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.
56
33%
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/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 a solid structure with both 'what' and 'when' clauses, which is its main strength. However, the capabilities listed lean toward abstract category names rather than concrete actions, and the trigger terms, while relevant, are somewhat generic and could overlap with other performance-oriented skills. The multi-agent niche helps with distinctiveness but isn't reinforced with enough specific, natural trigger terms.
Suggestions
Replace abstract terms like 'coordinated profiling' and 'cost-aware orchestration' with more concrete actions, e.g., 'profile agent execution times, distribute workloads across agents, optimize API call costs, detect bottlenecks in agent pipelines'.
Expand trigger terms in the 'Use when' clause with natural variations users would say, such as 'agent latency', 'scaling agents', 'load balancing across agents', 'reducing agent costs', or 'multi-agent architecture'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (multi-agent systems) and some actions (profiling, workload distribution, cost-aware orchestration), but these are somewhat abstract and not fully concrete—'coordinated profiling' and 'cost-aware orchestration' are more like category labels than specific actionable tasks. | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize multi-agent systems with profiling, workload distribution, cost-aware orchestration) and 'when' (Use when improving agent performance, throughput, or reliability) with an explicit 'Use when...' clause. | 3 / 3 |
Trigger Term Quality | Includes some relevant terms like 'multi-agent systems', 'agent performance', 'throughput', and 'reliability', but misses many natural variations users might say such as 'agent latency', 'scaling agents', 'load balancing', 'agent costs', 'multi-agent architecture', or 'agent coordination'. | 2 / 3 |
Distinctiveness Conflict Risk | The multi-agent focus provides some distinctiveness, but terms like 'performance', 'throughput', and 'reliability' are very broad and could overlap with general performance optimization, infrastructure, or monitoring skills. 'Agent' could also conflict with other agent-related skills. | 2 / 3 |
Total | 9 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a verbose, abstract document that reads more like a marketing overview or table of contents than actionable guidance. It explains concepts Claude already understands, provides non-executable pseudocode with undefined functions, and lacks concrete workflows with validation steps. The content would need to be fundamentally restructured around specific, executable procedures with real tools and clear validation checkpoints.
Suggestions
Replace all pseudocode with executable examples using real libraries/tools, or remove code blocks entirely and provide specific CLI commands or concrete step-by-step procedures instead.
Cut sections 1-8 down to a concise overview with links to separate detailed files (e.g., PROFILING.md, COST_OPTIMIZATION.md) — the current monolithic structure provides breadth without depth.
Add explicit validation checkpoints and rollback procedures to workflows, e.g., 'Run benchmark suite before/after changes; if latency increases >5%, revert with `git checkout`'.
Remove the 'Role' context section, abstract bullet-point lists (e.g., 'Intelligent multi-agent coordination'), and marketing language — these waste tokens without adding actionable information.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive conceptual explanations Claude already knows (what profiling is, what coordination principles are, what cost optimization means). Bullet-point lists of abstract concepts like 'Intelligent context compression' and 'Semantic relevance filtering' add no actionable value. The 'Role' section with marketing language ('cutting-edge AI orchestration techniques', 'holistically improve system performance') wastes tokens. | 1 / 3 |
Actionability | Code examples are pseudocode with undefined functions (semantic_truncate, aggregate_performance_metrics, DatabasePerformanceAgent) and incomplete implementations (select_optimal_model just has 'pass'). The reference workflows are vague 4-step abstractions like 'Initial performance profiling' → 'Agent-based optimization' with no concrete commands, tools, or executable steps. Nothing is copy-paste ready. | 1 / 3 |
Workflow Clarity | The Instructions section lists 4 high-level steps without any specifics on how to execute them. Reference workflows are abstract sequences with no validation checkpoints, no error recovery, and no concrete commands. For a skill involving orchestration changes that could cause system-wide regressions, the complete absence of validation/rollback procedures is a critical gap. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with 8 numbered sections all inline, many of which contain only bullet-point lists of abstract concepts. No references to external files for detailed content. The document tries to cover profiling, context optimization, coordination, parallelism, cost, latency, quality tradeoffs, and monitoring all in one file without meaningful depth in any area. | 1 / 3 |
Total | 4 / 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
- sickn33/antigravity-awesome-skills
- Commit
d739c8b
- Reviewed
Table of Contents
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