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.
64
Quality
47%
Does it follow best practices?
Impact
96%
3.00xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-agent-orchestration-multi-agent-optimize/SKILL.mdQuality
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, it relies on somewhat abstract technical terminology rather than concrete actions, and the trigger terms could be more natural and comprehensive. The multi-agent focus provides some distinctiveness but could be sharper.
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, cost-aware orchestration')
Add more natural trigger terms users would say, such as 'multi-agent', 'agent swarm', 'parallel agents', 'agent bottlenecks', 'agent scaling'
| 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 uses more technical jargon ('cost-aware orchestration', 'coordinated profiling') that users may not naturally say. Missing common variations like 'multi-agent', 'agent coordination', 'load balancing'. | 2 / 3 |
Distinctiveness Conflict Risk | The multi-agent focus provides some distinctiveness, but terms like 'performance' and 'reliability' are generic enough to potentially overlap with general optimization or monitoring skills. Could conflict with single-agent performance tuning 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 waste tokens. While it attempts to provide code examples and workflows, the code is non-executable pseudocode and the workflows lack concrete validation steps. The content would benefit from aggressive trimming, executable code, and proper file organization.
Suggestions
Remove the 'Role' and 'Context' sections entirely - they add no actionable value and waste tokens on concepts Claude already understands
Replace pseudocode with executable, copy-paste ready examples or clearly mark them as 'conceptual patterns' if flexibility is intended
Add explicit validation checkpoints to workflows (e.g., 'Run baseline metrics BEFORE changes', 'Compare metrics AFTER each change', 'Rollback if regression detected')
Split detailed content into separate files: move code examples to EXAMPLES.md, agent descriptions to AGENTS.md, and keep SKILL.md as a concise overview with clear references
| 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 - cannot be copy-pasted and run. | 2 / 3 |
Workflow Clarity | The initial 4-step instructions provide a reasonable sequence, but the reference workflows are vague ('Agent-based optimization', 'Iterative performance refinement'). Missing explicit validation checkpoints and feedback loops for what are clearly risky orchestration changes. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with 8 numbered sections all inline. No references to external files for detailed content. The structure exists but everything is dumped into one file when API references, code examples, and workflow details should be separated. | 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 | |
- Repository
- boisenoise/skills-collections
- Commit
5c5ae21
- Reviewed
Table of Contents
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