Agent skill · neolabhq
do-in-parallel
Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.
What it needs
About 12k tokens when loaded.
What this skill does
do-in-parallel <task> Launch multiple sub-agents in parallel to execute tasks across different files or targets. Analyze the task to select the right-sized model tier per target, perform requirement grouping analysis (repeatable, shared, or independent), generate quality-focused prompts with Zero-shot Chain-of-Thought reasoning and mandatory self-critique, then dispatch meta-judges based on grouping (one per group or per independent task, all in parallel), followed by implementors for each task in parallel, with LLM-as-a-judge verification using grouping-appropriate evaluation specs after each completes. </task> <context> This command implements the Supervisor/Orchestrator pattern with parallel dispatch, requirement grouping, and meta-judge → LLM-as-a-judge verification. The primary benefit is parallel execution - multiple independent tasks run concurrently rather than sequentially, dramatically reducing total execution time for batch operations. Requirement grouping analysis reduces total agents by sharing meta-judges and judges across related tasks: repeatable groups (same task across targets) share one meta-judge spec, shared groups (interdependent tasks) use one combined judge. Key benefits: Parallel execution - Multiple tasks run simultaneously Requirement grouping - Reduces meta-judges and judges by identifying repeatable and shared task patterns Right-sized model - Chosen per target by the Model Selection Policy: sonnet/haiku by default, opus only when earned Fresh context - Each sub-agent works with clean context window Task-specific evaluation - Each meta-judge produces tailored rubrics and checklists for its specific task or group External verification - Judge applies target-specific meta-judge specification mechanically — catches blind spots self-critique misses Feedback loop - Retry with specific issues identified by judge Quality gate - Work doesn't ship until it meets threshold Common use cases: Apply the same refactoring across multiple files Run code …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills neolabhq/do-in-parallel--d110a8