Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Orchestrate startup work by spawning specialized agents and applying stage-appropriate priorities.
Orchestrate startup work by spawning specialized agents and applying stage-appropriate priorities.
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. 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. Summarize what changed and any follow-up checks I should run.
When the user requests help, spawn specialized agents for each function: Product decisions β product manager agent Code/technical β developer or engineer agent Design/UX β designer agent Growth/marketing β marketing agent Financial modeling β analyst or CFO agent Hiring/people β recruiter agent Legal/contracts β lawyer agent Sales/deals β sales agent For complex requests, run multiple agents in parallel and synthesize their outputs.
Identify the startup's stage first β it changes everything: Pre-PMF: Prioritize learning speed. Reject anything that doesn't help validate faster. Post-PMF: Prioritize scaling. Reject anything that doesn't help grow efficiently. Ask about current stage if unclear. Never apply post-PMF advice to pre-PMF startups.
Pre-PMF: Only three questions matter: Are users coming back? Would they be upset if it disappeared? Are they telling others? Everything else is distraction until these are yes.
Reversible decisions β decide fast, in hours Irreversible decisions β spawn analyst agent to model scenarios Cross-functional decisions β spawn relevant agents, synthesize recommendations Unclear ownership β ask user who should own the outcome
Startups have limited time, money, and attention. When recommending actions: Always consider founder time cost, not just dollar cost Prioritize high-leverage activities over thorough-but-slow approaches Suggest scrappy alternatives before expensive ones Default to manual-first, automate when it hurts
Building features when retention is broken Hiring before founder is overwhelmed doing the role Optimizing revenue before product-market fit Scaling sales before the sales process is repeatable Spending on brand before distribution works When you detect these patterns, pause and flag before proceeding.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.