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Found 84 Skills
Control interactive terminal sessions via tmux. Use when tasks need persistent REPLs, parallel CLI agents, or any process requiring a TTY that simple shell execution cannot handle.
Automated CLI-based parallel agent execution — spawn subagents via Gemini CLI, coordinate through MCP Memory, monitor progress, and run verification
Comprehensive React/TypeScript frontend code review with optional parallel agents
Parallel DAG-plan implementation skill. Reads a v-planning plan directory (root.md + step-<n>.md files), topologically schedules ready steps, and fans them out as parallel sub-agents. Use whenever the user invokes /v-implement, points at a plan directory produced by /v-plan, or asks to "run the parallel plan", "implement the DAG", or "fan out the steps" — even without those exact words. For linear plans (single .md file), use `implementing` instead.
Orchestrate multiple independent agents running simultaneously, leveraging Claude Sonnet 4.5's parallel tool execution capabilities to maximize throughput on multi-task requests.
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Scans the codebase against another skill's criteria using a parallel agent team. Use when the user says /scan <skill-name> to audit code quality, find violations, or assess conformance to best practices.
Track per-agent token usage and flag waste in parallel dispatch. Use after running parallel agents to evaluate cost vs value.
Invoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.
Spawn 10 independent parallel agents to analyze source material from distinct perspectives, synthesize findings, and apply improvements to a target agent or skill. Use when source material is complex and multi-angle extraction justifies 3-5x token cost over inline analysis. Use for "parallel analysis", "multi-perspective", or "deep extraction". Do NOT use for routine improvements, simple source material, or when token budget is limited.
Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection and quality-focused prompting
Agent testing methodology - run agents with test inputs, observe outputs, iterate until outputs are accurate and well-structured.