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:sickn33/antigravity-awesome-skills --skill agent-orchestration-multi-agent-optimize64
Quality
47%
Does it follow best practices?
Impact
96%
3.00xAverage score across 3 eval scenarios
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/agent-orchestration-multi-agent-optimize/SKILL.mdDiscovery
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 more comprehensive to capture natural user language around agent systems.
Suggestions
Replace abstract terms with concrete actions (e.g., 'profile agent execution times, balance task queues across agents, optimize API costs' instead of 'coordinated profiling, workload distribution')
Expand trigger terms to include natural variations like 'agents', 'agent coordination', 'scaling agents', 'agent bottlenecks', 'agent costs'
| 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', and 'multi-agent systems', but misses common variations users might say such as 'agents', 'scaling agents', 'agent coordination', 'load balancing', or 'agent costs'. | 2 / 3 |
Distinctiveness Conflict Risk | The multi-agent focus provides some distinctiveness, but terms like 'performance', 'throughput', and 'reliability' are generic enough to potentially overlap with general performance optimization or monitoring 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 don't add value for Claude. While it covers many relevant topics for multi-agent optimization, the code examples are pseudocode rather than executable, and the document structure is a monolithic wall covering too many topics without proper organization or external references.
Suggestions
Remove the 'Role' and 'Context' sections entirely - they add no actionable guidance and waste tokens on concepts Claude already understands
Replace pseudocode examples with executable code or remove undefined functions (DatabasePerformanceAgent, semantic_truncate, etc.) and provide real implementations or library references
Split content into separate files (PROFILING.md, COST_OPTIMIZATION.md, ORCHESTRATION.md) with SKILL.md serving as a concise overview with clear navigation links
Add explicit validation checkpoints and error recovery steps to the reference workflows, especially for orchestration changes that could cause system-wide regressions
| 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 with undefined functions (semantic_truncate, aggregate_performance_metrics, DatabasePerformanceAgent). The examples illustrate patterns but aren't executable or copy-paste ready. Reference workflows are vague bullet points rather than concrete steps. | 2 / 3 |
Workflow Clarity | The initial Instructions section provides a reasonable 4-step workflow with validation mention, but the rest of the document lacks clear sequencing. Reference workflows are high-level bullet points without validation checkpoints or error recovery steps for what are described as complex multi-agent operations. | 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 clear navigation or separation. | 1 / 3 |
Total | 6 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Reviewed
Table of Contents
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