.claude/skills/widehiveRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
WideHive — Wide Research orchestration for AutoClaw / OpenClaw agents: fan out 100+ context-isolated sub-agents over a large target list, merge programmatically with zero LLM calls, synthesize scenario-shaped reports (financial tables / academic reviews / tech matrices). Triggers: widehive, wide research, batch research, 批量调研, 大范围调研.
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These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/widehiveRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/widehiveRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/widehiveRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
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sha256:18bf242547aca429f499409bc0d1ef841e041055f721dc27aa16917245994cfdscripts/merge_results.pyskill-card.mdSKILL.mdWideHive 1.0.0 — initial release - Launches "WideHive" skill for large-scale, context-isolated batch research across 20–100+ objects. - Implements structured fan-out orchestration using sub-agents, strict task isolation, and code-based result merging without LLM involvement in aggregation. - Provides scenario-tailored templates and reporting for financial, academic, and tech research tasks. - Includes robust error handling, retries, and progress reporting; ensures all per-object outputs are checkpointed and directly verifiable. - Requires sub-agent spawning support (e.g., AutoClaw/OpenClaw).