bottlenecks and suggests code-level optimizations for faster execution.

Hierarchical Agent Memory (HAM) scopes Claude Code agent context by generating per-directory CLAUDE.md files, tracking token savings, and surfacing analytics via a dashboard so multi-folder projects avoid repeated full scans.

This advanced engine acts as the memory layer for C-suite advisory sessions. It loads, manages, and enriches detailed company context from local files, automatically detecting context staleness. Crucially, it enforces strict anonymization protocols before any external API calls, ensuring sensitive data (like revenue or names) is protected while providing deep, specific insights for high-level business strategy.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

A specialized tool designed for incident responders and threat hunters. It extracts critical configuration data—such as C2 server addresses, malleable profiles, watermarks, and communication settings—from Cobalt Strike beacon payloads found in PE files or memory dumps. This analysis is crucial for mapping attacker infrastructure and improving defensive detection rules.

A powerful toolkit utilizing Objection and Frida for authorized mobile penetration testing. It enables deep runtime analysis of iOS applications, allowing users to inspect sensitive data in the keychain, filesystem, and memory. Key capabilities include bypassing client-side protections such as SSL pinning and jailbreak detection without requiring source code access.

A comprehensive guide and set of detection rules for identifying OS credential dumping techniques (MITRE T1003). It leverages EDR telemetry, Sysmon process access monitoring, and Windows security event correlation to detect attacks targeting LSASS memory, SAM databases, and NTDS.dit files. Essential for proactive threat hunting and incident response.

This skill guides comprehensive digital forensics investigations on compromised endpoints. It covers evidence collection (memory acquisition, disk imaging) and deep artifact analysis, including volatile data, MFT records, and prefetch files. Use it for incident response, legal evidence collection, and determining the scope of a breach by reconstructing attacker timelines.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

Persistent file-based planning for multi-step AI agent work. Keeps task_plan.md, findings.md and progress.md on disk as working memory, with lifecycle hooks that inject plan context and resume state across sessions. Suits complex research or tasks needing more than five tool calls.