SkillOrchestra is a high-performance routing framework that intelligently directs tasks to AI agents based on explicit skill competence and cost-efficiency metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-orchestra
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-orchestra using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is SkillOrchestra?
SkillOrchestra represents a significant advancement in how multi-agent systems operate within the Openclaw Skills ecosystem. By moving away from computationally expensive Reinforcement Learning (RL) routers, it utilizes a sophisticated skill handbook and detailed agent profiles to map specific task contexts directly to the most capable and cost-effective models. This approach delivers a 700x reduction in learning costs compared to traditional methods.
The framework is designed for high-stakes environments where both accuracy and budget are critical. With the release of version 2.0.0, SkillOrchestra now includes a routing cache and pattern learning capabilities, achieving a 4x speedup in routing latency while maintaining a 95% accuracy rate. It ensures that every request managed by your Openclaw Skills setup is handled by the optimal agent, balancing high-fidelity reasoning with strict operational cost control.
SkillOrchestra Use Cases
- Dynamically routing complex software engineering tasks to high-reasoning models while delegating routine documentation to cost-efficient agents.
- Preventing routing collapse in multi-agent environments by ensuring load is distributed across diverse agent profiles.
- Implementing cost-aware AI workflows that maximize the performance-to-price ratio across various LLM providers.
- Accelerating batch processing of AI requests through parallel predictive routing and semantic caching.
How SkillOrchestra Works
- Context Inference: The system analyzes the incoming request against a Skill Handbook to identify required capabilities such as coding, reasoning, or research through pattern matching.
- Competence Mapping: It evaluates available agent profiles, calculating a real-time performance-cost ratio based on the specific skills demanded by the request context.
- Optimal Selection: The router selects the agent that maximizes the performance-to-cost ratio while cross-referencing routing history to avoid over-utilization of a single model.
- Pattern Learning and Caching: Successful routing decisions are stored in a semantic cache and analyzed by the Pattern Learner to improve future predictive routing speed and accuracy.
SkillOrchestra Setup
To integrate this framework into your Openclaw Skills environment, you must load the orchestra API and define your skill patterns.
# Load the SkillOrchestra API in your environment
. skills/skill-orchestra/skill-orchestra-api.ps1
# Route a request with caching enabled
Route-SkillOrchestra -Context "Analyze this code and suggest improvements" -UseCache
# Clear the routing cache to reset patterns
Clear-RoutingCache
SkillOrchestra Data Schema & Taxonomy
SkillOrchestra organizes its logic through structured profiles and repositories to ensure high-speed decision making within Openclaw Skills.
| Entity | Description | Key Attributes |
|---|---|---|
| Skill Handbook | Regex-based mapping of user intent to specific skills | Skill Name, Regex Pattern, Weight |
| Agent Profile | Metadata tracking competence and cost per model | Name, Competence Map, Cost per Skill, Base Cost |
| Routing History | A circular queue used to prevent routing collapse | Agent Name, Timestamp, Success Ratio |
| Routing Cache | Semantic store for rapid re-routing of similar contexts | Context Hash, Selected Agent, Confidence Score |
SkillOrchestra Advanced Features
- Predictive Routing: Anticipates the next likely task contexts based on current interactions to pre-warm specific agents.
- Routing Collapse Prevention: Built-in logic that identifies and breaks loops where the system becomes overly reliant on a single agent.
- Performance-Cost Modeling: Allows developers to set granular thresholds for token spend versus required output quality.
- Semantic Cache Matching: Utilizes an 85% similarity threshold to provide sub-15ms routing responses for recurring task types in Openclaw Skills.
SKILL.md
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