agent-orchestration
Master the art of spawning and managing sub-agents. Write prompts that actually work, track running agents, and learn from every outcome. Part of the Hal Stack 🦞
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/abeltennyson/abel-agent-orchestration📖 Complete setup guide: https://skillboss.co/skill.md
Agent Orchestration 🦞
By Hal Labs — Part of the Hal Stack
Your agents fail because your prompts suck. This skill fixes that.
The Core Problem
You're not prompting. You're praying.
Most prompts are wishes tossed into the void:
❌ "Research the best vector databases and write a report"
You type something reasonable. The output is mid. You rephrase. Still mid. You add keywords. Somehow worse. You blame the model.
Here's what you don't understand: A language model is a pattern-completion engine. It generates the most statistically probable output given your input.
Vague input → generic output. Not because the model is dumb. Because generic is what's most probable when you give it nothing specific to work with.
The model honored exactly what you asked for. You just didn't realize how little you gave it.
The Core Reframe
A prompt is not a request. A prompt is a contract.
Every contract must answer four non-negotiables:
| Element | Question |
|---|---|
| Role | Who is the model role-playing as? |
| Task | What exactly must it accomplish? |
| Constraints | What rules must be followed? |
| Output | What does "done" look like? |
Miss one, the model fills the gap with assumptions. Assumptions are where hallucinations are born.
The 5-Layer Architecture
Effective prompts share a specific structure. This maps to how models actually process information.
Layer 1: Identity
Who is the model in this conversation?
Not "helpful assistant" but a specific role with specific expertise:
You are a senior product marketer who specializes in B2B SaaS positioning.
You have 15 years of experience converting technical features into emotional benefits.
You write in short sentences. You never use jargon without explaining it.
The model doesn't "become" this identity—it accesses different clusters of training data, different stylistic patterns, different reasoning approaches.
Identity matters. Miss this and you get generic output.
Layer 2: Context
What does the model need to know to do this task exceptionally well?
Context must be:
- Ordered — Most important first
- Scoped — Only what's relevant
- Labeled — What's rules vs. editable vs. historical
## Context
### Rules (never change)
- Design system: Tailwind, shadcn components
- Voice: Professional but warm, never corporate
### Current State (may evolve)
- Landing page exists at /landing
- Using Next.js 14 with App Router
### Historical (for reference)
- Originally built with Create React App, migrated Jan 2025
Without labels, the model treats everything as equally optional. Then it rewrites your core logic halfway through.
Layer 3: Task
What specific action must be taken?
Not "write something about X" but precise instructions:
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-abeltennyson-abel-agent-orchestration": {
"enabled": true,
"auto_update": true
}
}
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