Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Spawn multiple sub‑agents to perform concurrent research on a list of topics, inspired by Kimi.com’s OK Computer and Agent Swarm features【453334500861599†L40...
Spawn multiple sub‑agents to perform concurrent research on a list of topics, inspired by Kimi.com’s OK Computer and Agent Swarm features【453334500861599†L40...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
This skill lets OpenClaw emulate the “100 sub‑agents” style of Kimi’s Agent Swarm【453334500861599†L40-L99】. When you need to research several topics at once, the skill spins up lightweight sub‑agents that fetch the top web results via DuckDuckGo. By running these tasks in parallel, the skill reduces overall waiting time and surfaces a diverse set of sources.
Run concurrent searches for multiple topics. Inputs query (string, repeated): One or more search phrases. You can provide multiple query flags to search many topics at once. At least one query is required. Example python scripts/swarm_search.py --query "Agent Swarm" --query "OpenClaw skills" Output The script prints a JSON array where each element corresponds to a search query. Each element contains the original query and an array of result objects (title and URL). The format is easy for downstream agents to parse and can be further processed or summarised.
Use ok-computer-swarm whenever you need to gather high‑level information on multiple topics concurrently. It is ideal for: Broad research tasks that involve several different subjects. Generating a starting point for more in‑depth analysis. Situations where time is critical and sequential research would be too slow.
The skill uses DuckDuckGo’s free API; results may be less comprehensive than paid search APIs. It performs minimal summarisation. Consider integrating additional summarisation or reading tools if you need deeper insights.
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.