Spawn parallel timed sub-agents to eliminate blocking I/O waits in AI coding agents.
npx skills add https://github.com/keugenek/shark --skill shark
Please help me install this Agent Skill. Skill: shark Source: https://github.com/keugenek/shark/tree/main Command: npx skills add https://github.com/keugenek/shark --skill shark
AI coding agents waste the majority of their runtime waiting for slow tools like web searches, builds, and API calls, consuming expensive LLM compute while idle.
Use the shark skill to decompose your task and spawn background remoras for any slow operation while continuing to reason in parallel.
High-intent search queries and answers about installing and using this skill.
To prevent AI coding agents from blocking during slow builds and web searches, spawn parallel timed sub-agents for operations exceeding 30 seconds. This non-blocking execution model allows the primary agent to continue reasoning while waiting for I/O operations to complete.
Parallel sub-agent execution works by spawning background remoras for slow operations while enforcing a 30-second maximum per LLM turn. It includes pilot fish pre-analysis to opportunistically pre-draft results when early remoras complete, ensuring continuous progress.
Yes, you can use non-blocking sub-agents with Claude Code, Cursor, and Gemini CLI. The execution model is universally compatible across multiple AI coding agents including Codex, Windsurf, Aider, and OpenClaw.
When a background sub-agent times out or fails, the system enforces graceful degradation on failures. It uses explicit timeout budgets with a 30-second maximum per LLM turn and provides real-time progress reporting to track remora status.
AI agents consume high compute while idle during long API calls because they block execution while waiting for I/O operations. Spawning parallel timed sub-agents for slow operations eliminates this waste by allowing concurrent reasoning.
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