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.
60
51%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agent/skills/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, the capabilities listed are somewhat abstract (profiling, orchestration) rather than concrete actions, and the trigger terms could be expanded to capture more natural user language variations around agent systems.
Suggestions
Add more concrete actions like 'analyze agent bottlenecks', 'configure parallel execution', 'monitor agent resource usage' to improve specificity
Expand trigger terms to include variations users might naturally say: 'agents', 'parallel agents', 'agent scaling', 'load balancing', 'agent coordination'
| 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', 'parallel processing', 'load balancing', 'scaling agents', 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
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill functions primarily as a table of contents rather than actionable guidance. While it correctly uses progressive disclosure to reference detailed sub-skills, the main file lacks any concrete examples, executable code, or specific commands. The 16 sub-skill references without clear categorization make navigation difficult, and the abstract 4-step workflow provides no practical implementation details.
Suggestions
Add at least one concrete, executable code example in the main skill (e.g., a basic profiling snippet or cost tracking command) rather than deferring all actionable content to sub-skills
Group the 16 sub-skills into 3-4 logical categories (e.g., 'Profiling', 'Optimization', 'Cost Management', 'Workflows') with brief descriptions of when to use each category
Expand the 4-step instructions with specific commands or validation checks (e.g., 'Run `python profile_agents.py --baseline` to establish metrics')
Remove the 'Role:' line and emoji decorations that add no instructional value
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient with clear sections, but includes some unnecessary framing like 'Role: AI-Powered Multi-Agent Performance Engineering Specialist' and emoji decorations that add no value. The numbered knowledge modules list is verbose when a simpler reference structure would suffice. | 2 / 3 |
Actionability | The instructions are vague and abstract ('Establish baseline metrics', 'Profile agent workloads') without any concrete code, commands, or specific examples. There's no executable guidance in the main skill file - everything is deferred to sub-skills. | 1 / 3 |
Workflow Clarity | The 4-step instruction sequence is present and includes validation mention, but lacks explicit checkpoints, specific validation commands, or feedback loops for error recovery. The steps describe what to do conceptually but not how to verify success at each stage. | 2 / 3 |
Progressive Disclosure | References to 16 sub-skills are provided, but the organization is overwhelming - a flat list of 16 numbered items without clear categorization or guidance on which to read first. The main skill provides almost no actionable content itself, pushing everything to sub-files. | 2 / 3 |
Total | 7 / 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
- Dokhacgiakhoa/antigravity-ide
- Commit
332e58b
- Reviewed
Table of Contents
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