Agent Skills Hub

codebase-analyzer

Automates the creation and maintenance of CLAUDE.md files. It monitors codebase evolution and keeps project memory in sync with file changes, structure, and build commands.

massive-context-mcp

Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.

session-reflection-analysis

Analyze Claude Code session history to identify inefficiencies, optimize token usage, and suggest workflow improvements.

native-app-performance

Native macOS/iOS app performance profiling via xctrace and CLI-based hotspot analysis without opening the Instruments UI.

flaky-detect

Identify, categorize, and troubleshoot flaky tests by analyzing CI history, execution patterns, and code structure to improve test suite reliability.

cross-plugin-detective

Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.

simplemem-skill

Persistent, semantic long-term memory for AI agents. Save, query, and retrieve cross-session dialogues, decisions, and multimodal context using semantic compression.

kill-claude-mem

Kills stale claude-mem worker and MCP server processes to recover RAM and improve performance in memory-constrained environments like GitHub Codespaces.

memcontext-autopilot

An automated memory middleware for AI agents, implementing a Retrieve-Respond-Save loop to maintain long-term persistent context across conversations.

swift-review

Expert Swift code review for macOS/iOS. Detects memory leaks, threading bugs, concurrency issues, and accessibility gaps using parallel analysis agents.

entry-point-analyzer

Map the attack surface of smart contract codebases by identifying and categorizing state-changing entry points.

new-analyzer

Guide for implementing a new AI coding agent analyzer in Splitrail to track token usage, costs, and performance metrics.