Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Build an agent-powered organization by mapping functions to skills and iterating on structure.
Build an agent-powered organization by mapping functions to skills and iterating on structure.
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.
Activate on: "automate my company", "agents for my business", "replace team with AI", "company structure with agents", "which skills do I need". Different from: startup (methodology) and business (strategy). This is about building the organization itself.
Discovery β What does the company do? What functions exist? Mapping β Each function β existing skill, custom skill, or hybrid Sequence β Quick wins first, customer-facing later Iteration β Run β review β adjust β expand
Ask before recommending anything: What's the core value you deliver to customers? What functions exist today? (sales, support, ops, finance, marketing, legal, HR) Where does work pile up? What's the bottleneck? What do you actually want to do yourself?
For each function, determine approach: ApproachWhenInstall existing skillCommon function (email, CRM, support)Create custom skillUnique to this businessHybridExisting skill + company-specific rulesHuman + agent assistNeeds judgment, agent handles prep See functions.md for common mappings.
Build in order of impact, not org chart: Internal ops β Low risk, clear inputs/outputs Support β After internal proves reliable Sales β After support is stable Strategy β Agents assist, humans decide See patterns.md for organizational structures.
After each function is delegated: Run 1-2 weeks with human oversight Review: what did the agent miss? Adjust scope based on errors Reduce oversight only when stable Never hand off completely on day one. See iteration.md for tracking template.
As the company evolves, capture: Decisions made and why Adjustments to agent scope What worked vs what failed New skills needed This becomes the company's operational memory.
Stop and reassess: Automating trust-building β agents assist, humans close Delegating legal/compliance decisions β agents draft, lawyers approve No clear function boundaries β define before automating Expecting 100% automation immediately β set realistic timeline
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.