agent-orchestration-multi-agent-optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
Install with Tessl CLI
npx tessl i github:duclm1x1/Dive-Ai --skill agent-orchestration-multi-agent-optimize56
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description has good structure with an explicit 'Use when' clause and covers the domain adequately. However, the capabilities listed are somewhat abstract (profiling, orchestration) rather than concrete actions, and the trigger terms could be expanded to include more natural user language variations for multi-agent system work.
Suggestions
Add more concrete actions like 'analyze agent communication patterns', 'identify bottlenecks', 'configure load balancing' to improve specificity
Expand trigger terms to include natural variations: 'agents', 'multi-agent', 'distributed agents', 'agent scaling', 'agent bottlenecks'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (multi-agent systems) and lists some actions (coordinated profiling, workload distribution, cost-aware orchestration), but these are somewhat abstract concepts rather than concrete, actionable tasks like 'extract text' or 'fill forms'. | 2 / 3 |
Completeness | Clearly answers both what (optimize multi-agent systems with profiling, workload distribution, orchestration) and when (improving agent performance, throughput, or reliability) with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Includes relevant terms like 'agent performance', 'throughput', 'reliability', but misses common variations users might say such as 'agents', 'scaling', 'load balancing', 'multi-agent', 'distributed systems', or 'agent coordination'. | 2 / 3 |
Distinctiveness Conflict Risk | The multi-agent focus provides some distinctiveness, but terms like 'performance' and 'optimization' are generic enough to potentially overlap with general performance tuning or system optimization skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill suffers from significant verbosity with marketing-style language and conceptual explanations that add no value for Claude. While it attempts to cover multi-agent optimization comprehensively, the code examples are pseudocode rather than executable, and the massive inline content should be split across reference files. The initial instructions are reasonable but the rest of the document dilutes rather than enhances actionability.
Suggestions
Remove the 'Role' and 'Context' sections entirely - they contain no actionable information and waste tokens on marketing language
Replace pseudocode examples with executable code or remove them - functions like `semantic_truncate()` and `aggregate_performance_metrics()` don't exist and can't be run
Split detailed sections (profiling agents, cost optimization, latency techniques) into separate reference files and link from a concise overview
Add explicit validation checkpoints to workflows, e.g., 'Verify baseline metrics are captured before proceeding' and 'Run regression tests before deploying orchestration changes'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with unnecessary conceptual explanations ('AI-Powered Multi-Agent Performance Engineering Specialist', 'cutting-edge AI orchestration techniques'). Contains marketing-style language and explains concepts Claude already knows. The 'Role' and 'Context' sections add no actionable value. | 1 / 3 |
Actionability | Contains code examples but they are pseudocode/incomplete (undefined functions like `semantic_truncate`, `aggregate_performance_metrics`, classes without implementations). The code is illustrative rather than executable - you cannot copy-paste and run any of these examples. | 2 / 3 |
Workflow Clarity | The initial 'Instructions' section provides a clear 4-step sequence, but lacks explicit validation checkpoints. The reference workflows are vague ('Agent-based optimization', 'Iterative performance refinement') without concrete steps. Missing feedback loops for error recovery in orchestration changes. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with 8+ major sections all inline. No references to external files for detailed content. The document tries to cover profiling, context optimization, coordination, cost management, latency, quality tradeoffs, and monitoring all in one file without appropriate splitting. | 1 / 3 |
Total | 6 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
Table of Contents
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